// VERIFY · SIMULATION & EVALS
Score every run, with the method that fits.
LLM-judge, assertion, rubric, dataset and trajectory scoring, applied online to live traffic or offline against versioned datasets. Scope each eval to the agents and runs it should grade, and calibrate judges against human labels.
THE PROBLEM
LLMs are easy to eval. Agents aren't.
A benchmark scores one answer. An agent plans, calls tools and loops; the answer can look fine while the trajectory was a nine-tool detour that got lucky. Scoring agents means scoring the path, not just the destination.
// HOW RUNAGAIN SOLVES IT
A dozen heuristic scorers (exact match, BLEU, ROUGE-L, JSON diff, regex…), built-in LLM judges for factuality, correctness, relevance, conciseness and safety, weighted multi-criterion rubrics, and dataset comparison. Or bring your own judge prompt.
Model-free tool-trajectory scoring grades how well the agent used its tools, with recovery invalidation so an agent that errored and recovered isn't punished like one that failed.
Attach evals to live traffic and scope them to all runs, one agent, or any metadata attribute, with a coverage slider so an expensive judge samples 10% while a cheap check runs on everything.
Annotation queues collect human ratings on real traces, and the judge-alignment table shows how well each LLM judge agrees with your reviewers, so you know when to trust the machine grade.
Evaluate is one stage of the loop: scores feed the next pass.
Wondering exactly what you can score with? Browse the full eval catalog →
COMMON USE CASES
RELATED SOLUTIONS
Be first to run again.
Book 30 minutes and see the loop on your own agents, or write to tamas@runagain.ai.