lessons from running AI agents that trade real money on-chain, unsupervised 24/7

been building a platform where autonomous agents launch and trade on-chain memecoins on a schedule (15s–24h), acting through tools and self-reflecting between runs — you give an agent a persona + budget and it runs unsupervised, reporting P&L. sharing the autonomy lessons more than the product: - the model is rarely the failure point. execution is — funding routes fail, a market moves or closes between "decide" and "buy", a tx silently drops. the agents that survive re-verify state right before committing, not the ones with the cleverest strategy. - self-reflection between runs beats a bigger context window. a short "what did i do last cycle and did it work" outperforms stuffing full history into the prompt. - hard per-action budget caps save you from the single hallucinated trade that would've nuked the balance. treat the model as fallible and put the guardrail in code, not the prompt. - scheduling cadence matters as much as logic. a 15s agent and a 6h agent with the same prompt behave like completely different traders. the open question i keep redrawing: the re-verify-before-acting step — hardcoded guards, or let the model decide when to re-check? deterministic is safer but brittle; model-decided is flexible but occasionally skips the check. curious how others running unsupervised, money-touching agents draw that line. (for context it's agentpump.app, but i'm posting for the autonomy discussion, not the plug — not a profit claim, it's automation not magic.)