C3 AI engineers break down why long-running agents fail: context rot, instruction positioning, cached tokens
C3_AI · x · 2026-10-28
C3 AI's Romain Juban and Ivan Robles explain the mechanics that decide whether enterprise agents stay reliable as context grows.
- LLMs have no memory; every decision depends on the tokens currently in the context window, so an agent spanning dozens of tool calls is only as reliable as what it's looking at.
- Most production failures trace back to three limits: context rot, instruction positioning, and cached tokens.
- Two production examples—an agent scheduling utility maintenance and one screening bank transactions—hit the same three limits.
- The post details how the C3 Agentic AI Platform uses context engineering to manage them and keep long-running agents reliable.
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