Managing Context in Long-Run Agentic Applications
AI Summary
This article explores how Slack manages context windows and state alignment in long-running, multi-agent security investigation systems. Because language model APIs are stateless and context windows have strict limits, accumulating raw message histories causes performance degradation and high latency. Slack introduces three complementary context channels—the Director's Journal, the Critic's Review, and the Critic's Timeline—to provide tailored, structured working memory for different agents without overwhelming them.
Why it matters
As engineers build more complex, autonomous multi-agent systems, managing context windows and preventing hallucination or loss of coherence become critical scalability bottlenecks. Understanding how to structure working memory and partition information across specialized agents provides a practical blueprint for building reliable long-running AI applications.
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