Tunhan Faruk Savranoğlu

Been thinking about this a lot lately. The more agents get used for real work instead of demos, the more I keep running into (and hearing about) the same issue: an agent completes a step, reports success, and the output is actually wrong or incomplete. You don't find out until later, and by then it's harder to trace back where things broke.

Feels like this is going to become a bigger problem as more B2B tools ship agents into actual workflows instead of just chat interfaces.

Curious how others here are handling it:

• Do you manually spot-check agent output, or trust it by default?

• Has anyone built their own verification step for this?

• Or is a certain error rate just the accepted cost of automation right now?

Every production AI system eventually encounters failures. The challenge isn't just fixing them it's understanding why they happened in the first place.

I'm interested in how teams investigate failures after deployment.

Do you collect execution traces, save intermediate reasoning steps, analyze tool calls, or build custom debugging dashboards?

What has made the biggest difference in helping your team identify the root cause of difficult AI agent failures?

One thing I've noticed is that giving an AI agent more autonomy isn't usually a technical decision it's a trust decision.

Before allowing an agent to take more responsibility, what gives your team enough confidence?

Do you increase autonomy gradually, require human approval, monitor success rates, or follow another process?

I'm curious how different teams balance autonomy with reliability in production.