Agent Evals in CI/CD: From Vibe Checks to Gates
Most teams shipping agents rely on manual testing. Here's how to build automated eval pipelines that gate deployments with real quality thresholds.
Lab // Technical notes
AI Engineering Lab: Technical Deep Dives
Technical deep dives into AI agent engineering — architecture patterns, protocol internals, and the implementation details behind production-grade systems.
Context Engineering: Why It Matters More Than Your Model
Context engineering is the top challenge for 57% of orgs running agents in production. The full stack, from system prompts to MCP, with code.
Multi-Agent Systems: Patterns That Work Beyond the Demo
Single agents hit ceilings. How multi-agent architectures work in practice — orchestration patterns, failure modes, cost realities, working code.
Claude Code in Production: Hooks, MCP & Custom Skills
Hooks, plugins, MCP servers, skills, and CLAUDE.md turn Claude Code into your production dev workflow. Here's how each extension point works.
Anatomy of an AI Agent: How MCP Connects Your Systems
MCP is to AI agents what USB is to peripherals. How the protocol works, how to build an MCP server, and what production deployment requires.
AI Agent System Prompts: What Claude Code Reveals
We dissect Claude Code's actual system prompt and extract the design principles that make AI agents reliable and safe in production.
Anatomy of an AI Agent: How Tool Calling Actually Works
A technical breakdown of the tool-calling loop that powers modern AI agents — from prompt design to execution sandboxing.