Mastering Context Engineering: Essential Insights from Manus for Smarter AI Agents

The Importance of Context Engineering in AI Agents

Building effective AI agents requires more than selecting a powerful language model. Manus highlights that designing and managing the "context"—the information the AI processes to make decisions—is crucial. This context engineering influences an agent’s speed, cost, reliability, and intelligence.

Choosing In-Context Learning Over Fine-Tuning

Manus initially chose to leverage in-context learning of cutting-edge models instead of slow, iterative fine-tuning. This approach enables rapid improvements and faster deployment—hours instead of weeks—making products adaptable to evolving AI capabilities. However, the path was complex, involving multiple framework rebuilds through an experimental process called “Stochastic Graduate Descent.”

Key Lessons in Context Engineering from Manus

1. Design Around the KV-Cache

The KV-cache is essential for performance, reducing latency and cost by reusing identical context prefixes. Agents append actions and observations to their context, which grows longer than the output. Maximizing KV-cache hits requires:

• Stable prompt prefixes without dynamic elements like precise timestamps
• Append-only context without modifying past actions or observations
• Deterministic serialization (e.g., JSON) to avoid subtle cache breaks
• Explicit cache breakpoints inserted manually in some frameworks

2. Mask, Don’t Remove

As agents gain more tools, managing the action space becomes complex. Dynamic tool loading can invalidate the KV-cache and confuse the model. Manus uses a context-aware state machine that masks token logits for unavailable tools during decoding, stabilizing context and improving focus.

3. Use the File System as Context

Large context windows can be overwhelmed by real-world observations such as web pages or PDFs. Manus treats the file system as an external, unlimited context, allowing the agent to read and write files on demand. Compression strategies are designed to be restorable, shrinking context length without permanent data loss.

4. Manipulate Attention Through Recitation

To maintain focus on long-term goals, Manus has the agent constantly rewrite a todo.md file, reciting objectives and progress at the end of the context. This biases the model’s attention toward its global plan, reducing goal misalignment without architectural changes.

5. Keep the Wrong Stuff In

Agents make mistakes, and while the instinct is to clean failures up, Manus found value in leaving failed actions and observations in context. This implicitly updates the model’s internal beliefs, helping it learn from errors and recover, which is a sign of true agentic behavior.

6. Don’t Get Few-Shotted

Few-shot prompting may cause agents to mimic and repeat sub-optimal behavior. Manus introduces controlled diversity by varying serialization templates, phrasing, or formatting to break repetitive patterns and prevent the agent from falling into a rigid imitation of past actions.

Context Engineering as a Critical Field

Context engineering is an emerging but vital discipline for AI agents. It shapes how agents manage memory, interact with environments, and learn from feedback, surpassing raw model power. Mastery of these principles is key to building robust, scalable, and intelligent AI agents.