I found it more helpful to try and "steer" the LLM into self-correcting its action if I detect misalignment. This generally improved our task success completion rates by 20%.
Basically through two layers. Hard rules (token limits, tool allowlists, banned actions) trigger an immediate block - no steering, just stop. Soft rules use a lightweight evaluator model that scores each step against the original task intent. If it detects semantic drift over two consecutive steps, we inject a corrective prompt scoped to that specific workflow.
The key insight for us was that most failures weren't safety-critical, they were the agent losing context mid-task. A targeted nudge recovers those. Generic "stay on track" prompts don't work; the correction needs to reference the original goal and what specifically drifted.
Steer vs. kill comes down to reversibility. If no side effects have occurred yet, steer. If the agent already made an irreversible call or wrote bad data, kill.
nordic_lion|18 days ago
zachdotai|18 days ago
The key insight for us was that most failures weren't safety-critical, they were the agent losing context mid-task. A targeted nudge recovers those. Generic "stay on track" prompts don't work; the correction needs to reference the original goal and what specifically drifted.
Steer vs. kill comes down to reversibility. If no side effects have occurred yet, steer. If the agent already made an irreversible call or wrote bad data, kill.