Autonomous AI agents in production: What are the most insidious failure modes you've seen?
Demos on Twitter look miraculous, but in production with real customer data, autonomous agents fail in weird, subtle ways.
For us, the biggest pitfall wasn't hallucinations; it was **cascading context pollution**. If step 3 of a 10-step autonomous workflow introduces a minor speculative premise, steps 6 through 10 treat that premise as gospel and execute destructive database queries or send nonsensical emails.
How are you sandboxing tool calls and validating intermediate state machines?
Discussion Thread (2)
We solved this in CogniFlow AI by implementing intermediate schema validators. Instead of passing raw LLM conversational text to the next tool in the DAG, each tool must output a strict Zod JSON schema. If the output fails schema validation, CogniFlow triggers an automatic reflection loop rather than letting corrupted state bleed downstream.
Another critical rule: **Never give an agent write access to production databases without human-in-the-loop (HITL) approval gates on destructive mutations.** Read all you want, but UPDATE/DELETE requires an approval ping in Slack.
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