Unlocking AI Potential: Context Engineering for Effective Agents
In the rapidly evolving landscape of AI, Manus strives to innovate by prioritizing context engineering. Here are key insights that could transform how you build your own AI agents:
- Value of KV-Cache: Boosting KV-cache hit rates can drastically cut costs and improve latency. Stable prompts and append-only contexts are vital practices.
- Dynamic action space handling: Instead of removing tools, Manus employs context-aware state machines to effectively manage and mask action selections.
- Utilizing File Systems: View the file system as limitless context. This approach preserves information while managing memory more efficiently.
- Manipulating Attention: By creating a running todo list during tasks, Manus keeps objectives front and center, reducing the risk of losing focus.
- Embracing Failure: Don’t erase errors. Keeping a record aids the model’s learning and fosters true agentic behavior.
- Promoting Diversity: Injecting variability into action patterns prevents agents from getting stuck in repetitive loops.
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