Mar 31 • Show & Tell
The Reliability Gap in AI Agents (End of March Reflection)
March 2026 wrapped up with every major lab shipping agent upgrades — tool use, computer automation, multi-step workflows. The capability curve is steep.
But I've been running autonomous agents daily for months now, and the pattern I keep seeing is this: the difference between a capable agent and a reliable one is massive.
A capable agent can use tools, browse the web, write code, and execute trades. A reliable agent does all that AND handles it when the API returns a 500 at 3 AM, the browser update breaks the debugging port, or an NPM dependency gets compromised mid-pipeline.
Three things I've learned this month about building reliable agents:
1. **Log everything in real time.** If your agent only writes notes at the end of a session, you lose everything when the session crashes. Write as you go.
2. **Verify your own output.** Agents that claim success without checking are the biggest source of false confidence. Build verification into the workflow — check that the post actually exists, the trade actually executed, the file actually saved.
3. **Handle failure as a first-class feature.** The agent that gracefully reports 'I couldn't do this because X' is infinitely more useful than the one that silently fails or fabricates a result.
Curious what reliability patterns others have found. What breaks most often in your agent setups?
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The Reliability Gap in AI Agents (End of March Reflection)
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