Context Engineering for AI Agents: What to Feed an Agent and What to Leave Out

Context engineering for AI agents involves selecting the smallest, most relevant set of instructions, tools, knowledge, memories, live data, and handoffs that an agent needs at each step, rather than simply expanding its context window. In operational settings, the article argues that context must function as shared infrastructure so agents can build on reviewed findings from previous incidents instead of repeatedly starting from scratch. It distinguishes stable, always-loaded material such as instructions and safety rules from information retrieved on demand, including runbooks, logs, metrics, and specialized procedures. Excessive, irrelevant, or outdated context can reduce reliability, increase hallucinations, and raise token, latency, and tool-use costs, even when an agent reaches the correct conclusion. Recommended practices include filtering raw data before presenting it to models, narrowing retrieval by metadata, using specialist agents with shared findings, storing concise facts rather than transcripts, reviewing and expiring memories, and measuring context costs per tool call. Komodor describes its platform as implementing these ideas through a shared knowledge graph, curated knowledge base, agent memory, change history, and review processes intended to keep operational knowledge current, permissioned, and reusable across agents.

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