Automation consultant. I build AI-powered workflows using Claude, n8n, and open-source tools. Sharing practical guides on AI agents, no-code automation, and cost optimization.
The production failure patterns you describe are painfully accurate. In my experience building automation workflows with AI agents, the #1 killer is error cascading - when one tool call fails and the agent tries to 'recover' by making increasingly wrong decisions instead of gracefully degrading. The fix that worked best for me was implementing explicit checkpoint/rollback semantics - every agent action gets a snapshot, and on failure you roll back to the last known good state rather than letting the LLM improvise a recovery. Also, structured output validation between every step catches hallucinated parameters before they hit your APIs.
Network infrastructure engineer turned AI systems builder. Spent a career on switches, fiber, and network architecture. Now building AXIS — trust infrastructure for the agentic economy.
Thanks Archit — error cascading is exactly the nightmare scenario that pushed me to build this.
You nailed it: letting the LLM improvise a recovery is asking for trouble. They'll confidently make things worse.
The checkpoint/rollback pattern you describe is essentially what Sagas do in Nexus — every step gets a compensation action, and on failure you unwind cleanly instead of hoping the agent figures it out.
The structured output validation point is interesting. Right now Nexus validates at the orchestration layer (did the step succeed/fail), but validating the content of outputs between steps could catch hallucinated parameters before they propagate.
Would you want that as a built-in primitive, or more of a "validation agent" you wire into your workflow?
Curious what automation workflows you've been building.
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The production failure patterns you describe are painfully accurate. In my experience building automation workflows with AI agents, the #1 killer is error cascading - when one tool call fails and the agent tries to 'recover' by making increasingly wrong decisions instead of gracefully degrading. The fix that worked best for me was implementing explicit checkpoint/rollback semantics - every agent action gets a snapshot, and on failure you roll back to the last known good state rather than letting the LLM improvise a recovery. Also, structured output validation between every step catches hallucinated parameters before they hit your APIs.
Thanks Archit — error cascading is exactly the nightmare scenario that pushed me to build this.
You nailed it: letting the LLM improvise a recovery is asking for trouble. They'll confidently make things worse.
The checkpoint/rollback pattern you describe is essentially what Sagas do in Nexus — every step gets a compensation action, and on failure you unwind cleanly instead of hoping the agent figures it out.
The structured output validation point is interesting. Right now Nexus validates at the orchestration layer (did the step succeed/fail), but validating the content of outputs between steps could catch hallucinated parameters before they propagate.
Would you want that as a built-in primitive, or more of a "validation agent" you wire into your workflow?
Curious what automation workflows you've been building.