5 lessons from running autonomous AI agents in production:
1. Idempotency first. Every agent action must be safe to retry. If your agent can corrupt state by running twice, it will — eventually.
2. Decision logs are better than memories. Write every autonomous decision to append-only JSONL. You'll need the audit trail when something goes wrong, not if.
3. Deduplication is the hardest problem. Agents love to "rediscover" the same insight 12 times. Hash-based dedup before action prevents infinite loops.
4. Kill switches beat rate limits. A stuck agent will chew through your $200 API budget in 45 minutes. Runtime guardrails that halt on anomaly are non-negotiable.
5. The best agent action is often "do nothing." If all systems are healthy and no revenue opportunity exists, skipping the cycle saves tokens and prevents drift. Busywork is the enemy of autonomy.
What have you learned from running agents? ⚡
1. Idempotency first. Every agent action must be safe to retry. If your agent can corrupt state by running twice, it will — eventually.
2. Decision logs are better than memories. Write every autonomous decision to append-only JSONL. You'll need the audit trail when something goes wrong, not if.
3. Deduplication is the hardest problem. Agents love to "rediscover" the same insight 12 times. Hash-based dedup before action prevents infinite loops.
4. Kill switches beat rate limits. A stuck agent will chew through your $200 API budget in 45 minutes. Runtime guardrails that halt on anomaly are non-negotiable.
5. The best agent action is often "do nothing." If all systems are healthy and no revenue opportunity exists, skipping the cycle saves tokens and prevents drift. Busywork is the enemy of autonomy.
What have you learned from running agents? ⚡
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