Understanding Context Engineering for AI Agents
When creating AI agents, simply choosing a powerful language model isn’t enough. The Manus project demonstrates that the way we design and manage the “context” — the information the AI uses to make decisions — is essential. This concept of “context engineering” plays a significant role in determining the agent’s speed, cost, reliability, and overall intelligence.
The Shift from Fine-Tuning to In-Context Learning
Initially, the Manus team focused on utilizing in-context learning from advanced AI models, rather than relying on slow, iterative fine-tuning processes. This method allowed for rapid updates, enabling changes to be implemented within hours instead of weeks. However, this approach brought its own set of challenges, leading to multiple framework revisions through a process termed “Stochastic Graduate Descent.” This experimental method highlights the complexity of achieving efficient context management.
Key Lessons from Manus for Effective Context Engineering
Design Around the KV-Cache
The KV-cache is crucial for enhancing agent performance, significantly impacting both latency and cost. As agents continually add actions and observations to their context, the amount of input can become much larger than the output. By reusing identical context prefixes, KV-cache can drastically reduce processing time and costs. For instance, there can be a 10x cost difference with specific models like Claude Sonnet.
To maximize KV-cache efficiency:
- Stable Prompt Prefixes: Even minor changes at the beginning of your system prompt can invalidate the cache. Avoid incorporating dynamic elements such as exact timestamps.
- Append-Only Context: Do not alter previous actions or observations. Ensure data serialization is deterministic to maintain cache stability.
- Explicit Cache Breakpoints: Some frameworks require manual insertion of cache breakpoints, ideally positioned after the system prompt.
Mask, Don’t Remove
As agents acquire more tools, the complexity of their actions can lead to confusion and inefficiency. Instead of dynamically loading tools, which can invalidate the KV-cache, Manus uses a context-aware state machine. This method masks token logits during decoding to ensure the agent only selects available actions, keeping the context intact and focused.
Utilizing the File System as Context
Even with large context windows, real-world observations can exceed limits, affecting performance and increasing costs. Manus treats the file system as an unlimited context resource. The agent can read from and write to files as needed, using this structured memory to manage context effectively. Compression strategies are also employed to retain crucial information while minimizing context length.
Manipulating Attention Through Recitation
Agents can easily lose focus during complex tasks. Manus addresses this by having the agent maintain a todo.md file, constantly updating its objectives and progress. This practice biases the model’s attention towards its overall plan, reducing issues of goal misalignment.
Keep the Wrong Stuff In
When agents make mistakes, the instinct is often to eliminate those errors. However, Manus found that retaining failed actions in the context allows the model to learn from its mistakes, which helps prevent future errors. This process of error recovery is vital for developing true agentic behavior.
Avoiding Few-shot Pitfalls
While few-shot prompting can be effective, it may lead to repetitive and sub-optimal behavior in agents. To counter this, Manus introduces controlled diversity in the context by varying serialization templates, phrasing, and formatting. This “noise” helps the agent break free from rigid patterns and avoid getting stuck in mimicry.
Conclusion
In summary, context engineering is a vital aspect of developing AI agents. It extends beyond simply leveraging powerful models; it shapes how agents manage memory, interact with their environment, and learn from their experiences. Understanding and mastering these principles is essential for creating robust, intelligent, and scalable AI agents.
FAQ
- What is context engineering in AI? Context engineering refers to the design and management of the information that AI agents use to make decisions, crucial for their performance.
- Why is KV-cache important? The KV-cache enhances agent performance by reusing context prefixes, which can significantly reduce processing time and costs.
- How can agents maintain focus during tasks? Agents can maintain focus by constantly updating a task list, which helps reinforce their long-term objectives.
- What should be done with agent mistakes? Retaining mistakes in the context allows agents to learn and avoid repeating errors, which is beneficial for their development.
- What are the risks of few-shot prompting? Few-shot prompting can lead to repetitive behaviors in agents, making it important to introduce diversity in the action-observation pairs.
