Technical guides and step-by-step instructions on building cloud-native applications, engineering thought leadership, and scaling Agentic AI.
Moving DevPulse from a working prototype to a production system. Learn how to implement persistent SQLite checkpointing so agents survive crashes, add LangSmith distributed tracing to debug multi-agent pipelines, inject human-in-the-loop approval gates for high-risk actions, and build cost safeguards that prevent runaway API spend.
Prompt engineering gets your agent working. Context engineering gets it to production. Learn the four strategies — Write, Select, Compress, and Isolate — that prevent context rot, reduce token costs by 80%, and maintain reasoning accuracy as task complexity scales. Applied throughout the DevPulse system.
When a single agent reviewing 23 files is too slow and too context-heavy, you need subagents. Learn how to dynamically spawn isolated child agents, run them in parallel with thread pools, handle failures gracefully, and aggregate findings — all without context pollution between files.
The harness is the execution engine of a deep agent. Learn how to build a production-ready LangChain harness with Pydantic-validated tools, multi-model fallbacks, and a middleware layer for caching, rate limiting, and audit logging — all wired into the DevPulse code review system.
Deep agents are not just smarter chatbots — they plan, delegate, persist state to files, and resume work across sessions. Learn the architectural principles that make them viable in production and build the foundations of DevPulse, a real AI-powered code review system.
In Part 1 , we defined the “Agentic Mindset.” Now, it’s time to move into production. In this post, we will build a fully autonomous Securit...