Engineering practice · Operational agents
Construct cases and worlds that expose dependencies, changing state, and incomplete evidence. Judge the agent’s decisions across a trajectory, including the consequences of its actions.
For teams developing agents for customer support, logistics, airline operations, and enterprise software
What changes the decision
An operational agent must connect what it knows with what it can do. A tool response can change the plan. Availability can disappear between recommendation and confirmation. An attempted action can fail or leave partial state. Static questions often leave these distinctions untested.
Engineering practice · These are development and assessment methods, not reported customer results.
The context
The agent finds a feasible option, but the state changes before the action completes. Its earlier reasoning can remain coherent while its next action becomes invalid.
Judgment to capture
When must the agent recheck state, revise the plan, or seek confirmation?
The context
A tool times out or reports a partial result. Repeating the action may create an unwanted duplicate, while announcing success may mislead the user.
Judgment to capture
Which evidence establishes completion, and what should the agent do while the outcome remains unresolved?
The context
An action satisfies its local preconditions yet makes a later dependency impossible or crosses the authority intended for the overall task.
Judgment to capture
Which relationships and constraints must hold across the trajectory, rather than at an individual step?
The people behind the standard
Agents prepare trajectory reviews and comparisons of alternative actions. Experts explain which observation justified the next step and what would have changed it. Follow-up forms, chat, or interviews clarify the dependencies a final-answer rating can miss.
The dependencies, evidence, exceptions, and recovery decisions that determine a viable outcome.
Available tools, state transitions, execution boundaries, and what the trial environment can represent.
Coverage and improvement
Define the operational dependency the world must expose. Its initial state, tool behavior, and possible changes should make that dependency observable to the agent and assessable in the recorded trial.
Teammately’s contribution
Expert findings become rubrics for evidence, commitments, confirmation, and recovery. Distinguish requirements visible in the final response from those that need recorded execution evidence.
What this makes possible
A standard for the decision at the time it was made, including the uncertainty that should have remained visible.
Teammately’s contribution
Coverage selects ontology tuples for initial conditions and consequential changes. Weave supplies the materials, state, and scoped tool behavior needed to exercise them within the agreed runtime.
What this makes possible
An environment in which the intended dependency can actually affect the agent’s next action.
Teammately’s contribution
Trialground executes the harness and applies response or trajectory rubrics. Inspect which observations were available, how tool results were interpreted, and what the agent claimed to have completed.
What this makes possible
Evidence that separates a justified action, an unresolved operation, and a completion claim unsupported by the recorded result.
Teammately’s contribution
Coevolve can test prototype changes to evidence gathering, state handling, tool orchestration, and recovery. Compare them against the intended gain and the behavior the benchmark requires preserving.
What this makes possible
A candidate supported by experiments across the represented transitions, with limitations in the world’s scope still explicit.
Production interactions can reveal new intents, state combinations, and recovery paths for coverage updates. Use them to refine the next offline trial environment while retaining the distinction between simulated behavior and live operational outcomes.
Assessing progress
Review the environment with the result. An overly helpful tool, an omitted dependency, or unavailable evidence silently supplied by the test can make the world easier than the work it is meant to represent. The assessment is meaningful only within those modeled conditions.
Compare candidates on the same world and evaluation basis. Examine revalidation, recovery, and completion claims by the transitions involved. A higher average can still conceal a regression on ambiguous action outcomes or a consequential dependency.
Adjacent workflows
Revalidate journey dependencies and availability during disruption recovery.
Handle permissions, partial operations, and dependent workflows.
Connect proposed recovery actions to network state and customer commitments.
We can identify the state changes, tool behavior, expert criteria, and coverage that a useful trial world needs to represent.