When RevOps AI Agents Break: How to Improve Accuracy Over Time

<p>Everyone’s shipping AI agents.</p><p><strong>Almost no one talks about what happens when they’re wrong.</strong></p><p></p><p>The hallucination nobody catches. A prompt that slowly stops producing the output you expected. Latency that makes a workflow unusable. An edge case that works perfectly in testing and falls apart the minute real users get involved.</p><p>Getting an agent live is one challenge. <strong>Making it more accurate, reliable, and effective over time is another.</strong></p><p></p><p>In this session, <a target="_blank" rel="noopener noreferrer nofollow" href="https://www.linkedin.com/in/alexfromnooks/" title="https://www.linkedin.com/in/alexfromnooks/">Alex Avila</a> (Nooks) and <a target="_blank" rel="noopener noreferrer nofollow" href="https://www.linkedin.com/in/gerard-martelly/" title="https://www.linkedin.com/in/gerard-martelly/">Gerard Martelly</a> (Vapi) will get into the less glamorous, but much more important, work that happens after launch.</p><p>They’ll share examples from agents and AI workflows they’ve personally <strong>built, broken, measured, and improved</strong>, including what went wrong and how those failures changed the systems around them.</p><p></p><p>At the center of the conversation is the <strong>learning loop</strong>: how you continuously evaluate agent output, identify where things are breaking, and feed those lessons back into prompts, architecture, guardrails, and workflows.</p><p>Because better agent performance isn't just about writing a smarter prompt.</p><p>It means deciding what “good enough” accuracy actually looks like. Building fallback logic for when things go wrong. Knowing when a human needs to stay in the loop. Measuring drift instead of discovering it from an angry user. And accepting that tuning isn't something you finish once and move on from.</p><p></p><p>🔑 <strong>Key Takeaways</strong></p><ul><li><p>Why reliable agent output depends on <strong>system architecture, fallback logic, and output parsing</strong>, not just better prompting</p></li><li><p>How to decide what level of <strong>accuracy you're willing to trade for speed</strong></p></li><li><p>A practical framework for deciding when an agent can operate autonomously vs. when a <strong>human should stay in the loop</strong></p></li><li><p>What to measure across <strong>accuracy, latency, adoption, and drift</strong></p></li><li><p>How to turn failures and user feedback into a <strong>continuous learning loop</strong></p></li><li><p>Lessons from AI agents that actually <strong>broke in production</strong>, and what Alex and Gerard changed as a result</p></li></ul><p></p><p>📣 <strong>Speakers</strong></p><ul><li><p><a target="_blank" rel="noopener noreferrer nofollow" href="https://www.linkedin.com/in/alexfromnooks/" title="https://www.linkedin.com/in/alexfromnooks/"><strong>Alex Avila</strong></a><strong>,</strong> Founding Solutions Engineer, Nooks</p></li><li><p><a target="_blank" rel="noopener noreferrer nofollow" href="https://www.linkedin.com/in/gerard-martelly/" title="https://www.linkedin.com/in/gerard-martelly/"><strong>Gerard Martelly</strong></a><strong>,</strong> Senior Manager, RevOps, Vapi</p></li></ul><p></p><p></p><p>Not a member of the RevOps Co-op yet? <a target="_blank" rel="noopener noreferrer nofollow" href="https://www.revopscoop.com/membership/join-the-club" title="https://www.revopscoop.com/membership/join-the-club">Join here</a> to connect with 20,000+ pros who love revenue operations!</p>