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Failures & Fixes
How agents fail in the real world, and how to stop the bleeding.
- Why AI Agents Fail: Common Production Problemsβ β βWhy AI agents fail in production: infinite loops, tool spam, budget explosion, prompt injection, and runtime errors. Which failures happen most often and how to stop them.
- Agent Drift: When AI Agents Gradually Lose Focusβ β βAgent drift happens when an AI agent slowly moves away from the original task. Learn why it happens in production and how runtime limits help prevent it.
- Infinite Agent Loop: when an AI agent does not stopβ β βInfinite loop happens when an agent keeps generating new steps without real progress. Why this happens and how it is stopped in production.
- Agent Deadlocks: When Agents Block Each Otherβ β βA deadlock appears when multiple agents wait for each other and the system cannot move forward. Why this happens in multi-agent systems and how to prevent it.
- Tool Spam: When AI Agents Call Tools Too Oftenβ β βTool spam happens when an agent repeatedly calls the same tools without making progress. Learn why it happens and how tool limits stop it.
- Tool Failure: When Agent Tools Breakβ β βTool failure happens when external APIs or tools return errors, time out, or behave unpredictably. Learn how agents should detect and handle these failures.
- Token Overuse: When Agents Spend Too Many Tokensβ β βToken overuse happens when agents waste tokens on long reasoning loops or unnecessary context. Learn how to control token usage in production.
- Budget Explosion: When Agent Costs Spiralβ β βBudget explosion happens when uncontrolled agent execution causes API and model costs to rise fast. Learn how execution budgets keep costs predictable.
- Hallucinated Sources: When Agents Invent Sourcesβ β βHallucinated sources happen when an agent cites documents, links, or facts that do not actually exist. Learn why it happens and how to detect it.
- Response Corruption: When Agent Outputs Breakβ β βResponse corruption happens when agent outputs become incomplete, malformed, mixed, or logically broken across steps. Learn why it happens in production.
- Context Poisoning: When Agent Context Becomes Unreliableβ β βContext poisoning happens when memory, retrieved data, or prior messages contaminate the agentβs reasoning. Learn how bad context leads to bad decisions.
- Prompt Injection: When Agents Are Manipulatedβ β βPrompt injection happens when malicious input changes agent behavior, bypasses instructions, or triggers unsafe actions. Learn how production systems defend against it.
- Cascading Failures: When One Agent Failure Spreadsβ β βCascading failures happen when one tool, service, or agent error triggers a wider chain of failures. Learn why agent systems are vulnerable to this pattern.
- Partial Outage: When Part of the Agent System Failsβ β βPartial outages happen when only part of an agent system stops working while the rest remains available. Learn how this breaks pipelines and user flows.
- Multi-Agent Chaos: When Too Many Agents Competeβ β βMulti-agent chaos happens when too many agents interact without clear roles, limits, or coordination. Learn why complex agent systems become unstable.