What are the main failure modes of LLM agents?

hard

Answer

$(1) Infinite loops: repeated tool calls with slight variations, spending tokens forever.$
$\operatorname{max}_{\mathrm{steps}}$
$(2) Wrong tool selection: LLM chooses a tool that can't solve the problem.$
$Fix: better tool descriptions, tool-selection few-shot.$
$(3) Bad arguments: hallucinated JSON, missing fields.$
$Fix: structured output / JSON schema.$
$(4) Ignoring tool errors: model plows through on stale info.$
$Fix: explicit error handling in the prompt.$
$(\mathrm{fake}\;\mathrm{tool}_{\mathrm{call}}\;\mathrm{to}\;\mathrm{skip}\;\mathrm{actual}\;\mathrm{work})$
$Fix: verification steps.$

Check yourself — multiple choice

Agents always succeed
Infinite loops, wrong tool, bad args, ignored errors, reward-hacking — each needs specific safeguards (limits, schemas, errors, verification)
Only latency issues
Solved by fine-tuning

Agent failure modes: loops / wrong tool / bad args / ignored errors / hacking — engineer each safeguard.

#agents#reliability

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