7 lessons
Agentic context management and the ReAct pattern
How an AI agent that plans, calls tools, and observes results manages its own growing context — and why this is a materially harder problem than a simple chatbot.
Tool calling and structured outputs
How a model actually "does things" instead of just talking — function/tool calling mechanics, JSON schemas, and why structured output is a context engineering concern, not just a formatting nicety.
Human-in-the-loop and oversight
Why fully autonomous context-engineered systems are often the wrong design goal, and the concrete patterns for keeping a human involved at exactly the right moments.
Multi-agent context sharing
What happens when several AI agents need to collaborate — the coordination patterns for sharing context between them without duplicating or losing information.
Context compression techniques beyond summarization
Prompt compression, selective context pruning, and structured formats — practical ways to shrink what you send without losing what matters.
Context window budgeting as a formal allocation problem
Turning "what fits in the context window" into an explicit, numerical allocation decision across competing needs, with a worked algorithm and code.
Capstone: a complete context engineering pipeline in C++
Combining retrieval, layered memory, agent scratchpad management, and explicit budgeting from every module into one working end-to-end system.