An agent that scores well on your task benchmark can still delete the wrong file, close a ticket it should have escalated, or push a change…
Most teams ship an MCP agent to production the same way they ship a feature: it passed the demo, the tests are green, and someone says "look…
The model at the top of the leaderboard is not necessarily the model you should ship. Leaderboards rank one axis, task score, on someone els…
Public MCP benchmarks cannot tell you whether your agent picks the right tool from your MCP servers. They test generic, fixed toolsets under…
Scoring only the final answer of an agent is dangerous. An agent can return the correct result while calling the wrong tool, retrying five t…
Benchmark contamination is when benchmark data leaks into a model's training data, so the model has effectively seen the test before taking…
There is no best LLM. There is only the best model for a specific job, measured on your task, through your harness, against your budget. So…
Yes, the harness affects LLM performance, and it affects it a lot. You do not ship a model. You ship a system: a prompt, an agent loop, tool…
LLM-as-a-judge is reliable, but only when you control for its known failure modes. Used as a raw scorer it inherits three documented biases…
pass@k is the probability that an agent succeeds on at least one of k tries. pass^k is the probability that it succeeds on all k tries. That…
A demo tells you an agent can do a task once. Production asks whether it can do the task a thousand times unattended without a costly mistak…
Use a public benchmark to survey the field and shortlist models. Use a private eval to decide what you ship. A benchmark is a shared public…
Contamination-free LLM evaluation means testing a model on data it could not have seen during training. If your evaluation set overlaps with…