When people talk about AI agents, the conversation usually revolves around reasoning, planning,...
AI agents spiral in infinite retry loops on failures, wasting tokens and causing cascading damage without failure safeguards. Circuit breaker patterns—retry limits, spend caps, approval gates—are essential governance controls for autonomous agents in production.
When people talk about AI agents, the conversation usually revolves around reasoning, planning, memory, and tool usage.
What gets discussed far less is what happens when the agent is wrong.
A few months ago, while experimenting with autonomous workflows, I noticed something interesting. The agent wasn't failing in a dramatic way. It wasn't crashing. It wasn't throwing exceptions.
It was simply trying harder.
A tool call failed.
The scariest AI agent failures don't trigger alerts. They look like success. Here's a 7-dimension...
A few months into building AI agents for client projects, we hit a pattern that should sound familiar...
Have you noticed how every demo of "an AI agent" looks impressive in the video and falls apart the...
Most "AI agents" you see online are basically: a scheduled loop a prompt a couple API calls That...
Most of the failures I see in production AI agents aren't reasoning failures. The model picks the...
Picture this: you write a requirement. Clear, specific. The agent reads it, does exactly what you...
