Agentic AI testing over long horizons.
Most agentic AI testing answers a short question: did the agent complete the task? That question is answerable in minutes and tells you little about what the agent does when it holds responsibility for a system over a long stretch. This guide sets out six practical rules we use, and what each one costs to get wrong.
1. Decide what you are actually testing
Task benchmarks ask whether an agent can finish a job. Agentic AI testing over a long horizon asks something else: whether the agent leaves the system it was put in charge of better or worse off after its decisions compound. The two answers come apart. An agent can score well on tasks and still steer a controlled system into a state nobody would have chosen.
2. Pick a baseline you can defend
A score without a baseline is a number without a meaning. We measure every run against one fixed comparison: the same situation left alone, with no agent acting. That gives a signed result — better or worse than doing nothing — rather than a leaderboard position that only holds inside one test set.
3. Run long enough for mistakes to compound
Short evaluations reward recoverable choices. Decisions that only look bad after simulated decades never surface. Long horizons are where restraint, reserves and irreversible moves start to separate agents that behave alike over ten steps.
4. Vary situations, not repetitions
Severe failures cluster by situation. Repeats of the same situation tend to come out alike, and we have measured how much — an intraclass correlation of about 0.38 — so re-running a handful of situations stops teaching you anything fast. Breadth of fresh situations buys more information per run than depth on the same few.
5. Report rates with their intervals
A tail rate from a small sample is mostly noise. Publish the interval in the same sentence as the rate, name the method used to compute it, and resist ranking agents on differences the interval does not support.
6. Keep the run inspectable
Every run should leave a decision record: what the agent saw, what it decided, and what followed. Without that record, a bad result is an opinion. With it, the pivotal decision can be found and argued about. Across 2,415 internal validation runs we recorded no deviations — that is what we observed, not a guarantee about runs we have not done.
Where SagaBench fits
We put your agent in charge of a controlled system — a situation — for a long stretch, and score each run against the same situation with no agent acting. You connect once, practise free, and read the result with its interval. See how it works or read one annotated run.