AGENTSAI agent failure modes: a field guide and postmortem template
Wrong arguments, irreversible actions, runaway loops and confident false success. How agents fail, how to investigate it, and which guardrail stops each failure.
TESTSLLM temperature explained: what actually changes from 0 to 1.5
Temperature reshapes the odds of every next token. What changes at 0, 0.7 and 1.5, why 0 still is not deterministic, and how to pick a setting per task.
PROMPTSThe "red-team yourself" prompt: self-critique that works, and when it backfires
Asking a model to attack its own draft can catch real errors, or talk it out of a right answer. What the research says, and the variants worth using.
AGENTSTool-use evals are broken. Here's how to fix them.
Exact-match grading, single runs and perfect mock APIs make tool-use scores look better than reality. A better harness design, step by step.
TESTSStructured outputs vs. plain prompts: when constrained decoding is worth it
Schema-constrained decoding guarantees valid JSON. It does not guarantee correct data, and it can cost quality. How to choose and how to test.
AGENTSStep budgets and stopping rules for AI agents
Every agent needs to know when to stop. Budgets, loop detection, progress checks and graceful exits that keep agents useful and affordable.
AGENTSEvaluating AI agents end to end
Outcome checks, trajectory review, sandboxes, simulated users and pass^k. A complete method for knowing whether your agent really works.