AI Agent Error Handling Is Broken and Nobody Talks About It

The Retry Loop Is the New Black Hole

I talked to a dev who woke up to a $38 Anthropic bill because their agent got stuck in a retry loop overnight. The agent kept hitting the same failing Slack API call over and over. Each attempt triggered another request. The system didn't crash. It just burned money. This is not an edge case. It is the classic failure mode for autonomous agents according to multiple agent engineers. LLMs often hallucinate that tweaking one parameter will fix a hard error so they keep trying the same broken path. Your AI agent isn't down. It's just stuck in an infinite loop and your monitoring stack has no idea because the agent is technically still running.

The Hidden Cost of Bad Error Handling

• ●Infinite loops burn tokens at $15 per 1M tokens for OpenAI models
• ●Agents can easily rack up $50, $100 in overnight API costs
• ●Knight Capital lost $440 million in 2012 from a trading algorithm that ran wild
• ●TransAlta lost $24 million from a simple spreadsheet copy paste error
• ●Production outages caused by AI agents are already happening

Why Most AI Agents Don't Recover

The problem isn't that AI models can't fix mistakes. Some can self correct and notice their own errors. The problem is that most systems are not built to handle recovery gracefully. They either silently fail or they enter a retry spiral. Good error handling requires dead loop detection and fallback paths that don't just repeat the same broken action. You need to know when an agent is stuck. You need to have a plan for when an API call fails. You need to handle hallucinations without panicking. Most teams don't have any of this. They ship models and hope for the best.

The Real AI Agent Problem Is Control

The difference between an AI agent that works and one that burns money is how it handles errors. Anthropic Computer Use came out a year before OpenAI Operator but Operator is still struggling to reach basic task completion on OSWorld benchmarks. Coasty uses a computer use agent that controls real desktops browsers and terminals not simulated environments. Coasty hits 82% on OSWorld which is higher than any other agent. That performance gap isn't about raw model power. It's about better error handling and recovery. When an agent fails it doesn't spiral. It pauses. It retries with a different approach. It alerts a human. It moves to a fallback workflow. That is what makes a computer use agent actually useful in production.

Why Coasty Exists (And Why Your Current Agent Might Fail)

You can build your own error handling but it takes time and expertise. Coasty does it for you. Coasty uses a computer use agent that succeeds 82% of the time on OSWorld. That's higher than Claude Sonnet 4.6 at 72.5% and way ahead of OpenAI Operator at 38%. Coasty handles retry loops dead loop detection and fallback workflows automatically. You get a desktop app or cloud VMs for parallel execution and agent swarms. You can bring your own keys or use Coasty's free tier. If you're serious about AI automation you need an agent that doesn't just break. You need one that can handle the inevitable failures and keep going.