Understanding Representational Drift in Long-Running AI Agents
Authors: Dr Swarneendu AI
Originally published on Towards AI.
After 500 cycles, a long-running agent is no longer the same agent that started. His goal has changed. Its constraints have been eroded. It’s measurable, it’s preventable, and no one built this instrument. Until now.
Fareed Khan’s longtime agent has survived the host’s reboot. It survived context overflow. It survived 31 elements over-ported to 14.
The article explains why representational drift is mathematically inevitable in long-lived agents: repeated lossy compression (summarization, decision logging distillation, and abstraction/consolidation) erases recoverable information, so that an agent’s output distribution deviates from its behavior at the start of the cycle (quantified via KL divergence). It then proposes a practical probe-based drift detector using lightweight multiple-choice questions with known correct answers and statistical hypothesis testing (chi-square) to detect changes in interpretation. When drift is detected, the recommended solution is to inject the original instruction as a targeted “drift correction” anchor into the active context, grounding the agent before the deviation worsens, thus keeping the KL divergence close to zero. The article concludes by emphasizing that this instrumentation can distinguish useful long-term agents from costly near misses and provides related references.
Read the full blog for free on Medium.
Published via Toward AI
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Note: The content of the article contains the views of the contributing authors and not of Towards AI.
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