
Context Engineering for Coding Agents
The 4 harness components that keep your context window high-signal.
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The 4 harness components that keep your context window high-signal.

The guide to isolating your harness and safely executing its commands, locally or remotely.

Context engineering techniques to avoid wasting tokens on what doesn't matter

One agent loop, 9 tools, and a terminal you can steer.

Designing the harness around the model, from the agent loop to a remote swarm.

Ingest, query, and serve a unified memory from a single database.

Turn dead notes into a living LLM wiki your AI agents can query, maintain, and grow

Build a unified memory (knowledge graph or an LLM wiki) and serve it over MCP servers or skills, so any agent, open or closed, plugs in within minutes.

Turn every AI agent change into a measured experiment you compare before and after to detect regressions and measure performance.

A component-by-component teardown of an agentic harness, from tools and skills to memory, sandbox, and permissions.

The resolution, deduplication, and review pipeline that keeps agent memory usable as it grows.