Running AI agents in production at scale — what pain are you hitting, and what's actually working?
Not talking about building or demos. Talking about operating agents in live environments, across teams, with real business processes running through them.
If that's you — what are you running into day to day, and have you found anything that actually works?
The pain points I keep hearing about at this stage:
- Human-in-the-loop routing — agents that need approval on certain actions but there's no clean system for it. Someone becomes a bottleneck or nothing gets reviewed.
- No audit trail — when something goes wrong, nobody can reconstruct what the agent did, in what order, or what it had access to at the time.
- Tool and access sprawl — agents connected to multiple systems with no clean map of what's authorized to do what.
- Governance added after the fact — the agent ships, then legal or security starts asking questions nobody has good answers to.
- Can't hand it off — the person who built it is the only one who can run it, so it doesn't scale past one person.
Two things I'm genuinely curious about:
- Is this your reality, or is the real friction somewhere else entirely?
- If you've solved any of this — even partially — what did that actually look like?
Specifically interested in multi-agent setups and teams operating inside enterprise environments where compliance and accountability matter. That's a small crowd and Reddit might not be where they are — but worth asking directly.
u/No-Conflict4823 — 5 days ago