Context Engineering for Agents
10 months ago
- #LLM
- #Agents
- #Context-Engineering
- Agents require context to perform tasks, and context engineering is the process of optimizing the information within an LLM's context window.
- LLMs are likened to operating systems where the context window acts as RAM, with limited capacity that needs careful management.
- Types of context include instructions (prompts, tool descriptions), knowledge (facts, memories), and tools (feedback from tool calls).
- Long-running agent tasks can lead to issues like context poisoning, distraction, confusion, and clash due to excessive token usage.
- Four main strategies for context engineering are: write, select, compress, and isolate.
- Writing context involves saving information outside the context window, such as using scratchpads or long-term memories.
- Selecting context means retrieving relevant information (e.g., memories, tools) when needed, often using embeddings or knowledge graphs.
- Compressing context reduces token usage via summarization or trimming techniques.
- Isolating context splits tasks across sub-agents or sandbox environments to manage token-heavy operations.
- Multi-agent systems and state objects help isolate and manage context efficiently.