Anthropic's Official Best Practices for Agent Engineering

thisdudelikesAI · x · 2026-07-16

Anthropic recently released an engineering guide, noting that traditional prompt engineering is outdated and the new core focus is context engineering. To maximize model capabilities in long-context and complex Agent tasks, the guide outlines several key practices:

Structured notes: Instead of cramming everything into the conversation history, Agents should persist their progress and plans in a NOTES.md file to avoid getting lost in lengthy histories.
Retrieval over recall: Longer contexts are prone to "context rot." Extract only the information needed for the current step and continuously summarize completed work.
Freeze prefixes: Keep stable information like system prompts at the very beginning and unchanged, placing dynamic content at the end. This maximizes Prompt cache hit rates and reduces costs.
Clean up unused tool calls: Raw outputs from old tools in the history can distract the model. Cleaning these up, combined with memory and context editing, yielded a 39% performance boost in internal evaluations.
Use sub-agents: Launch dedicated sub-agents with clean contexts for deep exploration tasks, passing back only concise summaries to keep the main Agent focused.
Self-verification: Provide test scripts for the Agent to check its own results, minimizing frequent human intervention.

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Anthropic's Official Best Practices for Agent Engineering

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