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Coding agents can now work across whole repositories, use tools, and handle longer software tasks, which makes context engineering an increasingly practical skill: deciding what an agent should know, how it discovers that information, and when it should receive it. This episode explains why more context isn't automatically better, and why building the environment around an AI agent is becoming part of building the software itself.
It draws on Martin Fowler and Thoughtworks' definition of context engineering as curating what a model sees to get a better result, covering instructions, guidance, tools, MCP servers, skills, subagents, and hooks as the mechanisms involved, and their warning that too much context reduces effectiveness and adds cost.
The central example comes from OpenAI's own experience building an internal product almost entirely with Codex agents, writing no code by hand. An early, sprawling AGENTS.md file crowded the agent's context and became hard to maintain, so the team rebuilt it as a roughly hundred-line map pointing to structured documentation elsewhere in the repository, along with exposed logs, metrics, and application behavior. The lesson: give an agent a map, not an encyclopedia.
The episode also explains progressive context disclosure, the idea of giving an agent enough to navigate at first and loading deeper detail only when a task requires it, and Agent Skills, which package instructions, scripts, and resources an agent can load on demand rather than keeping every rule permanently in context. It notes Thoughtworks' caution that reusing third-party skills without review carries serious supply-chain security risk.
The practical takeaway: start with one recurring development task, identify what an experienced engineer would need to know to do it, make that information and those tools discoverable, and use the agent's failures to improve the environment rather than piling on more prompt instructions. As Fowler puts it, even well-engineered context only shifts probabilities; it never guarantees the model's behavior.
Sources & References
Martin Fowler / Thoughtworks: Context Engineering for Coding Agents — https://martinfowler.com/articles/exploring-gen-ai/context-engineering-coding-agents.html
OpenAI: Harness Engineering: Leveraging Codex in an Agent-First World — https://openai.com/index/harness-engineering/
Thoughtworks Technology Radar: Progressive Context Disclosure — https://www.thoughtworks.com/radar/techniques/progressive-context-disclosure
Thoughtworks Technology Radar: Agent Skills — https://www.thoughtworks.com/en-ec/radar/techniques/agent-skills
OpenAI: The Next Evolution of the Agents SDK — https://openai.com/index/the-next-evolution-of-the-agents-sdk/
Voice narration is AI-generated.
