Agentic AI failures aren't a model problem.
They're an architecture problem — and most teams only figure this out after they've already upgraded the model twice.
The debugging loop I've seen: flaky agent → swap in a bigger model → still flaky → add more context window → still flaky → eventually realize there's no retry logic, no verification gate between steps, no state that can recover mid-task.
A model call is non-deterministic. You wouldn't call an external API without error handling and retries. But "call the LLM and trust the output" is exactly what most agent pipelines do in production.
The fix isn't a smarter model. It's designing at the orchestration layer — retry budgets per step, verification gates that catch bad output before it propagates, HITL checkpoints for decisions you can't undo.
"Agents are unreliable" is the symptom. Missing scaffolding is the diagnosis.
Is your agent pipeline designed to tolerate a 30% failure rate on any given step — or are you counting on the model to never fail?
https://www.trantorinc.com/blog/ai-agent-failure-modes-what-goes-wrong-design-resilience
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