Definition
What is Agent Drift?
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The gradual behavioral deviation of an AI agent from its original goal, role, or correct operation over long-running tasks.
Agent drift is the umbrella phenomenon covering six distinct failure modes in long-running agentic systems: goal drift, context drift, role drift, tool-use drift, hallucination cascades, and plan decay. Most production agent failures trace to one of these mechanisms rather than to raw model capability, and most are solvable with context engineering rather than bigger models.
Further reading
Articles about Agent Drift
AI agent reliability is a context problem
AI agent reliability fails because the same task assembles different context every run. Non-determinism is a context engineering problem, not a model flaw.
Context pruning helps agents, until it doesn't
Context pruning helps AI agents in one regime and hurts in another. A 2026 study of models from 4B to 284B maps when to prune stale context and when not to.
Context bloat: why long-running agents break
Context bloat is when accumulated tool-call output crowds out an agent's task. Tool calls, not window size, break long-running agents. Here is the fix.
How agents manage their own context window
Agent context management is shifting from fixed harness rules to learned, runtime decisions an agent makes about its own window. What the 2026 research shows.
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