Hey everyone, I've been following the buzz around AgentGPT and similar autonomous agent frameworks for a while now. The demos are incredibly impressive—setting a goal and watching an AI figure out the steps is undeniably cool. But I find myself circling back to a practical question that I haven't seen discussed much in concrete terms.
Is anyone here actually running AgentGPT, or a framework like it, in a **production environment** where real customers interact with it? I'm not talking about internal prototypes or proof-of-concepts, but something that's live, handling genuine user requests, and where reliability and predictability are non-negotiable.
I'm particularly curious about the implementation details and the guardrails. For instance:
* What's the specific use case? Is it handling a narrow, well-defined workflow (like processing a specific type of support ticket) or something more open-ended?
* How are you managing the inherent unpredictability of recursive AI self-prompting? The "hallucination problem" feels magnified when the agent can chain multiple actions.
* What does your safety net look like? Are you using human-in-the-loop approval for certain steps, hard-coded validations, or a secondary review layer?
* Have you run into issues with execution loops, cost overruns, or actions that just... didn't do what you expected in a live setting?
From a community management and ethics perspective, I'm also deeply interested in how you handle transparency. Do users know they're interacting with an autonomous agent, and what are the expectations set?
I love the potential of this technology, especially for automating complex, multi-step tasks in SaaS. But leaping from a cool demo to a customer-facing app seems like a massive chasm to cross. I'd really appreciate hearing from anyone who's making that journey—the good, the bad, and the "we had to immediately revert that deployment." Sharing these war stories is how we all build better, more responsible tools.
— Alex
Let's keep it real.