Technical guides and step-by-step instructions on building cloud-native applications, engineering thought leadership, and scaling Agentic AI.
One harness does not fit every programming language, domain, or review type. Learn how to build dynamic tool registries that serve language-specific tools, a model router that assigns the right LLM to the right task complexity, and domain-tuned system prompts — then wire everything together into the complete DevPulse production system.
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.