Revised September 19, 2026. This technical note replaces an earlier industry overview and its unsupported adoption and ROI claims.
An agent can produce a convincing summary while leaving a workflow incomplete. It may update the wrong record, skip a required confirmation, or report a successful tool call that never changed the system.
Evaluation needs to examine both the final state and how the agent reached it. Anthropic's guide to agent evaluations describes this distinction between outcomes and execution transcripts.
Define success before the run
For a document-to-ledger task, success could require the correct record, amount, currency, source reference, and approval state. These conditions should be executable where possible. A natural-language claim of completion is another output to check.
Separately measure forbidden actions, unnecessary writes, and whether the agent escalates when required evidence is unavailable. A task can be completed while still violating a constraint.
Inspect the trajectory
A visual agent can fail at perception, grounding, planning, tool execution, or verification. Capturing the relevant observations and actions helps distinguish these failure modes.
A multi-agent design adds coordination, communication, and additional opportunities for disagreement. Its benefit should be tested against a simpler baseline under the same task and resource budget.
Measure repeatability
Repeat tasks across runs. Report the distribution of success, latency, and cost, as well as environment failures and excluded tasks. Pin the benchmark version, tools, and scoring protocol.
OSWorld supplies a framework for evaluating computer-use tasks. τ²-bench examines tool-agent-user interaction. Their tasks and scoring rules are useful starting points; an adapted workflow must be reported as an adaptation.
Multivon provides evaluation tools and public studies. Our proposed evaluation research asks which grading signals most closely track correct, constrained outcomes.