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General agent concepts

Failure modes

A failure mode is a recurring, nameable way an agent goes wrong. Common ones: hallucinated facts, wrong tool selection, malformed tool arguments, context lost mid-conversation, loops that never terminate, tasks declared done that were not, instructions overridden by retrieved content.

Failure modes exist because agent failures share causes. A given prompt, retrieval setup, or tool interface produces the same class of error across many different inputs, so failures that look unrelated in isolation often turn out to be the same mode with the same cause.

Naming a mode makes it measurable. Once a failure mode has a name, it's concrete enough to write a grader for, count, and track over time. A mode observed at a specific rate is a measurement, and a set of modes with rates describes an agent's reliability precisely enough to compare across versions.

Which modes dominate depends on the architecture, because each architecture creates its own opportunities to fail. RAG agents fail at grounding, tool-heavy agents fail at argument formation, and multi-agent systems fail at handoffs. The distribution across modes is usually skewed, with a small number of modes accounting for most observed failures.

7 questions

Answered, plainly.

Are most agent errors caused by the agent's own logic?No. In Datadog's 2026 report, 5% of LLM call spans errored in February 2026, and 60% of those were exceeded rate limits, not the agent's reasoning.answerCan content an agent retrieves override its own instructions?Yes. Text inside a webpage, document, or tool result an agent reads can contain instructions the model follows, a failure called indirect prompt injection.answerHow do multi-agent systems actually fail?Across three root causes: 41.77% specification issues, 36.94% inter-agent misalignment, and 21.30% task verification failures, per the MAST taxonomy's 1,600+ annotated traces.answerHow do I catch an agent that claims a task is done when it isn't?Grade the state a session actually left behind, a payment reference, a ticket status, a booking, against what the user asked for, instead of trusting the agent's own closing message.answerWhat causes a retry storm in an agent?A permanent error the retry policy treats as temporary, most often a tool name the model invented; one ReAct benchmark saw 466 of 513 retries hit exactly that.answerWhat is inter-agent misalignment?The failure category where agents miscommunicate, lose context at a handoff, or return contradicting results, distinct from one agent simply getting its own task wrong.answerWhy did an error one agent made become the next step's ground truth?Because a wrong intermediate output gets consumed downstream as input, and the next step reasons correctly from a false premise; one study found consistency fall to 23.5% at ten hops.answer

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