HomeResearch LibraryAgent Drift: Semantic Degradation in Long-Running Auton…
empirical studyChapter 1Microsoft Research · 2024

Agent Drift: Semantic Degradation in Long-Running Autonomous Systems

Research Team (Microsoft Research)

Abstract

We characterize the phenomenon of agent drift — the gradual degradation of semantic coherence in long-running autonomous AI systems. We identify three primary drift mechanisms: context contamination, goal displacement, and tool call entropy.

Eigenvector Insight — Zone III / PASF-PADE AnalysisNot part of the original paper
Eigenvector Research — Marco van Hurne
How this paper contributes to solving the Zone III problem (PASF-PADE)

This is the most important paper in the corpus for enterprise practitioners. Agent drift is the silent killer of long-horizon deployments. The three mechanisms — context contamination, goal displacement, and tool call entropy — are exactly what Eigenvector observes in production deployments.

Why AI is not sufficient for Zone III without this

Zone III refers to high-complexity, high-risk, long-running agentic workflows — the class of enterprise AI deployments where a single failure can cascade across hundreds of steps. Standard AI models, trained to predict the next token, are not inherently designed for durable, governed, multi-step execution. This paper addresses one or more of the structural gaps that make Zone III deployments unsafe without explicit architectural intervention.

Key Contributions

  • →Agent drift characterization
  • →Three drift mechanisms identified
  • →Mitigation strategies

Topics

semantic driftlong-horizon agentsreliabilitydegradation