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Plan Proactive Agent Eval Guardrails

plan_proactive_agent_eval_guardrails
Read-only

Maps evaluation gaps in proactive assistants to guardrails for state-machine, user simulation, goal inference, intervention timing, and multi-app orchestration.

Instructions

Map proactive-assistant eval gaps to PARE-style state-machine, active-user-simulation, goal-inference, intervention-timing, and multi-app orchestration gates.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
workflowNoProactive assistant workflow name.
appsNoApps involved in the proactive workflow.
statesNoModeled app states.
stateCountNoNumber of modeled states.
actionCountNoNumber of state-dependent actions.
taskCountNoNumber of benchmark tasks or scenarios.
hasStateMachineNoWhether apps are modeled as finite state machines.
hasActiveUserSimulationNoWhether active user simulation exists.
hasGoalInferenceEvalsNoWhether goal inference is graded.
hasInterventionTimingEvalsNoWhether intervention timing is graded.
hasMultiAppEvalsNoWhether multi-app orchestration is graded.
flatToolApiOnlyNoCurrent eval only covers flat tool calls.
proactiveWritesNoProactive agent can write or mutate state.
userVisibleActionsNoInterventions can notify, schedule, send, or affect users.
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The annotation declares readOnlyHint: true, and the description is consistent with a read-only mapping/planning operation. However, the description does not add deeper behavioral context beyond the high-level purpose, such as side effects, prerequisites, or invariant guarantees.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single, well-structured sentence that front-loads the action and lists key dimensions. No redundant information; every word earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 14 parameters (all optional) and no output schema, the description is too terse. It does not explain what the tool produces (e.g., a report, a plan, a set of gates) or how the parameters influence the result. The agent lacks information to fully understand the tool's role in a workflow.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds value by grouping the boolean parameters into a 'PARE-style' framework, giving higher-level meaning (e.g., hasStateMachine, hasActiveUserSimulation are part of the mapping). This goes beyond the schema's atomic descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb 'Map' and clearly identifies the resource 'proactive-assistant eval gaps' and the target 'PARE-style...gates'. It lists distinct aspects (state-machine, active-user-simulation, etc.) and stands out among siblings, which are a diverse set of tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description does not provide any guidance on when to use this tool versus alternatives. No explicit context, exclusions, or alternative tools are mentioned, leaving the agent to infer usage solely from the purpose.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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