Beyond Prompts: How Context and Harness Engineering Power Reliable AI Agents

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As developers build more complex LLM-based applications, simple prompt engineering is proving insufficient for tasks that require understanding codebases, running tests, and recovering from errors. Context engineering addresses what information a model should receive at any given moment, treating the context window as a finite resource that must be carefully curated rather than flooded with data. Techniques like RAG, memory systems, and just-in-time retrieval are all considered context-engineering strategies aimed at maximising signal over volume. Harness engineering goes a step further by providing the surrounding infrastructure — including tool execution, state management, permissions, validation, and observability — that allows an agent to act reliably in real environments. Together, these three disciplines form a more complete framework for building AI agents capable of handling sophisticated, multi-step software tasks.

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