What's the biggest bottleneck preventing AI agents from production?
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Everyone is talking about AI agents, but building a demo and deploying them in production are two very different things.
While building an AI agent orchestration platform, I expected prompting to be the hard part. Surprisingly, it wasn't. The biggest challenge has been making agents reliable enough to trust without constant human supervision.
For us, that meant experimenting with builder/tester agent pairs instead of relying on a single agent to verify its own work.
I'm curious what others have experienced.
What's been the biggest blocker in your production deployments?
Reliability?
Cost?
Security?
Debugging?
Something else?
I'd love to hear real experiences from people building or deploying AI agents.
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For us it isn't really agent reliability in isolation, it's what happens after the agent's write succeeds. It updates a record correctly, but two or three other systems built assumptions on the old state and nothing tells them it changed. Cache invalidation, another screen reading stale data, that class of bug is invisible in testing because each piece works fine on its own with everything else mocked out. Debugging is the symptom you notice, the actual blocker is state going stale in places the agent never touched directly.
For me, the biggest blocker is recoverability, not raw model capability. An agent will eventually make a wrong assumption, hit a timeout, or update one system while another still holds stale state.
Builder/tester pairs help, but I’d also want every workflow to be idempotent, observable, and able to stop safely. The agent should know what changed, verify the downstream state, and either retry, roll back, or ask for approval instead of quietly continuing.
Production trust probably comes from making failures easy to detect and recover from, not trying to eliminate them completely. How are you handling partial success when one tool call works but the next one fails?