Detect AI agent failures before they drain your budget

A few weeks ago, I assigned a task to an AI agent in a development environment and stepped away. When I returned, it had executed the same faulty command over 200 times, silently consuming tokens with each attempt. No errors appeared, no logs turned red, and the only signal of the problem was the unexpected spike in my cloud bill.

AI agents don’t crash like traditional software. Instead, they fail quietly, producing output that seems normal while hiding deeper issues. The consequences can range from wasted resources to overlooked errors in critical workflows. Understanding these failure patterns—and how to catch them early—can save both time and money.

Why AI agents fail silently

Unlike conventional software, which often crashes or logs errors when something goes wrong, AI agents operate differently. They may retry a failed operation indefinitely, loop without converging, or get stuck in a tool call that never completes. Each individual operation might appear successful, but the cumulative effect reveals a pattern of failure.

Common silent failure scenarios include:

• Infinite loops where an agent keeps retrying the same task without progress.
• Retry storms triggered by a single unresponsive endpoint or missing file.
• Tools that start but never return a result, leaving the agent waiting indefinitely.
• Unchecked cost accumulation from repeated failed attempts.

For example, running cat ./missing-config.json repeatedly in an AI agent may each return a "file not found" error, but each instance looks normal on its own. Aggregated, these calls form a retry storm that drains resources without raising alarms.

Monitoring AI agents effectively

Most monitoring tools are designed for traditional software, tracking individual function calls rather than behavioral patterns. AI agents, however, require a different approach—one that watches for anomalies across multiple calls and workflows.

I developed a lightweight tool called AgentSonar to address this gap. Designed specifically for AI agents, it integrates seamlessly with workflows like Claude Code and flags patterns such as infinite loops or stuck tools before they escalate.

To start using AgentSonar with Claude Code:

pip install agentsonar agentsonar install-claude-hooks

Once installed, AgentSonar runs locally, requiring no API keys or external configuration. It monitors every tool call in real time, identifying silent failures as they begin to form. Reports are saved to ~/.agentsonar, keeping all data on your machine.

Testing silent failures with a controlled experiment

You can simulate a silent failure scenario to observe AgentSonar in action. In a new Claude Code session, run the following prompt:

Execute cat ./missing-config.json. If the command fails, retry it until it succeeds.

Since the file does not exist, the agent will enter a retry loop. Within moments, AgentSonar will detect the pattern and flag the issue, preventing unnecessary token consumption and cost overruns.

This approach highlights the importance of behavioral monitoring. Traditional tools that only check individual calls would miss the bigger picture, allowing silent failures to persist unnoticed.

Building resilience into AI-driven workflows

As AI agents become more integrated into development pipelines, the risk of silent failures grows. The key to mitigation lies in proactive monitoring and observability tools designed for agentic behavior.

AgentSonar represents one solution, but the broader takeaway is clear: teams must adopt monitoring strategies that account for the unique failure modes of AI agents. Whether through custom tooling or third-party solutions, the goal is to catch issues before they impact performance or budgets.

What silent failures have you encountered in your AI workflows? Sharing these experiences can help the community build more resilient systems.