Frameworks

Artificial Intelligence has become one of the most influential technologies in modern software development. From chatbots and recommendation systems to sentiment analysis and intelligent search, machine learning models are now expected features in many mobile applications. For several years, integrating AI into iOS applications almost always meant sending user data to cloud services. APIs such as OpenAI, Anthropic Claude, and Google Gemini allowed developers to leverage state-of-the-art language models without worrying about infrastructure or hardware limitations. While this approach is simple, it also introduces several challenges, including network latency, API costs, internet dependency, and privacy concerns. Fortunately, the landscape has changed dramatically. Today's Apple devices contain incredibly powerful hardware, including the Apple Neural Engine (ANE), powerful GPUs, and highly optimized CPUs capable of running sophisticated machine learning models directly on the device. This shift has made on-device AI more practical than ever. Instead of relying entirely on cloud services, developers can now deploy transformer models directly within their applications, enabling offline functionality, lower latency, improved privacy, and reduced operational costs. In this article, we'll explore how to integrate an ONNX-based transformer model into an iOS application using Swift. We'll load a model, execute inference with ONNX Runtime, and prepare the necessary transformer inputs for models such as DistilBERT. A Brief History of LLM Integration in Swift Projects Large Language Models were originally designed to run on powerful cloud infrastructure because of their immense computational requirements. Training these models required thousands of GPUs, and even inference demanded hardware far beyond what smartphones could provide at the time. Because of these limitations, early Swift applications integrated AI almost exclusively through cloud APIs. User prompts were transmitted to remote servers where the model generated a response before sending the results back to the application. Although this architecture worked well, it also introduced unavoidable drawbacks: Internet connectivity became mandatory.Responses depended on network latency.User data had to leave the device.API usage generated recurring operational costs. Meanwhile, Apple continued investing heavily in machine learning acceleration. The introduction of the Apple Neural Engine in 2017 marked a turning point. Every generation of iPhone, iPad, and Mac became increasingly capable of executing neural networks efficiently. At the same time, Apple expanded Core ML, Metal Performance Shaders, and hardware acceleration APIs that allowed developers to run increasingly sophisticated models locally. The open-source AI community accelerated this transition even further. Frameworks such as llama.cpp, MLX, MLC LLM, and ONNX Runtime made it possible to execute optimized transformer models directly on Apple devices. Developers could now deploy popular open-source models including Llama, Mistral, Phi, Gemma, and Qwen without requiring any cloud infrastructure. Apple later introduced Foundation Models as part of Apple Intelligence, further demonstrating the industry's movement toward local AI processing. Today, Swift developers have more choices than ever before. Depending on the application, developers can choose between cloud-hosted models, hybrid cloud/local inference, or fully offline on-device inference. For many applications, including text classification, semantic search, recommendation engines, and lightweight AI assistants, on-device inference has become the preferred solution. Why ONNX? Before diving into the implementation, it's worth understanding why ONNX has become one of the most popular deployment formats for machine learning models. ONNX (Open Neural Network Exchange) is an open standard for representing machine learning models. Instead of locking your project into a specific framework such as TensorFlow or PyTorch, ONNX provides a portable format that can be executed across many different platforms. This portability offers several advantages. A model trained in Python using PyTorch can be exported as an .onnx file and later executed inside an iOS application without rewriting the model itself. Likewise, the exact same model can often be shared between iOS, Android, Windows, Linux, and macOS. This dramatically simplifies deployment across multiple platforms. Microsoft maintains ONNX Runtime, a highly optimized inference engine capable of executing ONNX models efficiently across different hardware accelerators. For Swift developers, this means we only need to load the ONNX model, provide the expected inputs, and retrieve the outputs generated by the runtime. Loading an ONNX Model Let's assume we've already trained our transformer model and exported it to ONNX. Our project now contains a file named MoodClassifier.onnx. The model can either be added directly to the application bundle or packaged as a Swift Package resource. The first step is locating the model inside the application. Swift let modelPath = Bundle.main.path(forResource: "MoodClassifier", ofType: "onnx") If modelPath is not nil, the application has successfully located the model. If it returns nil, verify the following: The model has been added to the target.The filename matches exactly.The resource exists inside the application bundle.The file extension is correct. Successfully locating the model is the first indication that everything has been configured correctly. Installing ONNX Runtime Executing an ONNX model requires an inference engine. Fortunately, Microsoft provides an official Swift Package for ONNX Runtime that can be added using Swift Package Manager. Swift .package(url: "https://github.com/microsoft/onnxruntime-swift-package-manager",from: "1.24.2") After adding the dependency, we're ready to create an inference session. Running Inference Running inference simply means executing a trained machine learning model using new input data. Unlike training, inference does not modify the model. It only computes predictions. Creating an ONNX Runtime session requires three primary components: ORTEnvORTSessionOptionsORTSession The environment configures runtime behavior and logging. The session options allow developers to customize execution behavior. Finally, the session loads the model into memory and prepares it for inference. Swift let env = try ORTEnv(loggingLevel: .warning) let options = try ORTSessionOptions() let modelPath = Bundle.main.path(forResource: "MoodClassifier", ofType: "onnx")! let session = try ORTSession(env: env, modelPath: modelPath, sessionOptions: options) let outputs = try session.run(withInputs: [:], outputNames: ["logits"], runOptions: nil) In this example, the model returns a tensor called logits. Depending on how the model was exported, your output tensor may have a different name. Always inspect the exported model to determine the available output names. Preparing Inputs for Transformer Models Most transformer models, including DistilBERT, BERT, and RoBERTa, cannot process raw text directly. Instead, they expect numerical tensors representing the input sentence. This process is called tokenization. Tokenization converts natural language into token IDs that correspond to entries within the model's vocabulary. Alongside the token IDs, transformer models also require an attention mask. The attention mask tells the model which tokens belong to the original sentence and which tokens are merely padding added to maintain a fixed sequence length. Using the correct tokenizer is extremely important. The tokenizer used during inference must be identical to the tokenizer used while training the model. Even small differences in vocabulary or preprocessing rules can generate completely different token IDs, resulting in poor predictions despite using the correct model. Using Swift Transformers Hugging Face provides an excellent package called Swift Transformers that simplifies tokenization directly within Swift. The package can be added using Swift Package Manager. Swift .package(url: "https://github.com/huggingface/swift-transformers", from: "1.3.3" ) Once installed, you can load the tokenizer that matches the model used during training and generate the input_ids and attention_mask required by the transformer. After generating these arrays, they must be converted into ONNX tensors before inference. Creating ONNX Input Tensors The generated token arrays must be wrapped inside ORTValue tensors. The following example converts both arrays into tensors before executing the model. Swift let env = try ORTEnv(loggingLevel: .warning) let options = try ORTSessionOptions() let modelPath = Bundle.main.path(forResource: "MoodClassifier", ofType: "onnx")! let session = try ORTSession(env: env, modelPath: modelPath, sessionOptions: options) let inputData = NSMutableData(bytes: &inputIDs, length: inputIDs.count * MemoryLayout<Int64>.size) let inputTensor = try ORTValue(tensorData: inputData, elementType: .int64, shape: [1, inputIDs.count] as [NSNumber]) let attentionData = NSMutableData(bytes: &attentionMask, length: attentionMask.count * MemoryLayout<Int64>.size) let attentionTensor = try ORTValue(tensorData: attentionData, elementType: .int64, shape: [1, attentionMask.count] as [NSNumber]) let outputs = try session.run(withInputs: ["input_ids": inputTensor, "attention_mask": attentionTensor], outputNames: ["logits"], runOptions: nil) In this example, two tensors are created: input_ids, which contains the numerical representation of the input text, and attention_mask, which tells the model which tokens should participate in the attention mechanism. These tensors are then passed into the ONNX Runtime session, which executes the model and returns the requested outputs. Conclusion On-device AI is no longer a niche capability reserved for flagship applications. Thanks to frameworks such as ONNX Runtime and Swift Transformers, integrating transformer models into iOS projects has become both accessible and practical. In this article, we explored how to load an ONNX model, execute inference using Microsoft's ONNX Runtime, and prepare the input_ids and attention_mask tensors required by transformer models. These components form the foundation for deploying a wide range of AI-powered features directly within Swift applications. As Apple's hardware continues to evolve and transformer models become increasingly efficient, local inference will play an even greater role in the future of mobile development. Whether you're building a sentiment analyzer, semantic search engine, recommendation system, or lightweight AI assistant, ONNX Runtime provides a robust and portable solution for bringing modern machine learning to iOS.

Grok 4.7 promises stronger AI coding performance without a higher base price, but heavy token consumption could reduce those savings. SpaceXAI released Grok 4.7 on Sept. 21 as its latest model for coding and professional knowledge work. According to the company, it uses a larger base model than Grok 4.6 and underwent a longer reinforcement-learning run focused on difficult tasks that can take hours to complete. The training is designed to improve how Grok verifies its own work, handles long contexts and manages multi-step assignments. SpaceXAI also trained it to natively understand the Grok Bot harness, which the company says improves conversational performance and general knowledge work. Coding gains come with caveats In company-reported testing, Grok 4.7 outperformed Grok 4.6 across several coding benchmarks. On CursorBench 4.0, Grok 4.7 scored 46.3%, up from 40.4%. Its Terminal-Bench 4.0 score nearly doubled to 38%, compared with 20.3% for its predecessor. It also reached 71% on DeepSWE v1.1 at high effort and 64% on EEBench, compared with 65.2% and 53%, respectively, for Grok 4.6. But the gains do not amount to a clean sweep. SpaceXAI's own comparison puts Fable 5.1 Max ahead of Grok 4.7 on CursorBench and Terminal-Bench. Grok also trails competing models on some professional knowledge-work and clinical benchmarks in SpaceXAI’s comparison. Independent testing raises another concern. Artificial Analysis reported that Grok 4.7 at its xHigh reasoning setting used about 81,000 output tokens per Intelligence Index task, substantially more than Grok 4.6 High and GPT-6 Astra Max in its testing. The price is the real hook Grok 4.7 starts at $2 per million input tokens and $6 per million output tokens, unchanged from Grok 4.6. The faster version costs $4 per million input tokens and $12 per million output tokens. That pricing makes the model attractive for developers running large workloads, but the token consumption complicates the calculation. A cheaper token does not automatically produce a cheaper completed task if the model needs substantially more tokens to finish it. For companies, the useful measurement will therefore be cost per successful job, not simply the API price. New safeguards for cybersecurity SpaceXAI says Grok 4.7 introduces a new safeguard stack designed to improve jailbreak resistance while limiting unnecessary refusals. The company reports a 62.4% score on LatchBio’s biosafety benchmark and says its safeguards allowed 3.3% of the risky dual-use prompts tested with HackerBench v0.3. Selected cybersecurity partners are also receiving invite-only access to Grok 4.7 for red-team testing. Those figures come from SpaceXAI's own testing, so broader independent evaluations will be important. What Grok 4.7 means for users For developers, Grok 4.7 is most relevant when work involves repositories, terminals, repeated tool calls and long coding sessions rather than simple prompts. Professionals could also benefit from its focus on documents, presentations and extended knowledge-work tasks. The model is available through Cursor, Grok Build, the Grok API, third-party coding tools, model routers and cloud platforms. The main limitation is predictability. Benchmark results vary by methodology, effort setting and toolchain, while high token consumption could raise real-world costs. Where the release could matter most Grok 4.7’s unchanged pricing could make it attractive for agentic AI workflows in which models operate for extended periods rather than answer individual prompts. However, organizations should test the model using their own repositories, tools and approval processes before assuming its lower API rates will reduce overall spending. The more useful measurement will be cost per successful task, including token consumption, retries, latency, and human review. Independent evaluations and production results will ultimately determine whether Grok 4.7’s benchmark gains translate into dependable savings. Editor’s note: This article originally appeared on our sister publication, TechRepublic.

Most AWS migration projects don't fail because of technical complexity. They fail because teams treat migration as a single activity rather than a set of distinct strategies applied to different workloads. AWS defines seven migration strategies — the 7Rs — that determine how each application moves to the cloud. The decision of which strategy applies to which workload has more impact on project cost, timeline, and outcome than any architectural choice you'll make after. Yet in practice, most teams default to "lift-and-shift everything" without evaluating whether that's appropriate. This article presents a practitioner's framework for classifying workloads into the 7Rs, based on delivering 50+ AWS migrations across fintech, SaaS, healthcare, and e-commerce. The 7R Strategies Retire Not every workload deserves migration. During discovery, you will invariably find applications that are redundant, unmaintained, or replaceable. In a typical enterprise portfolio of 20–40 applications, 10–20% qualify for retirement. Decision criteria: No active users, duplicate functionality already covered by another system, or maintenance cost exceeds business value. Common mistake: Teams skip this step because retiring applications requires stakeholder conversations. The result is migrating dead applications that consume compute budget indefinitely. Retain Some workloads shouldn't migrate in this wave. Applications with deep hardware dependencies, pending end-of-life within 12 months, or complex regulatory constraints that require legal review before cloud deployment are candidates for retention. Decision criteria: High migration complexity combined with low business urgency, or external constraints that prevent cloud deployment within the project timeline. Retain is not "never migrate." It's "not now." Document these workloads with a future migration path and trigger conditions. Rehost (Lift-and-Shift) Moving applications to EC2 or containers without code modifications. AWS Application Migration Service (MGN) automates this by continuously replicating servers and orchestrating cutover with minutes of downtime. Decision criteria: Application has a short remaining lifespan (1-2 years), speed of migration matters more than optimization, or the application is a black box with no available source code. Timeline: Days to weeks per workload. Trade-off: You gain cloud elasticity and pay-as-you-go pricing immediately, but you inherit all existing architectural inefficiencies. A poorly designed monolith on-premises becomes a poorly designed monolith on EC2. Relocate Hypervisor-level migration, primarily for VMware workloads moving to VMware Cloud on AWS. The OS, application, and configuration remain untouched. Decision criteria: Large VMware estate, tight data center exit deadline, and applications that cannot tolerate any configuration change. Replatform Migration with targeted adaptations to managed services. The application architecture stays intact, but you replace self-managed infrastructure components with AWS equivalents: Self-ManagedAWS ManagedOperational BenefitSelf-hosted PostgreSQLRDS for PostgreSQLAutomated backups, patching, failoverCron jobs on EC2EventBridge + LambdaNo server to maintain, pay-per-invocationSelf-managed RedisElastiCacheAutomatic failover, scalingNginx load balancerApplication Load BalancerManaged TLS termination, WAF integrationSelf-hosted ElasticsearchOpenSearch ServiceManaged cluster scaling, snapshots Decision criteria: Application is well-structured but operationally expensive. The team spends significant time on database maintenance, patching, backup verification, or scaling. Timeline: 2-4 weeks additional per workload compared to rehost. Trade-off: Moderate additional effort (schema compatibility testing, connection string changes) in exchange for a 40-60% reduction in ongoing operational cost. For most mid-complexity applications, replatforming represents the optimal balance between migration effort and long-term benefit. Refactor (Re-Architect) Rebuilding applications for cloud-native patterns: microservices decomposition, containerization (ECS/EKS), serverless (Lambda), event-driven architecture (EventBridge, SQS, SNS, Step Functions). Decision criteria: The application is a core business asset that needs capabilities the current architecture cannot deliver — true horizontal scaling, independent service deployments, multi-region active-active, or zero-downtime deployments. Timeline: Months. Budget accordingly. Trade-off: Highest upfront investment, but delivers the best long-term results in terms of deployment velocity, fault isolation, and scaling capability. Reserve this for 2-3 applications maximum in a migration portfolio. Repurchase Replacing custom-built software with a commercial SaaS product. The application doesn't move to AWS; it moves to a vendor. Decision criteria: The in-house application solves a problem that is not a core competency and commercially available alternatives have matured to cover your requirements. Common candidates: CRM, HR systems, monitoring, project management. The Decision Framework Classification should happen during the assessment phase, before any infrastructure work begins. For each workload, evaluate four dimensions: 1. Business Value How critical is this application to revenue generation or core operations? High: Core product, customer-facing, revenue-generatingMedium: Internal operations, supports core processesLow: Legacy, rarely used, or duplicate functionality 2. Technical Complexity How difficult is it to migrate given current architecture, dependencies, and state management? High: Stateful, tightly coupled, hardware dependencies, proprietary protocolsMedium: Standard web application with database, some external integrationsLow: Stateless, containerizable, standard protocols 3. Team Capacity Does your engineering team have the skills and bandwidth to support a complex migration approach? High capacity: Can support re-architecting alongside other workLimited capacity: Can handle replatforming with some external supportMinimal capacity: Rehost or retain is the only realistic option 4. Time Constraint How quickly must this workload be operational on AWS? Immediate (weeks): Data center exit, contract expiryStandard (1-3 months): Planned migration within a programFlexible (3-6 months): Can wait for deeper optimization Mapping Dimensions to Strategy Business ValueComplexityCapacityTimeRecommended StrategyLowAnyAnyAnyRetire or RepurchaseAnyHighLowImmediateRehost (with future replatform plan)MediumMediumMediumStandardReplatformHighMedium-HighHighFlexibleRefactorAnyAnyAnyBlockedRetain A Practical Example Consider a portfolio of 15 applications for a mid-size SaaS company: Plain Text ┌─────────────────────────────────────────────────────┐ │ RETIRE (3) │ │ - Legacy admin panel (replaced by new one 2024) │ │ - Internal wiki (moved to Confluence) │ │ - Prototype service (never went to production) │ ├─────────────────────────────────────────────────────┤ │ RETAIN (1) │ │ - Hardware security module integration │ │ (requires legal review for cloud deployment) │ ├─────────────────────────────────────────────────────┤ │ REHOST (4) │ │ - Backoffice tools (low traffic, stable) │ │ - Legacy reporting engine (EOL in 18 months) │ │ - Monitoring collector agents │ │ - Staging environment clone │ ├─────────────────────────────────────────────────────┤ │ REPLATFORM (5) │ │ - Main API (PostgreSQL → RDS, cron → Lambda) │ │ - Worker services (EC2 → ECS Fargate) │ │ - File processing pipeline (S3 + Lambda) │ │ - Authentication service (→ElastiCache for sessions│ │ - Notification service (→ SES + SQS) │ ├─────────────────────────────────────────────────────┤ │ REFACTOR (1) │ │ - Core product platform (monolith → microservices) │ ├─────────────────────────────────────────────────────┤ │ REPURCHASE (1) │ │ - Custom CRM (→ HubSpot) │ └─────────────────────────────────────────────────────┘ This distribution — 20% retire, 7% retain, 27% rehost, 33% replatform, 7% refactor, 7% repurchase — is representative of what I see in practice. The replatform bucket is almost always the largest. Migration Tooling Alignment Each strategy maps to specific AWS tooling: StrategyPrimary ToolsRehostAWS Application Migration Service (MGN), Migration HubReplatformDMS (databases), manual adaptation, Terraform/IaCRefactorECS/EKS, Lambda, Step Functions, custom developmentRelocateVMware Cloud on AWS AWS Migration Hub provides a unified tracking dashboard across all strategies. For database migrations specifically, AWS Database Migration Service (DMS) handles both homogeneous and heterogeneous migrations with continuous replication (CDC), enabling near-zero-downtime cutovers. Common Anti-Patterns "Rehost everything, optimize later." Teams that plan to rehost first and replatform in a second phase rarely execute phase two. The urgency disappears once applications are running, and the team moves to other priorities. If replatforming is the right strategy, do it during migration. "Refactor everything for cloud-native." The opposite extreme. Not every application needs microservices. A well-structured monolith running on ECS Fargate can serve thousands of requests per second with simpler operations than a distributed system. "One strategy for all workloads." Every application in the portfolio has different characteristics. The decision framework exists because one size does not fit all. Conclusion The 7R classification exercise takes 3-5 days for a typical portfolio. It requires involvement from engineering leads, product owners, and sometimes finance (for retire/repurchase decisions). The output - a workload-by-workload strategy map - becomes the foundation for accurate timeline estimates, resource planning, and budget allocation. Without it, you're building infrastructure for workloads that might not need to exist. For a comprehensive breakdown of migration costs, the full 6-phase delivery process, and AWS tooling details, see my complete AWS cloud migration guide.

After a decade of building and debugging large-scale data pipelines across financial services, payments processing, and analytics platforms, I can tell you that almost every slow Spark job I've investigated had the same root cause — and it wasn't the one the team thought it was. The default response when a Spark job is slow is to add more executor memory, increase the number of executors, or bump spark.sql.shuffle.partitions. Sometimes that helps. Usually it doesn't. What I've found, consistently, is that the real problems are structural — a join strategy mismatch that silently multiplies your intermediate dataset by ten times, a single slow task on a degraded node that holds an entire stage hostage, or a decrypt chain that re-reads source data six times when it only needed to read it once. This article is organized around five patterns I keep seeing across teams. Each one looks different on the surface but traces back to a misunderstanding of how Spark actually executes your code. For each pattern, I'll describe what it looks like, when it bites you, the failure mode, and how to fix it. Pattern 1: The OR Join That Quietly Multiplies Your Data What It Looks Like A join condition with an OR clause. Usually introduced when a business requirement adds a secondary matching rule — match on primary card number, or if the transaction is a virtual card transaction, match on the underlying physical PAN. The SQL looks reasonable. The engineer tests it on a sample, and it returns the right rows. When It Bites You At scale. With 100 million transaction rows and 50 million account rows, this query starts running for hours. The output size is also wrong — much larger than expected before DISTINCT trims it down. The Failure Mode Spark cannot use a hash join or sort-merge join when the join condition contains OR. It falls back to BroadcastNestedLoopJoin — for every row in the left table, scan every row in the right table. That's O(n x m). On real datasets, this produces an intermediate result in the hundreds of GB before any downstream filter runs. I've watched a pipeline that should produce 8 GB of output generate 400 GB of intermediate data because of exactly this pattern, taking a 20-minute job to 4 hours. You can verify this in 30 seconds: run df.explain(formatted) and look for BroadcastNestedLoopJoin in the physical plan. If you see it on a join involving any table over a few million rows, it's almost certainly unintentional. The Fix Split the join into two equi-join legs and UNION ALL the results: SQL -- Leg 1: primary match (equi-join — uses SortMergeJoin or BroadcastHashJoin) SELECT txn.*, acct.* FROM transactions txn JOIN accounts acct ON txn.card_number = acct.card_number UNION ALL -- Leg 2: fallback match, filtered scope only SELECT txn.*, acct.* FROM transactions txn JOIN accounts acct ON txn.fpan = acct.physical_pan WHERE txn.transaction_type = 'VIRTUAL' Each leg is a proper equi-join. Apply DISTINCT at the end to deduplicate rows that matched both. The performance difference is routinely an order of magnitude. Pattern 2: The Straggler Task That Nobody Notices Until It's Too Late What It Looks Like A stage that should take 10 minutes takes 3 hours. The Spark UI shows nearly all tasks completed quickly. One or two tasks are still running with a disproportionately long duration. When It Bites You Jobs running on shared YARN or cloud infrastructure where any node can have a bad disk, a noisy neighbor, or degraded network throughput. Also common in stages that call external services per partition — one slow API response can cause a single partition's tasks to take 100x longer than the others. The Failure Mode A stage doesn't complete until the last task completes. Not the median. Not p95. The absolute last one. If 2,200 tasks finish in under 2 minutes and one takes 3 hours and 7 minutes, the stage takes 3 hours and 7 minutes. The other 2,199 executors sit idle. This is the straggler problem, and it's distinct from data skew. The diagnostic: in the Stage detail view, check the task duration distribution. If MAX is dramatically higher than p99, that's a straggler (hardware or external service issue). If p75 is already much higher than p50, that's skew (data distribution issue). They require different fixes, and many teams treat them identically. The Fix For stragglers caused by degraded infrastructure, enable Spark speculation: Properties files spark.speculation=true spark.speculation.multiplier=3 # task must be 3x slower than median spark.speculation.quantile=0.9 # wait for 90% completion before speculating Speculation re-launches slow tasks on a different executor and uses whichever copy finishes first. The caveat: don't use this on stages that write to non-idempotent sinks. For read-heavy or compute-heavy stages — including external decryption calls — it's often the single most impactful config change you can make. Pattern 3: The df.rdd Decrypt Chain That Recomputes Everything Six Times What It Looks Like A pipeline that calls an external encryption or decryption service per record, implemented as a series of df.rdd.mapPartitions() calls, one per column that needs to be processed. When It Bites You When you have multiple columns to decrypt. Each .rdd call creates a new computation starting from the original DataFrame — Spark re-reads from source, re-executes all upstream joins and filters, and then runs the decryption for that column. With six columns to decrypt, you're doing that six times. The Failure Mode Two distinct sub-problems compound each other. First, going to RDD bypasses Catalyst entirely — no predicate pushdown, no column pruning, no Tungsten execution. Second, without a persist checkpoint before the chain, every decrypt call lineages all the way back to the source. I've seen this double the runtime of a job compared to the same pipeline with a single persist() before the decrypt chain. On top of that, the external call latency per partition is dominated by the number of HTTP round trips, not the payload size. Cutting your batch size in half doubles your request count and roughly doubles your wall-clock time for that stage. Most teams set an initial batch size and never revisit it. The Fix Two changes, applied together: Persist the input DataFrame before starting the decrypt chain. This means the join and filter logic runs once, and each decrypt call reads from the cached result.Increase the batch size for external calls. Test at several sizes — going from 20,000 to 40,000 records per batch often cuts stage time by 30-50% with no change to correctness. Scala val base = rawDf.filter(...).join(key1, ...).persist(StorageLevel.MEMORY_AND_DISK) val step1 = decryptColumn(base, secret1) // reads from cache val step2 = decryptColumn(step1, secret2) // reads from cache val step3 = decryptColumn(step2, secret3) // reads from cache Without persist, step2 re-executes everything step1 did from source. With persist, each step reads from the in-memory result of the previous. Pattern 4: The shuffle.partitions Setting That Nobody Updates What It Looks Like A job that works fine in staging — where data volumes are 10% of production — but runs slowly, spills to disk, or produces thousands of tiny output files in production. When It Bites You When the default spark.sql.shuffle.partitions=200 is left unchanged. 200 partitions made sense as a default for medium datasets but is almost always wrong at production scale — either too few (huge partitions, memory pressure) or too many (tiny partitions, scheduling overhead, small files problem). The Failure Mode Too few partitions means each executor handles a disproportionately large chunk of data. With 200 partitions on a 1 TB shuffle, each partition is 5 GB. That will spill to disk. Too many partitions means thousands of 1 MB tasks — the scheduling overhead becomes significant, and your output has thousands of tiny files that hurt downstream readers. With Adaptive Query Execution (AQE) enabled in Spark 3.2+, this problem largely manages itself. AQE merges small post-shuffle partitions automatically and can handle modest skew. But AQE can't help if it's disabled, and it can't fix the upstream causes of extreme skew. The Fix Enable AQE if you're on Spark 3.2+: Properties files spark.sql.adaptive.enabled=true spark.sql.adaptive.coalescePartitions.enabled=true spark.sql.adaptive.skewJoin.enabled=true If you need to set shuffle.partitions manually, target roughly 128-256 MB per partition post-shuffle. For a 500 GB shuffle, that means 2,000-4,000 partitions. Set it high and let AQE coalesce down — that's cheaper than setting it low and getting OOM errors. Pattern 5: The Incremental Job That Degrades Silently Over Time What It Looks Like A job that runs in 15 minutes when first deployed and runs in 4 hours six months later. No code changes. No obvious data quality issues. The team attributes it to data growth. When It Bites You When the job fails a few times in a row, and the recovery accumulates multiple windows' worth of data. Or when the watermark logic was designed for small windows but nobody anticipated that the underlying join tables would grow significantly. The Failure Mode Two separate causes, often confused. First, if the watermark is a single timestamp and the job has been failing, recovery runs can accumulate large backlogs. A job that normally processes 2 hours of data may need to process 48 hours on first successful recovery, with no change to the resource configuration. Second, growth in reference data (like an accounts table or lookup table used in a join) increases the size of every run regardless of whether the incremental input grew. I've seen a 30-minute job become a 3-hour job purely because the accounts table grew from 10 million rows to 80 million rows over 18 months, while the OR join condition (see Pattern 1) meant that growth was amplified into the intermediate result. The Fix Two design principles that pay off over the lifetime of the pipeline: Track processed partitions explicitly rather than using a single timestamp watermark. This makes recovery granular — you can replay specific missing partitions without re-processing everything after them.Add a fast-path no-op check before initializing the full Spark session. Check whether any new partitions exist first. A 5-second check that exits early is much better than a 2-minute executor startup that discovers there's nothing to process. For the reference table growth problem: if your lookup table grows significantly, revisit whether it can be broadcast (small enough to fit in executor memory) or whether the join itself needs to be redesigned. Quick Diagnostic Reference Use this table to map what you observe in the Spark UI to the likely pattern and first action to take: WHat you observeLikely patternconfirm withfirst action MAX task duration >> p99 Straggler (Pattern 2) Task timeline in Stage UI Enable spark.speculation p75 >> p50 task duration Data skew Input bytes per task Repartition on join key; AQE skewJoin BroadcastNestedLoopJoin in explain() OR join (Pattern 1) df.explain( formatted) Rewrite as UNION of equi-joins Stage runtime grows week on week; no code change Incremental accumulation or reference table growth (Pattern 5) Input bytes trend in History Server Audit watermark logic; check reference table size OOM errors or heavy disk spill Too few shuffle partitions (Pattern 4) Spill metrics in Stage UI Enable AQE or increase shuffle.partitions The Common Thread Every pattern here traces back to the same underlying issue: Spark is executing something different from what the engineer intended. The OR join was intended as a flexible matching rule; Spark turned it into a nested loop. The decrypt chain was intended as six independent transformations; Spark turned it into six full re-reads of source data. The incremental job was intended to process one window of data; without proper watermark design, it occasionally processes twelve. The Spark UI has everything you need to see this — task distribution, input and output sizes, physical plans, spill metrics. Most teams open it when something breaks and close it once they find the obvious error. Opening it proactively, forming a hypothesis, and then confirming or refuting it in the metrics is the practice that separates engineers who consistently improve pipeline performance from those who add executor memory and hope for the best. The mistake isn't choosing the wrong config. It's not understanding what Spark is actually doing with your code.

Most AI proof-of-concept projects don't break down while they're building. They fall down on scoping, weeks before coding is even written. If the objective is unclear, data is unavailable, or a success is not defined, a two-week experiment becomes a two-month drift, showing no return to a stakeholder. I've seen this on my own projects and on teams that I have worked on. The solution is simple, and it does: formulate the POC as a question with a number behind it, and then determine how to find out the answer. AI POC scoping is the practice of establishing a single metric and establishing the data and boundaries of that metric before development begins, and then creating a clear pass-or-fail criteria. When done well, it will tell you within a few weeks whether an idea is worth the real investment or not. The Reasons Why AI Proof of Concept Scoping Fails Three motifs recur and recur. The goal is a feature, not a question. A feature is called "Build a chatbot. A question a POC can answer is "Can a model solve 40% of tier-one tickets with no escalations?There was no initial data checking. Teams take for granted that data is available, has been labeled and is accessible. It often is not.No stopping rule is given. If there are no kill criteria, then a POC just keeps going until the funds run out or people lose faith in it. It takes an afternoon to fix these on paper. It takes weeks to repair them during the project. A Five-Part Framework for Scoping an AI POC 1. Determine What a Single Measurable Outcome Is Choose one of the metrics that is significant to a business owner, such as cost per ticket, hours saved per week, rate of errors, conversion lift, etc. Write it as a target number with a number. When it's impossible to define success in terms of a number, you're not ready to build yet. 2. First, Verify the Data, and Only Then Do the Rest! Ask three things. Is the data available?Is it possible to get there, within the law, technically?Can it be used as a source of learning? Take a sample and read it for yourself. Usable data saves weeks of modeling against unusable data. 3. Place a Hard Box Around Time and Cost A POC is a bet: cap the bet. Most ideas need to be given a period of 2 to 4 weeks to develop on a fixed budget. The limit is both a distraction and a way to ensure that the experiment does not devolve into "production" no one has approved. 4. Select Build, Buy, or Blend Not all issues require an individual model. Often it's one API call, and it's done in an afternoon. Make the decision early on testing a model, a workflow, or a vendor. At this stage, some teams not familiar with AI internally may even hire AI consulting services to test the approach and avoid wasting time on engineering. 5. In Advance, Agree With the Other Person on Kill Criteria Record the number that would make you stop. If after 2 weeks its accuracy is less than 70%, for example, we shelve it. Making the decision prior to becoming emotionally invested in the idea helps to maintain the integrity of the experiment. How 2026 AI Trends Change POC Scoping The stable scoping questions. The Options are no longer where they were. Agentic AI Takes ‘Done' to the Next Level Agentic systems autonomously perform multi-step actions. Hence, success is defined as what the agent can do and when it needs human consent. Look at not just the accuracy, but scope the guardrails as well. A POC without permissions and rollback is half the problem. Automation Pushes POCs Closer to Production The share of the pipeline that is automated has increased, meaning the gap between a working POC and a shippable feature is smaller than it was 2 years ago. Good, but it still confuses the issue, so make it known that a POC is still an experiment and not a soft launch. The True Test of Enterprise Adoption is Integration The hard part is not usually the model as AI transitions from pilot teams to core operations. It's about identity, data governance, and how it integrates with tools you already have. Integrate at least one realistic integration point in the POC to show production, NOT a sandbox. Before You Commit: Decision Factors Do a quick check before greenlighting a POC. Ownership: Who will do what as a result of a positive or negative outcome?Risk: If the model is not correct when it goes live, what happens?Reuse: Can the test be reused with the data pipeline and code?Skills: Do you have the people or a specific AI and machine learning need that requires custom consulting? If there are no answers for these, the POC is too early. Frequently Asked Questions 1) What's the Perfect Length of an AI Proof of Concept? The most common POCs last 2 to 4 weeks. Anything longer than that typically indicates that the scope of work was too big or a success measure was never established. 2) What's the Difference Between an AI POC and an MVP? A POC is the smallest test possible to get a yes/no answer to the question "will this work? The MVP is a genuine product for the users. How to overspend is to operate a POC as an MVP. When is it Time to Seek Assistance? If the problem is valuable, but your team doesn't have the expertise or experience in modeling and data or MLOps to scope the problem confidently. A good outside advisor, whether on the inside or an AI consulting firm, is worth his or her weight in gold because they will put the boot into you for your weak ideas and give your strong ones some grit. 4) What are the Ingredients to a Successful AI POC? One measurable result, data that can be verified, hard time box, agreed kill criteria. Teams that scope for those 4 things ship a lot more than teams that begin with a feature request. Closing Thought The best AI teams are not the ones that create the most POCs. They're the ones who scope them out fast enough to say no while they say yes with confidence. More than just a series of demos, the tight framework helps you identify what works in production.

I have spent most of 2026 writing about agent frameworks on DZone. In MCP vs Skills vs Agents With Scripts, I made the case that these are not competing choices; they are layers you stack. In Loop Engineering and the follow-up on Graph Engineering, I went a level deeper into how the loop itself gets structured once you decide to build something. This month the question changed shape on me again. It stopped being "how do I build an agent" and became "which of the fifteen tools with agent in the pitch do I actually install?" So I widened the comparison. This piece covers the managed AI teammate apps (Grok Bot, Claude Cowork, CellCog), the open-source runtimes you host yourself (OpenWork, OpenClaw), and the developer frameworks you write code against when a packaged app will not cut it (LangGraph, CrewAI, AutoGen, Microsoft Agent Framework, Google ADK, the Claude Agent SDK, Pydantic AI), plus where no-code tools like n8n still fit. Same rule as always: I am not picking a winner. I am telling you which one matches the job in front of you. Three Very Different Categories Before the table, it helps to sort these into buckets, because comparing CrewAI to Grok Bot is like comparing a Python library to a phone. They are not the same kind of thing. AI agent category Managed AI Teammate Apps These are products, not libraries. You sign in, you do not deploy anything, and someone else runs the compute. Grok Bot is xAI's agent product, now shipped under the SpaceXAI banner after the SpaceX-Cursor deal closed in August. Each bot gets its own cloud computer, signs into apps you already use, and works through a task until it needs your approval. The interface looks like a messaging app: a list of named bots instead of one chat thread. Access runs through Cursor's subscription tiers (SuperGrok Plus, SuperGrok Heavy, Cursor Pro+, Cursor Ultra, Cursor Teams) rather than a standalone plan. Claude Cowork is Anthropic's equivalent: an agentic knowledge-work app for non-developers that can reach into files, browse, and use spreadsheets and slides as tools. It is the product OpenWork explicitly built itself to be an open alternative to, which tells you a lot about how the market reads it: capable, but closed and vendor-tied. CellCog takes the teammate idea one step further and calls its agents employees. Each one gets an isolated workspace with its own inbox, task board, shifts, and memory that carries over between sessions. Entry is self-serve from around $8 a month, with usage billed per shift of real work rather than a flat seat price. Use this category when the person running the agent does not want to think about infrastructure, ever. The trade-off is the same across all three: your credentials and files live in someone else's cloud, and you are betting on their roadmap and uptime. Open-Source Runtimes You Host Yourself I covered these two in depth last time, so I will keep it tight here. OpenWork is a free desktop app built on OpenCode. It runs 50+ model providers with your own API keys and keeps files local by default. Good middle ground if you want the Claude Cowork experience without the vendor lock-in. OpenClaw is the MIT-licensed, self-hosted daemon that started as Peter Steinberger's weekend project and now has OpenAI, GitHub, NVIDIA, and Vercel backing its foundation. Agent behavior lives in a plain SOUL.md file, and the whole runtime, model router, memory layer, and messaging connectors are yours to audit and modify. Use this category when you want an app-like experience but refuse to hand your files or your model choice to a vendor. Developer Frameworks: What You Reach for When You Are Writing the Agent Yourself This is the category that grew the most this year, and it is the one most DZone readers will actually touch, since most of us are not shipping a consumer app; we are building an agent into a product or an internal tool. FrameworkOrchestration modelBest atLearning curveModel lock-inLangGraphDirected graph, explicit state, checkpointingComplex, long-running, auditable workflowsSteepestNoneCrewAIRole-based crews, sequential or hierarchical processFast prototyping of multi-agent workflowsEasiestNoneAutoGen / AG2Conversational group chat between agentsBrainstorming, debate, code review with multiple perspectivesMediumNoneMicrosoft Agent FrameworkUnified SDK, merged Semantic Kernel and AutoGenEnterprise .NET and Microsoft-stack shopsMediumNone, but tuned for AzureGoogle ADKHierarchical agent treeTeams already on Gemini and Google CloudMediumOptimized for Gemini, supports othersClaude Agent SDKTool-use chain with hierarchical subagentsAnthropic-native production agentsLow to mediumClaude modelsPydantic AIType-safe, harness-firstPython teams that want strict schemas and validationLowNone A few notes worth calling out beyond the table. LangGraph pulled ahead of CrewAI in GitHub stars this year, largely because its graph model maps cleanly onto production needs like audit trails and rollback points. CrewAI still wins on pure iteration speed. AutoGen is the odd one out: Microsoft put the original project into maintenance mode and folded its ideas into Microsoft Agent Framework, so the community fork AG2 is now the one carrying the conversational-agent torch forward. If your org already lives on Azure and .NET, Microsoft Agent Framework is the safer long-term bet over legacy AutoGen. If you are all-in on Claude models, the Claude Agent SDK gives you hierarchical subagent spawning and fallback model chains without pulling in a general-purpose framework you will only use a third of. I wrote about the shape of this decision- structure the loop yourself versus leaning on a framework's opinions for you- back in Loop Engineering. Nothing about that logic changed this year. What changed is how many good options now exist at each rung of the ladder. No-Code Automation Still Has a Seat at the Table Not every agent needs a framework. n8n and Make let you wire an LLM call into a visual workflow next to your existing integrations: a CRM update, a Slack post, a database write. If the "agent" part of your workflow is really one LLM call sitting inside a larger pipeline that already has clear steps, reaching for LangGraph is over-engineering. Reach for n8n instead and save the framework for the part of the system that actually needs a reasoning loop. The Full Comparison DimensionGrok BotClaude CoworkCellCogOpenWorkOpenClawLangGraph / CrewAI / etc.n8n / MakeWho hosts itVendorVendorVendorYou (local-first)YouYouVendor or self-hostedSetup effortSign inSign inSign inInstall the appClone and configureWrite codeDrag and dropModel choiceGrok onlyClaude onlyConfigurable50+ providersAny providerDepends on frameworkWhichever LLM node you useAudienceNon-technical teamsNon-technical teamsTeams that want "employees"Privacy-conscious teamsDevelopers and platform teamsDevelopersOps and automation teamsCost modelBundled subscriptionBundled subscriptionPer-seat plus per-shift usageFree, pay for API callsFree, pay for hosting and APIFree, pay for API callsFree tier, paid for scaleBest forSpeed, zero infraAnthropic-native knowledge workStanding roles with memoryApp experience without lock-inFull ownership and auditabilityCustom production agentsWiring an LLM into existing ops When to Use What If you are a non-technical team lead who wants an AI teammate today and does not want to hear the word Docker, pick Grok Bot or Claude Cowork based on whichever model ecosystem your org already trusts, and treat CellCog as the option if you specifically want standing roles rather than one-off task delegation. If you want that same app-like experience but your files cannot leave your machine and your model bill needs to stay transparent, OpenWork is the one to install first. Read up on the security tradeoffs in Trust No Agent before you connect it to anything that touches production credentials. If you are a platform or infrastructure team that needs to own every layer, including the messaging connectors and the memory store, OpenClaw is worth the setup time. It is also the option NVIDIA chose to build its NemoClaw enterprise stack on, which tells you it holds up under real compliance scrutiny. If you are writing an agent into a product, pick your developer framework by what your team already knows, not by star count. CrewAI if you need something running by Friday. LangGraph if the workflow has real branching logic and needs to be auditable six months from now. Microsoft Agent Framework if you live in Azure. Claude Agent SDK if you have already standardized on Claude. Pydantic AI if your team cares more about type safety than flexibility. And if the task is mostly plumbing with one smart step in the middle, do not reach for a framework at all. n8n or Make will get you there faster and with less to maintain. Conclusion Here is the thing I keep telling people who ask me to rank all of this. None of these tools are fighting over the same buyer. A managed app trades control for convenience. A self-hosted runtime trades setup time for ownership. A developer framework trades a learning curve for precision. A no-code tool trades flexibility for speed. That is the same argument I made about MCP, Skills, and Agent scripts: the question was never which layer wins; it was which layer matches the job in front of you. Ask yourself three questions before you pick anything: who is allowed to see the data this agent will touch, who is on the hook when it does something wrong at 2 a.m., and how much time does your team actually have to babysit infrastructure versus paying someone else to do it. Answer those honestly, and the right column in the table above picks itself. I will keep testing new entrants as they show up, and given how fast this category moved between August and now, I expect this list to be out of date within a quarter. That is fine. Pick based on your risk tolerance and your ops budget today, not on which name is trending on GitHub this week.

By Vidyasagar (Sarath Chandra) Machupalli FBCS CORE

A chatbot can explain data, summarize a screen, or answer questions, yet the application still behaves largely as before: business state lives elsewhere, actions remain disconnected from model output, and the interface is reduced to a transcript. Agentic UI takes a different approach. The model becomes a planner over explicit application capabilities, while Angular remains responsible for state, rendering, validation, authorization boundaries, and interaction. Angular’s current AI guidance already distinguishes basic chat experiences from agentic workflows and dynamic server-driven interfaces, while protocols such as AG-UI formalize streaming state and tool events between agent backends and frontends. Chat Is an Output Channel, Not the Application Model The key design shift is to model an agent run as a workflow rather than a sequence of messages. A purchasing screen, for example, can expose inventory lookup, draft modification, approval, and submission as capabilities. Natural language may start the flow, but the resulting interface should remain a normal application UI: editable fields, status indicators, review cards, validation messages, and explicit confirmation controls. AG-UI follows this direction by defining lifecycle, text, tool-call, and state events instead of treating every interaction as plain assistant text. Tool calls are represented through structured events, allowing a frontend to represent work in progress without attempting to parse model prose into application behavior. A small TypeScript event contract is enough to establish that separation. Discriminated unions fit especially well because TypeScript narrows union members through control flow, making event handling explicit and allowing every event variant to carry only the fields relevant to that state transition. TypeScript type AgentEvent = | { type: 'run.started'; runId: string } | { type: 'draft.updated'; patch: Partial<OrderDraft> } | { type: 'action.requested'; action: PendingAction } | { type: 'action.finished'; actionId: string; result: ActionResult } | { type: 'run.failed'; message: string }; function applyAgentEvent(event: AgentEvent) { switch (event.type) { case 'run.started': phase.set('running'); break; case 'draft.updated': draft.update(value => ({ ...value, ...event.patch })); break; case 'action.requested': pendingAction.set(event.action); phase.set('approval'); break; case 'action.finished': pendingAction.set(null); phase.set('ready'); break; case 'run.failed': error.set(event.message); phase.set('failed'); } } This reducer keeps model output away from direct DOM mutation. The agent proposes state transitions; Angular applies validated events to application state. Network payloads still require runtime validation because TypeScript annotations disappear during compilation and do not perform runtime checks. Casting arbitrary JSON to AgentEvent therefore establishes a compiler assumption rather than a runtime trust boundary. Let Angular Render State Instead of Model Prose Signals provide a natural projection layer for agent-driven state because Angular tracks signal reads and updates dependent consumers when signal values change. Angular also provides asynchronous resource APIs for integrating async data with signal-based code, although workflow event streams often benefit from an explicit reducer because event ordering, approvals, resumable execution, and intermediate actions are domain state rather than ordinary resource loading TypeScript const phase = signal<'idle' | 'running' | 'approval' | 'ready' | 'failed'>('idle'); const draft = signal<OrderDraft>(emptyDraft); const pendingAction = signal<PendingAction | null>(null); const error = signal<string | null>(null); const busy = computed(() => phase() === 'running'); const approvalRequired = computed(() => pendingAction() !== null); The template can render that workflow through established Angular components instead of constructing another interaction model inside a chat transcript. Signal reads naturally connect the workflow state to Angular rendering. HTML @if (pendingAction(); as action) { <app-action-review [action]="action" (approve)="approve(action.id)" (reject)="reject(action.id)" /> } <app-order-editor [draft]="draft()" [disabled]="busy()" /> This boundary also preserves the application’s existing component system. The model determines intent and proposes changes, while known Angular components determine presentation and interaction semantics. That division becomes increasingly important as model-produced output becomes more dynamic, since a trusted component vocabulary provides substantially more control than arbitrary generated markup. A2UI applies the same general principle by allowing agents to describe interface intent while host applications render native components from an approved catalog. Capabilities Need Stronger Boundaries Than Prompts An agent should not receive an unrestricted instruction to invoke arbitrary frontend behavior. Capabilities should be explicit, typed, narrow, and policy-aware. AG-UI distinguishes backend-defined and client-provided tools, including tools that request human input or confirmation. Angular 22 also introduced experimental WebMCP support for exposing structured application tools to agents running in browser environments, with the explicit goal of reducing dependence on brittle DOM-level interaction. A capability registry keeps execution deterministic while still allowing an agent to choose among operations deliberately exposed by the application. TypeScript type CapabilityName = 'lookupInventory' | 'applyDiscount' | 'submitOrder'; const capabilities = { lookupInventory: { mutates: false, validate: validateInventoryArgs, execute: lookupInventory }, applyDiscount: { mutates: true, validate: validateDiscountArgs, execute: applyDiscount }, submitOrder: { mutates: true, requiresApproval: true, validate: validateSubmitArgs, execute: submitOrder } } satisfies Record<CapabilityName, Capability>; async function dispatch(action: PendingAction) { const capability = capabilities[action.name]; const args = capability.validate(action.args); return capability.execute(args); } The satisfies operator verifies that the registry conforms to the required shape while retaining the more specific inferred type of each value, making capability registries practical without unnecessarily widening their entries. The runtime validate operation solves a different problem: tool arguments originated outside the TypeScript compiler and therefore cannot become trustworthy merely through static type declarations. Human approval should interrupt a run rather than merely decorate a destructive operation with a confirmation sentence. AG-UI formalizes this concept through interrupts: an agent run can pause for approval or structured input and later resume with an explicit response. That model maps naturally to Angular workflow state because an approval card can remain visible until a correlated decision is submitted. TypeScript async function approve(actionId: string) { const action = pendingAction(); if (!action || action.id !== actionId) { return; } await agent.resume({ actionId, decision: 'approved' }); } Server-side authorization still remains authoritative; frontend approval represents an interaction decision rather than permission to bypass backend policy. The same rule applies to generated content. Angular’s security guidance treats untrusted values as a security concern and specifically warns that bypassing sanitization with untrusted content can expose applications to cross-site scripting vulnerabilities. Model output therefore belongs in the same untrusted-input category as any other external payload. Dynamic UI Should Come From a Catalog, Not Arbitrary Markup Some workflows need more than predefined page states. An agent may need to choose whether a result is best represented as a form, comparison view, approval card, or status panel. A2UI addresses that requirement with a declarative format in which an agent describes UI intent and the host renders the result using native components from a trusted catalog. The project supports Angular among its rendering targets and is explicitly designed around declarative UI descriptions rather than transferring arbitrary executable frontend code across the agent boundary. That distinction matters. Generating raw HTML and injecting it into Angular creates unnecessary sanitization pressure, weakens design-system consistency, and expands the amount of generated material that must be treated as untrusted. A constrained component vocabulary limits what an agent can request while retaining enough flexibility for adaptive layouts. Google’s A2UI documentation describes the same model as declarative JSON rendered through components controlled by the host application rather than raw HTML, CSS, or JavaScript supplied by the remote agent. AG-UI and A2UI consequently address different parts of the same frontend problem. AG-UI provides the interaction stream for runs, state changes, tool calls, and human-in-the-loop control, while A2UI provides a declarative mechanism for richer agent-selected views. Neither protocol is mandatory for an Angular implementation; an application-specific event protocol and component registry can implement the same core ideas. Standardization becomes more valuable when several agent runtimes or frontend surfaces must share the same interaction contract. Angular’s experimental WebMCP support introduces another useful direction: capabilities already present in an application can be exposed as structured tools rather than rediscovered through DOM manipulation. Because Angular currently marks the relevant WebMCP APIs as experimental, isolating them behind the same capability layer prevents an emerging transport mechanism from leaking into business logic. Conclusion Agentic Angular interfaces become useful when AI stops being a chat-shaped feature and starts participating in typed application workflows. The durable boundary is not a prompt; it is a contract consisting of validated events, explicit capabilities, observable state transitions, controlled rendering, and deliberate approval points. Angular Signals provide a reactive surface for projecting agent state, TypeScript discriminated unions make workflow events tractable, and emerging protocols such as AG-UI, A2UI, and WebMCP demonstrate a broader shift toward structured agent-to-application interaction. The strongest implementation keeps business authority and UI integrity inside the application while allowing the model to plan, propose, and coordinate. That boundary produces software that remains testable, accessible, secure, and understandable even as agent behavior becomes substantially more capable.

By Bhanu Sekhar Guttikonda CORE

Every Java developer who runs services on Kubernetes has watched this scene play out. Traffic spikes, the autoscaler adds a pod, and then everyone waits. The container is running in two seconds. The application is not ready for another twelve seconds. During those ten seconds, your existing pods absorb the extra load, latency climbs, and if things are bad enough, the autoscaler panics and adds even more pods that are also not ready. I spent years treating Spring Boot startup time as a fact of life, the way you treat weather. Then I found out the JVM has had a fix for a big chunk of it since Java 12; it works beautifully inside Docker, and almost nobody bakes it into their images. It is called Class Data Sharing, CDS for short, and this article shows you how to make your Docker build do the work Where Those Twelve Seconds Actually Go When a Spring Boot application starts, the JVM is not mostly running your code. It is loading classes. A plain REST service with Spring Web, Spring Data, and a driver or two loads somewhere between ten and twenty thousand classes before it serves its first request. For every single one of those classes, the JVM does the same ritual. Find the class file inside a jar, read the bytes, parse them, verify the bytecode is legal, and build the internal metadata structures it needs at runtime. Thousands of times. Every startup. In every pod. Here is the part that should bother you. Your container image never changes after you build it. The same jar, the same classes, the same parsing work, repeated identically in every pod that ever starts from that image. The JVM is solving the same puzzle again and again and throwing away the answer each time. CDS is the JVM saying: let me solve it once, write the answer to a file, and just memory map that file next time. What a CDS Archive Is A CDS archive is a file, usually ending in .jsa, that contains classes already parsed and verified, stored in the exact internal format the JVM uses in memory. On startup, the JVM maps this file straight into memory. No finding, no parsing, no verifying. The work was done ahead of time. You have been using CDS without knowing it. Modern JDKs ship with a default archive covering the core JDK classes, which is why java -version is fast. The step almost everyone skips is creating an archive for your application classes, all fifteen thousand of them. That is where the real win lives. The mechanism has one rule that matters for us. The archive must be created with the same JVM and the same classpath that will use it. That rule sounds annoying until you realize a Docker image is the one place in your entire infrastructure where JVM and classpath are frozen forever. Docker is not just compatible with CDS. It is the perfect home for it. The Training Run Creating the archive takes two steps. First you do a training run, where the JVM starts your application, watches which classes get loaded, and writes the list down. Then you exit, and the JVM turns that list into the archive. Since Java 13, this is pleasantly simple: Shell java -XX:ArchiveClassesAtExit=app.jsa -jar app.jar Run the app, let it come up, stop it, and app.jsa appears. From then on you start the app like this: Shell java -XX:SharedArchiveFile=app.jsa -jar app.jar There is an obvious question here. The training run wants to actually start the application, and inside docker build there is no database, no message broker, nothing to connect to. A Spring Boot app that cannot reach Postgres will crash during training. Spring Boot 3.3 solved this neatly. Setting one property makes the application run through its entire startup sequence, create all bean definitions, and then exit just before touching the outside world: Shell java -Dspring.context.exit=onRefresh -XX:ArchiveClassesAtExit=app.jsa -jar app.jar The application loads nearly everything it will ever load, writes the archive, and exits cleanly with no infrastructure needed. This is exactly what a Docker build stage can do. The Dockerfile Here is the complete picture: a multi-stage build where the image trains itself: Shell FROM eclipse-temurin:21-jdk-alpine AS build WORKDIR /build COPY . . RUN ./mvnw -B package -DskipTests # Explode the jar so the classpath is stable RUN java -Djarmode=tools -jar target/app.jar extract --destination /app FROM eclipse-temurin:21-jre-alpine AS runtime WORKDIR /app COPY --from=build /app /app # Training run: start the context, record classes, exit RUN java -Dspring.context.exit=onRefresh \ -XX:ArchiveClassesAtExit=/app/app.jsa \ -jar /app/app.jar ENV JAVA_TOOL_OPTIONS="-XX:SharedArchiveFile=/app/app.jsa" ENTRYPOINT ["java", "-jar", "/app/app.jar"] Two details in there deserve a closer look. The extract step unpacks the fat jar into a folder with the dependencies laid out as plain files. CDS is picky about the classpath being identical between training and real runs, and a fat jar with nested jars inside it makes that fragile. The exploded layout keeps the classpath boring and stable, which is exactly what CDS wants. On Spring Boot 3.2 and older, the same idea works through the layertools jarmode instead. The training run happens as a RUN instruction, which means it executes once at build time on your CI server. Every container that ever starts from this image inherits the archive for free. You did the class loading homework once, in the build, and ten thousand pod starts copy the answer. What You Get Numbers vary with how heavy your application is, but the pattern is consistent. A typical Spring Boot 3 web service that started in 10 to 12 seconds lands somewhere between 5 and 7. The JVM portion of startup shrinks dramatically, and as a bonus, the archive is memory-mapped and shared, so if you run several JVMs on one node, they share those pages and total memory drops too. You can verify the archive is actually being used, which I recommend, because CDS fails silently by design. If something mismatches, it just quietly falls back to normal class loading: Shell docker run --rm my-service -Xlog:class+load=info | head -5 Classes loaded from the archive say source: shared objects file. If you see jar paths instead, the archive is being ignored, and the log will usually tell you why. The usual culprit is a classpath that differs from training, even by one entry. One honest caveat. The training run exercises startup, not your traffic. Classes that only load when a specific endpoint gets hit for the first time are not in the archive, so those first requests still do normal loading. The archive covers the framework and wiring, which is most of the cost, but it is not a magic warm-up for everything. Why This Beats the Alternatives You Have Heard Of Whenever container startup time comes up, someone mentions GraalVM native images, and native images are impressive. Millisecond startup is real. But they come with a price list: long build times, a closed-world assumption that fights with reflection, some libraries that simply do not work, and a different runtime profile you have to learn to debug. CDS costs you five lines of Dockerfile. Your application is still a completely normal JVM application. Same debugging, same profilers, same libraries, same behavior, just faster out of the gate. For most teams, that trade-off is not even close. It also stacks with what is coming. Project Leyden's AOT cache in Java 24 and beyond is essentially this same idea grown up, caching not just parsed classes but resolved linkage and compiled code. The Dockerfile pattern you build today, a training run at build time producing a cache file shipped in the image, is exactly the shape Leyden uses. Learning it now means the future is a flag change. The Takeaway Your Docker image is immutable. Your JVM does expensive, perfectly repeatable work on every startup. Those two facts fit together like puzzle pieces, and a training run inside docker build is where they connect. One extra build step, and every pod your autoscaler ever creates comes up in half the time. The next time you watch a rollout crawl because pods take forever to go ready, remember that the answer was hiding inside the build all along.

In high-volume data platforms, hardcoding validation logic into individual processing pipelines creates significant operational drag. As an enterprise data asset footprint grows, maintaining manual checks for hundreds of tables inevitably leads to mounting technical debt, silent schema drift, and a fragmented audit trail. To achieve data governance at scale, data architects must decouple validation rules from the execution engine. By utilizing a centralized metadata repository to dynamically generate validation suites, organizations can transform data quality from a reactive, script-based bottleneck into a configuration-driven infrastructure asset. The Metadata-Driven Architecture Instead of embedding validation constraints directly inside an ETL/ELT pipeline, this pattern isolates validation rules inside a centralized relational database schema. The orchestration engine programmatically queries this metadata at runtime, constructs the validation suites on the fly, executes them against target tables, and routes the evaluation metrics to an observability layer. This architecture provides three primary engineering advantages: Decoupled Governance: Data stewards can alter business rules or add expectations via simple DML updates without modifying or redeploying production application code.Schema Drift Resilience: The engine dynamically adapts to structural variations by programmatically evaluating target datasets against rules defined at the column level.Centralized Observability: Every rule execution generates a standardized, traceable metric payload, laying a consistent foundation for real-time data auditing and data lineage maps. 1. Defining the Metadata Schema (DDL) To implement this framework in an enterprise Lakehouse ecosystem, the metadata table must act as an immutable source of truth for constraints. Below is the production DDL required to initialize the control directory in Snowflake or Databricks: SQL CREATE TABLE data_quality_rules ( rule_id INT IDENTITY(1,1), table_name VARCHAR(255) NOT NULL, column_name VARCHAR(255) NOT NULL, expectation_type VARCHAR(255) NOT NULL, expectation_kwargs VARIANT NOT NULL, -- Stored as JSON object is_active BOOLEAN DEFAULT TRUE, updated_at TIMESTAMP_NTZ DEFAULT CURRENT_TIMESTAMP(), CONSTRAINT pk_rule_id PRIMARY KEY (rule_id) ); -- Seed metadata rules for execution tracking INSERT INTO data_quality_rules (table_name, column_name, expectation_type, expectation_kwargs) VALUES ('CUSTOMERS', 'CUST_ID', 'expect_column_values_to_not_be_null', '{}'), ('CUSTOMERS', 'AGE', 'expect_column_values_to_be_between', '{"min_value": 18, "max_value": 60}'), ('ORDERS', 'ORDER_ID', 'expect_column_values_to_not_be_null', '{}'); 2. Implementation: The Programmatic Execution Engine The core execution wrapper leverages Python and Great Expectations (gx) to programmatically turn rows of metadata into active validation suites. This script establishes a secure database connection via SQLAlchemy, harvests active constraints, generates runtime batch requests, and triggers structured checkpoints. Python import os import json import logging from datetime import datetime import pandas as pd from sqlalchemy import create_engine import great_expectations as gx from great_expectations.core.batch import RuntimeBatchRequest # Configure structured logging for production auditing logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') logger = logging.getLogger(__name__) class MetadataDataQualityEngine: def __init__(self, connection_string: str): self.engine = create_engine(connection_string) # Initialize Great Expectations ephemeral context for programmatic runtime control self.context = gx.get_context(context_root_dir=None) def fetch_active_metadata(self) -> pd.DataFrame: """Harvests active validation configurations from the centralized store.""" query = """ SELECT table_name, column_name, expectation_type, expectation_kwargs FROM data_quality_rules WHERE is_active = TRUE """ try: df = pd.read_sql(query, self.engine) logger.info(f"Successfully harvested {len(df)} active validation rules.") return df except Exception as e: logger.error(f"Failed to query metadata repository: {str(e)}") raise def compile_expectation_suite(self, table_name: str, rules_df: pd.DataFrame): """Assembles validation rules into a Great Expectations suite on the fly.""" suite_name = f"{table_name}_suite" suite = self.context.add_or_update_expectation_suite(expectation_suite_name=suite_name) # Filter metadata constraints for the specific target asset table_rules = rules_df[rules_df['table_name'] == table_name] for _, row in table_rules.iterrows(): # Parse JSON kwargs configuration gracefully kwargs = row['expectation_kwargs'] if isinstance(kwargs, str): kwargs = json.loads(kwargs) kwargs['column'] = row['column_name'] # Programmatically map string values to structured GX expectation objects expectation_config = gx.core.ExpectationConfiguration( expectation_type=row['expectation_type'], kwargs=kwargs, meta={"notes": f"Automated constraint enforcement for column: {row['column_name']}"} ) suite.add_expectation(expectation_config) self.context.add_or_update_expectation_suite(suite=suite) return suite def execute_quality_checkpoint(self, table_name: str, target_df: pd.DataFrame): """Builds a runtime batch request and evaluates data against the generated suite.""" timestamp = datetime.utcnow().strftime("%Y%m%dT%H%M%SZ") suite_name = f"{table_name}_suite" checkpoint_name = f"{table_name}_checkpoint" # Unique runtime composite signature prevents processing trace collisions batch_request = RuntimeBatchRequest( datasource_name="lakehouse_runtime_datasource", data_connector_name="runtime_data_connector", data_asset_name=f"{table_name}_{timestamp}", runtime_parameters={"batch_data": target_df}, batch_identifiers={"table_name": table_name, "execution_timestamp": timestamp} ) # Register and fire a dynamic checkpoint execution self.context.add_or_update_checkpoint( name=checkpoint_name, config_version=1, class_name="SimpleCheckpoint", validations=[{ "batch_request": batch_request, "expectation_suite_name": suite_name }] ) logger.info(f"Launching data quality checkpoint for table: {table_name}") return self.context.run_checkpoint(checkpoint_name=checkpoint_name) # Production Loop Execution Pattern if __name__ == "__main__": SF_CONN = "snowflake://<user>:<pass>@<account>/<db>/<schema>?warehouse=COMPUTE_WH&role=SYSADMIN" dq_engine = MetadataDataQualityEngine(connection_string=SF_CONN) metadata_rules = dq_engine.fetch_active_metadata() distinct_target_tables = metadata_rules['table_name'].unique() for current_table in distinct_target_tables: try: # Stage current batch dataset from target engine raw_data_df = pd.read_sql(f"SELECT * FROM {current_table}", dq_engine.engine) # Step 1: Build suite dynamically from relational rules dq_engine.compile_expectation_suite(table_name=current_table, rules_df=metadata_rules) # Step 2: Validate batch data and extract metrics payload eval_result = dq_engine.execute_quality_checkpoint(table_name=current_table, target_df=raw_data_df) if not eval_result["success"]: logger.warning(f"Data Quality anomalies detected on asset: {current_table}") else: logger.info(f"Asset {current_table} successfully cleared all metadata expectations.") except Exception as err: # Fault isolation ensures an asset failure never crashes cascading pipeline steps logger.error(f"Processing loop interrupted on asset {current_table}: {str(err)}") continue 3. Production-Grade Engineering Guardrails Building a dynamic system requires putting structural guardrails around the execution engine to prevent it from failing under enterprise pressures. Fault Isolation and Pipeline Resilience: Never let a validation failure on an upstream or non-critical business table halt your entire orchestration loop. Wrapping individual target assets in localized try-except blocks ensures that a failure on a secondary table (like CUSTOMERS) does not block downstream transactional tables (like ORDERS) from completing their validation lifecycles.Idempotency and Batching Identifiers: Every unique quality run must be traceable back to a specific moment in time to avoid overwriting or colliding results in your metadata tracking layer. Pair the table_name with an immutable execution_timestamp (such as a UTC ISO string) as a composite batch identifier. This guarantees an explicit audit trail across parallel streaming windows or backfilled data runs.Metadata-as-Code Frameworks: Treat the validation matrix table with the same operational rigor as production application code. Changes, additions, or deprecations of quality thresholds must follow a strict GitOps progression. Use schema migration version control tools (like Flyway or Liquibase) to manage, track, and deploy DML changes safely across staging and production clusters.Proactive Alerting Integration: Local HTML docs are insufficient for zero-downtime platforms. The metadata evaluation output dictionary must be integrated directly into cloud native alerting systems. Configure Webhook integrations or cloud alerting channels to route failed validation metrics directly to Slack channels or PagerDuty schedules. This ensures on-call engineers are proactively notified the moment a metric payload trends outside acceptable operational thresholds. Summary: The Architectural Impact Transitioning to a metadata-driven approach shifts data quality from a reactive "clean-up" task to an integrated, proactive engineering asset. By treating validation criteria as configurable metadata parameters rather than hardcoded script directives, architects eliminate technical debt and bridge the gap between business semantics and computing layers. This decoupled architecture provides the strict governance framework required to support high-stakes analytics and downstream machine learning layers, ensuring that every data element hitting your warehouse is automatically and transparently vetted before reaching production consumers.

Headless CMS architecture solved a real development problem. It separated content from presentation, gave frontend teams control over frameworks and deployment, and made structured content available to websites, apps, and other channels through APIs. The friction often appears later, when content operations become more complex. Routine publishing changes can still depend on engineering, especially when editors need more control over layout, preview, or page composition. That gap is why some teams consider a different architectural pattern: hybrid headless CMS. It keeps the structured, API-based approach of headless while adding a visual authoring layer to assemble approved components. The Authoring Problem Behind Pure Headless In a pure headless setup, the CMS manages structured content while the frontend controls how that content is rendered. For developers, that separation is valuable. Teams can use React, Vue, Svelte, native applications, or another presentation layer without tying the frontend directly to the CMS. The tradeoff becomes more visible when presentation changes frequently. A CMS may contain a hero title, image, CTA, and product description, but the frontend still determines how those elements become a page. Supporting visual preview, flexible layouts, and reusable page composition can therefore require additional engineering around preview APIs, component mapping, draft rendering, routing, deployment, etc. None of this is inherently a weakness in headless architecture. It is implementation work that teams need to account for. For applications with stable layouts and highly structured content, the model can work extremely well. For enterprises running many sites, markets, and campaigns, the amount of presentation-related work can become an operational bottleneck. When Content Work Becomes Engineering Work The clearest signal is the backlog. Consider a marketing team launching ten regional campaign pages. The content already exists, and no new application behavior is required. But several regions need a different component order, one needs an additional promotional block, and another needs a temporary landing page. In a tightly controlled pure headless implementation, those requests may still require developers to modify templates or component configuration. The workflow can become: Content request → development ticket → code change → review → build → deployment → editor validation That process makes sense when the requested change affects application behavior. It becomes expensive when the request is simply to rearrange approved components. Preview creates a similar issue. Headless systems can support preview, but developers often have to connect draft content with the rendering application so editors can see the actual result before publication. The CMS provides structured data. The frontend provides the presentation context. The distinction matters because the application still owns rendering, routing, accessibility, performance, and browser behavior. MDN provides useful background on the separation between server-side systems and client-facing application behavior. What Hybrid Headless Changes Hybrid headless keeps the API-based content model but adds visual composition capabilities for editors. Instead of letting editors create arbitrary frontend code, developers define the available building blocks. A content team can then assemble approved components through the CMS while the frontend remains responsible for how those components render. For example, developers might provide: HeroProduct gridCustomer quotePricing blockCTAFAQ Editors can change the order or selection of those components without changing the underlying application. The key difference is where composition happens. capabilitypure headlesshybrid headless Structured content Yes Yes API delivery Yes Yes Framework freedom Yes Yes Page composition Usually implemented in frontend logic Can be exposed through CMS authoring tools Visual preview Possible, often requires integration Commonly integrated into the authoring workflow Editor-controlled layouts Depends on implementation Typically a core capability Component governance Application specific Central to the model Definitions vary between CMS vendors, so engineering teams should evaluate the architecture rather than the label. A platform described as hybrid should still expose a clean delivery API that applications can consume independently. If the frontend becomes dependent on proprietary page rendering behavior, teams may reintroduce some of the coupling they were trying to remove. Developers Still Own the Architecture Hybrid headless changes who handles routine page composition, but developers still control the technical boundaries. They define components, validation, accessibility, performance, and application behavior. They also own the delivery contract between the CMS and frontend, including the security implications of new integrations and features. For teams adopting AI-powered capabilities, resources with AI security explained in practical terms can help clarify some of those risks. Overall, that means the architecture still depends on disciplined component and API design. Components that are too rigid send editors back to development tickets. Too many overlapping components create governance problems. The goal is simple: editors control approved composition, while developers retain control over how the application works. When Pure Headless Is Still the Better Fit Pure headless remains a strong choice when presentation is primarily application logic. A product dashboard is a good example. Developers may control nearly every screen because layout, state, permissions, and application behavior are closely connected. Pure headless also fits well when content changes are mostly structured data changes rather than page composition. Typical signals include: A small number of highly custom applicationsStable page structuresLimited need for editor-controlled layoutsContent reused heavily across channelsStrong frontend engineering capacityPresentation decisions that should remain in code In these environments, adding visual composition may introduce complexity without solving a real problem. When Hybrid Headless Becomes More Practical Hybrid approaches become more attractive when content operations generate repeated frontend work. Common signals include: Many sites, markets, or brands using the same component libraryFrequent campaign pagesEditors who need reliable visual previewRegular requests to rearrange approved page componentsEngineering queues filled with presentation changes that contain little new logicTeams that need stronger separation between component development and page assembly A useful test is to pull the previous quarter's engineering backlog and count how many tickets were created primarily to move an existing content block, change a layout, build a campaign page from existing components, or make another presentation change that required no new application behavior. Then look at who filed those tickets. If the same content or marketing teams repeatedly depend on developers for short-lived campaign changes, the organization may need more authoring autonomy rather than more frontend capacity. The Tradeoffs Hybrid Headless Does Not Remove Visual composition shifts work rather than eliminating it. Component governance becomes more important because shared components now act as an interface between engineering and content teams. Someone needs to own versioning, accessibility, documentation, budgets, and backward compatibility. Preview also needs production-quality engineering. A visual editor is useful only when what the editor sees accurately reflects what users will receive. Teams also need to decide how much flexibility to expose. Unlimited layout freedom can create inconsistent pages and undermine a design system. Too little flexibility recreates the ticket backlog the architecture was meant to reduce. The goal is controlled composition. Developers create safe building blocks. Editors assemble them within defined constraints. Evaluate the Workflow, Not the Label The architecture decision should start with the actual publishing workflow. Map who creates content, who changes layouts, who builds components, how preview works, what triggers a deployment, and which requests currently require engineering involvement. Then examine the CMS boundary. Can content be consumed independently through APIs? Can developers control component behavior? Can editors perform routine composition without changing application code? Can teams preview changes accurately? Can the architecture support additional channels without rebuilding the content model? Pure headless and hybrid headless preserve the same core idea: separating content from presentation. The practical difference is how much controlled presentation capability the platform gives back to content teams. For developers, the goal is to keep engineering focused on work that actually requires engineering. If developers are building components, integrations, and application behavior, the architecture is doing useful work. If they are repeatedly moving existing blocks around landing pages, the boundary probably needs another look.

By Alex Vakulov CORE