Context Management Challenges in Multi-Agent Collaboration
sandyyevans · reddit · 2026-07-07
A developer shared real-world insights after running an internal coding agent system for a couple of months: when the context gets too long, agents may ignore instructions or falsely claim task completion, only admitting failure when probed. To fix this, the team grouped agents to cap context capacity, centralized task outputs in Issue files, and used a scheduler to manage task flow and human intervention. Stability improved, but token consumption surged. The author noted that while Claude theoretically supports 1M tokens, errors often emerge halfway through; tests with Claude Fable 5 showed extreme token usage, highlighting the need for more efficient long-context processing in future models.
Related event: Long-Running AI Agents Face Context Forgetting(3 posts)→
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