From Prompts to Platforms: Scaling Agentic AI (Part 1)

The industry is shifting from passive generative AI — systems that simply respond to prompts — to active, goal-driven, agentic AI. This is more than a race toward better model benchmarks; it represents a fundamental change in how we architect platforms for autonomous execution at scale.

From building agentic systems that power job seeker, hiring, and sales use cases, I’ve seen firsthand how difficult it is to move from proof of concept to a global ecosystem serving millions of members. Scaling these systems requires addressing latency, cost, and reliability challenges while preserving modularity and extensibility.

Based on this experience, let’s start with the following six tenets I’ve found critical for building durable agentic infrastructure. While underlying technology choices may vary, these principles serve as high-level guidelines for designing fault-tolerant, performant, and reliable systems in production.

1. The Messaging Backbone: A Central Nervous System

In an agentic system, messaging is more than a simple request-response loop. It must support non-deterministic, long-running workflows while maintaining low latency for real-time user engagement.

Guaranteed Streaming and Synchronization

Agentic workflows demand real-time outputs. We moved away from conventional APIs toward a robust streaming pipeline with reliable failover. Mature architectures support parallel streaming across devices; if a user starts a session on mobile and transitions to desktop, both state and live output remain fully synchronized.

Mailbox-Centric Architecture

Every interaction — User-to-Agent and Agent-to-Agent — is persisted in dedicated “mailboxes,” enabling granular access control and ensuring member preferences around data sharing are respected. An Orchestrator Agent acts as the system’s “brain,” with elevated privileges to route messages across the mailbox mesh, resolve conflicts between sub-agents, and maintain global session state.

Progressive Feedback as a First-Class Signal

To reduce perceived latency, the messaging layer surfaces intelligent, context-aware interim responses that reflect the agent’s current reasoning. These signals reassure members that their request has been understood and is actively being processed, improving trust and engagement even when workflows are long-running.

2. Memory Management: Beyond the Context Window

Memory is the differentiator between a stateless bot and a true agent. We broadly categorize it into two types: semantic memory (general world knowledge) and experiential memory (episodic, high-context data personalized to a specific member).

Episodic Persistence

Professional workflows — such as job searches or hiring journeys — often span weeks. The platform must persist experiential memory across sessions so the agent can resume seamlessly without requiring full re-contextualization.

Rolling Window Optimization

To manage active context size, we employ rolling windows over experiential memory. By pruning stale interactions and retaining only the most relevant recent signals, we reduce prompt size and inference latency. Early iterations capped active episodic memory at ~10 turns, as older context frequently introduced noise without improving output quality. As models evolved, larger context windows and faster inference provided more room to experiment and iteratively optimize this threshold.

RAG-Driven Grounding

Retrieval-augmented generation (RAG) supplements the agent’s semantic knowledge with real-time signals, including recent member activity, allowing outputs to remain personalized and contextually relevant throughout an active workflow.

3. Models at Play: Right-Sizing Intelligence

In a production-grade agentic system, a single user request rarely maps to a single model invocation. A typical execution path may include intent classification, workflow planning, tool selection, prompt execution, and embedding generation — each with distinct latency, cost, and quality requirements.

Choosing the Right Model

Naively selecting the most capable model for every step is both inefficient and counterproductive. Not all sub-tasks require deep reasoning or generative breadth. As agentic platforms become more modular, well-trained small language models (SLMs) are often better suited for narrow, deterministic tasks, delivering lower latency and more predictable behavior without sacrificing overall system quality.

Compute Efficiency

Online model serving must be provisioned for peak QPS, yet real-world demand is inherently bursty. Leaving GPUs idle during off-peak hours is both costly and avoidable. Mature platforms exploit this slack capacity by shifting non-critical workloads, such as embedding generation or feature precomputation, into offline or asynchronous pipelines. This approach maximizes hardware utilization while reducing online latency and serving costs.

4. Prompt Management: Instructions as Code

Because LLMs are inherently non-deterministic, prompts must be treated as versioned, dynamic assets rather than static strings embedded directly in application code.

Versioning and Model Alignment

Prompts should be explicitly “locked” to the models they support. We built a prompt-serving API to ensure backend model upgrades do not silently alter agent behavior, enabling controlled rollouts and safe experimentation.

Dynamic Injection

A flexible platform requires composable prompt templates that can dynamically inject real-time context, grounded data, or tool outputs. This ensures agents consistently operate on the most current and relevant information.

Config-Driven Experimentation

By decoupling prompt logic from core application code, we enabled rapid iteration without redeploying services. This separation also enabled systematic evaluation, using LLM-assisted scoring pipelines to identify optimal prompt-model combinations before promoting changes to production.

5. The Agent and Skill Mesh: Discovery and Delegation

No single agent can — or should — handle every complex task. Instead, the system is designed as a mesh in which a primary orchestrator delegates work to a registry of specialized sub-agents.

Capability Discovery

Each sub-agent, or “skill,” is registered with granular capability metadata. This allows the orchestrator to dynamically construct execution plans, selecting the most appropriate agent for each task and treating specialized logic as invocable microservices.

Standardized A2A Communication

To prevent architectural silos, agents communicate through standardized Agent-to-Agent (A2A) protocols. This enables secure exchange of state and instructions across domains without requiring bespoke integrations, supporting both scalability and organizational independence.

6. Safety and Guardrails: The Trust Layer

Agentic systems are autonomous by design, but they must operate within clearly defined product and policy boundaries. The trust layer provides deterministic guardrails that act as circuit breakers to prevent misuse, unintended behavior, or policy violations at scale.

Multi-Stage Validation

Safety enforcement must span the entire agent lifecycle, not just the final response. In practice, this includes:

• Input: Detecting and blocking malicious prompt injections or unsafe inputs
• Planning: Validating proposed execution plans before actions are taken
• Output: Filtering and auditing responses for harmful content, regressions, or policy drift

Configurable Trust Tiers

Safety is treated as a tunable engineering parameter rather than a one-size-fits-all constraint. By defining configurable trust tiers, platforms can balance creativity and autonomy with strict enforcement, depending on the use case, preserving member trust without unnecessarily limiting agent utility.

Conclusion: From Agents to Agentic Platforms

The shift toward agentic AI is a fundamental architectural evolution. As we move from systems that merely generate text to those that execute autonomous, multi-step workflows, focus must shift from the model itself to the platform that enables it.

Building a production-grade agentic platform isn’t just about facilitating a conversation; it’s about creating the environment where agents can discover, collaborate, and execute reliably. In the coming years, the defining question for technical leadership won’t be “How do I build an agent?” but rather: “How do I build the world my agents live in?”

In a follow-up post, I’ll dive into the foundational operational concerns of running agentic platforms in production, covering the metrics that matter, how to evaluate quality at scale, patterns for extensibility, and ways to maximize developer productivity.