The Latest and Popular SDLC Topics

Graph Engineering: The Layer After Loop Engineering

A single agent is just the smallest possible graph: one node with an edge back to itself. Most tasks should stay that simple.

August 14, 2026

by Vidyasagar (Sarath Chandra) Machupalli FBCS

CORE

· 2,871 Views · 4 Likes

Enterprise AI Data Engineering With Snowflake Cortex and RAG

Learn how Snowflake Cortex and RAG turn messy enterprise data into reliable AI answers: chunking, vector search, and production pitfalls.

August 13, 2026

by Karini Kapoor

· 1,857 Views · 2 Likes

Benchmark LangGraph, Strands, OpenAI Agents, and Google ADK on the Same Agent Graph

An experiment ranks them on latency and tokens with an LLM-as-a-judge quality guardrail; then the same flag promotes the winner to production with no redeploy.

August 13, 2026

by Scarlett Attensil

· 2,219 Views · 2 Likes

AI Assist vs AI Complete: The Real Gap in Most AI Workflows Today

The real difference between AI features and AI that finishes the job — most enterprise AI helps with a workflow but doesn't own the outcome.

August 13, 2026

by Muralidharan Lakshmanan

· 2,199 Views · 2 Likes

Orchestrating Small Language Models Without Losing Events or Context

Temporal and Kafka orchestrate small language models reliably through durable workflows, ordered events, idempotency, retries, replay, and context preservation.

August 13, 2026

by Akhil Madineni

CORE

· 2,164 Views · 4 Likes

From Microservices to Agent Services: The Next Architectural Shift

AI agents redefine service boundaries by introducing intent-driven orchestration, semantic capabilities, and autonomous decision services.

August 12, 2026

by Uthej Mopathi

CORE

· 3,105 Views · 3 Likes

The AI Memory Security Blueprint

Protect enterprise RAG systems with provenance, context isolation, and vector database governance to reduce retrieval poisoning and prompt injection risks.

August 12, 2026

by Igboanugo David Ugochukwu

CORE

· 3,736 Views · 3 Likes

Building AI-Driven Service Operations: Integrating CRM, Inventory, and Field Service

Effective AI-driven CRM implementations depend on integration with field service, inventory, ERP, and logistics so predictive insights can be executed successfully.

August 12, 2026

by Abhishek Sharma

· 1,982 Views · 3 Likes

Machine Identity Debt

Learn why traditional cloud infrastructure struggles with AI workloads and how modern identity, trust, and governance improve security in production.

August 12, 2026

by Igboanugo David Ugochukwu

CORE

· 2,166 Views · 3 Likes

The AI Software Supply Chain Blueprint

Learn how to secure AI pipelines with trusted models, verified dependencies, ML-BOMs, and supply chain controls from development to deployment.

August 11, 2026

by Igboanugo David Ugochukwu

CORE

· 3,407 Views · 2 Likes

Why Traditional Cloud Infrastructure Breaks AI Workloads in Production

Legacy cloud infrastructure can't keep pace with AI workloads. Let's deep dive into the key failure points and how to fix them in production.

August 11, 2026

by Mohit Shah

· 2,713 Views · 2 Likes

The Agent in Your Pipeline Doesn't Have a Manager. That's the Problem.

AI agents are flooding development environments faster than governance can keep up. Learn why visibility, identity, and access controls matter now.

August 11, 2026

by Igboanugo David Ugochukwu

CORE

· 1,741 Views · 1 Like

Incident Management and the Rise of AI SRE Agents

A newer category, dedicated AI SRE agents, goes further: they actively query logs, metrics, and deploy history live during an incident.

August 11, 2026

by Vidyasagar (Sarath Chandra) Machupalli FBCS

CORE

· 2,929 Views · 3 Likes

A Framework-Agnostic Approach to SSR for Microfrontends

Framework-agnostic SSR for independently deployed microfrontends — without a shared build, central orchestrator, or framework lock-in.

August 11, 2026

by Vitaly Zheltko

· 2,702 Views · 2 Likes

We Empowered AI Agents With 'Hands,' Now We Require Kernel-Level Vision to Monitor Them

MCP tool use creates massive application-layer blind spots. Close the gap by monitoring agent behavior directly in the kernel space.

August 11, 2026

by Ammar Ekbote

· 2,135 Views · 2 Likes

How We Built an LLM Pipeline That Survives Traffic Spikes

A traffic spike took down our LLM summarizer. Here is the severity-routing + token-governor design that keeps it alive. Plan in tokens, not requests.

August 10, 2026

by Dileep Mundakkapatta

· 1,938 Views · 1 Like

Building an AI-Powered Incident Triage Agent with .NET Aspire

A practical, code-driven tutorial on building an AI-powered incident triage agent using .NET 10 and .NET Aspire 9, and other modern tools.

August 10, 2026

by Muhammad Asif Nawaz

· 2,702 Views · 2 Likes

Agentic AI in 2026: How Autonomous AI Agents Are Replacing Manual Dev Work

Autonomous AI agents are moving from demos to production in 2026. See how agentic AI, MCP, and multi-agent systems are changing dev workflows.

August 10, 2026

by Ghulam Ghous

· 1,789 Views · 2 Likes

GraphQL Isn’t Dead Yet, AI Agents Revived It

GraphQL was good at a time, then it simmered off. Is GraphQL about to make a comeback because of AI? Will GraphQL be able to serve better for AI Agents?

August 10, 2026

by Akash Lomas

· 1,453 Views · 3 Likes

A Practical Pipeline for Identifying Sensitive Columns Before Test Data Masking

In this article, I will be introducing a pipeline designed to identify sensitive data columns before masking steps and improve the efficiency of the data masking process.

August 10, 2026

by Siyuan Feng

· 1,682 Views · 1 Like

The Latest Popular Topics