Context Engineering for AI Agents: Lessons from Building Manus

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Keywords: Context Engineering, AI Agents, KV-Cache, Prompting, Error Recovery

Overview: This article discusses the practical lessons learned while building Manus, an AI agent, focusing on context engineering techniques. It emphasizes the importance of optimizing the KV-cache hit rate for efficiency, strategically masking tools instead of dynamically removing them, and using the file system as an extension of the agent’s context. The author also highlights the benefits of manipulating attention through task recitation, embracing errors for learning, and avoiding over-reliance on few-shot prompting to maintain agent diversity and robustness. The core message is that effective context engineering is crucial for building scalable, reliable, and adaptable AI agents.

Section-by-Section Summary:

• Design Around the KV-Cache: The KV-cache hit rate is crucial for AI agent performance, impacting latency and cost. Improving this rate involves keeping the prompt prefix stable, ensuring context is append-only, and marking cache breakpoints explicitly. Utilizing prefix/prompt caching in frameworks like vLLM can further optimize performance.

• Mask, Don’t Remove: Avoid dynamically adding or removing tools mid-iteration to prevent KV-cache invalidation and model confusion. Instead, use a context-aware state machine to mask token logits, controlling action selection without modifying tool definitions. This approach ensures a stable agent loop even under a model-driven architecture.

• Use the File System as Context: Treat the file system as an unlimited, persistent context for the agent, allowing it to write to and read from files on demand. Design compression strategies to be restorable, preserving URLs or file paths to avoid permanent information loss. This approach could pave the way for efficient agentic State Space Models (SSMs) that externalize long-term state.

• Manipulate Attention Through Recitation: By constantly rewriting a todo list, the agent recites its objectives into the end of the context, pushing the global plan into the model’s recent attention span. This avoids “lost-in-the-middle” issues and reduces goal misalignment, biasing the model’s focus toward the task objective. This is achieved without needing special architectural changes.

• Keep the Wrong Stuff In: Instead of hiding errors, leave them in the context to allow the model to learn from its mistakes. Seeing failed actions and their results updates the model’s internal beliefs, reducing the chance of repeating the same error. Error recovery is a key indicator of true agentic behavior.

• Don’t Get Few-Shotted: Avoid over-reliance on few-shot prompting, as it can lead to the model mimicking patterns even when they are no longer optimal. Increase diversity in actions and observations to break the pattern and tweak the model’s attention. Uniform contexts can make agents brittle.

Related Tools:

• Manus
• vLLM

References:

• In-context Learning
• BERT
• GPT-3
• Flan-T5
• Typical Agent
• KV-cache
• Autoregressive
• RAG
• Constrained Decoding
• Hermes format
• State Machine
• Neural Turing Machines
• Temperature
• Few-shot prompting

Original Article Link: https://manus.im/blog/Context-Engineering-for-AI-Agents-Lessons-from-Building-Manus

source: https://manus.im/blog/Context-Engineering-for-AI-Agents-Lessons-from-Building-Manus