Context Management in Long-Running Multi-Agent AI Systems
This article from Slack Engineering details the architectural approach to managing context in complex, long-running multi-agent AI systems, specifically in a security investigation service. It addresses the challenges of stateless language model APIs and limited context windows by introducing a multi-channel context management system. The system balances the need for agent coherence with creativity by providing tailored views of investigation state.
Read original on Slack EngineeringThe Challenge of Long-Run Coherence in AI Agents
Language Model (LLM) APIs are inherently stateless, meaning that each request is independent. For long-running applications, maintaining continuity requires the caller to send the entire message history with every request. This rapidly consumes the LLM's "context window", which has a hard limit on the amount of information an agent can process. Exceeding or even approaching this limit can significantly degrade response quality and increase costs. Multi-agent systems, like Slack's security investigation service, amplify this problem as each agent needs a relevant, but not overwhelming, view of the overall investigation state.
Balancing Continuity and Creativity with Context Channels
Slack's solution employs three complementary context channels, each serving a distinct purpose, to provide tailored information to different agents without overwhelming them or stifling independent reasoning. This design ensures agents are anchored to the wider team while still allowing for specialized focus.
| Context Channel | Purpose | Primary Consumer |
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