Why AI Teams Drift — and How to Keep Them on Track

Kari Jaaskelainen

08 Jan 2026 — 1 min read

Multi-agent AI systems can solve complex tasks, but over long runs they may slowly veer off course — a phenomenon this paper calls agent drift. As interactions pile up, behavior degrades, decisions slip, and agents stop aligning with each other.

Semantic drift: answers stray from the original intent.
Coordination drift: consensus and role clarity break down.
Behavioral drift: unintended strategies emerge and compound.

To measure this, the authors propose the Agent Stability Index (ASI), a 12-dimension score tracking response consistency, tool-use patterns, reasoning-path stability, and inter-agent agreement — so teams can detect drift before failures pile up.

Simulations show drift lowers task accuracy and increases human intervention. Three practical fixes help: episodic memory consolidation, drift-aware routing, and adaptive behavioral anchoring. Together, they cut drift without slowing throughput — a step toward more reliable, safer agentic AI in production.

Paper: https://arxiv.org/abs/2601.04170

Paper: https://arxiv.org/abs/2601.04170v1

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