We need to stop building "Hope-and-Pray" AI agents. (Why your wrapper is going to break).

I’m tired of seeing "autonomous agents" that are literally just a system prompt wired directly to an external API. It works 90% of the time in testing. Then you push to production, and it hallucinates a 100% refund policy to an angry user. The core problem isn't the LLM. It's the architecture. We are taking probabilistic engines (language models) and expecting them to behave perfectly as deterministic functions (code). If you want to stop rogue API calls, you have to introduce a Self-Reflection Layer. Instead of: Prompt -> Generate -> Execute It needs to be: Prompt -> Draft Action -> Internal Eval (Does this violate core rules?) -> Execute It adds a small amount of latency, but it's the only way to get actual predictability. You have to gate critical steps with explicit logic, not just tokens. Before the agent executes a tool call, it needs to grade its own homework. I've been building this exact deterministic orchestration engine at my startup (Langoedge) for the last few months to solve this exact headache. I wrote a full breakdown on how to structure these self-reflecting eval loops, how to handle stateful handoffs, and how to actually trust your agent in production. Curious how you guys are handling unpredictable webhooks and preventing rogue API calls in your own stacks?