Coverage — Definition

In this article, we walk through how to evaluate and strengthen coverage, what makes a use case genuinely “in coverage,” and the steps teams can take to broaden safe, high-quality automation. 

What is Coverage?

Coverage measures how fully an AI agent is prepared to handle the customer intents and workflows it’s responsible for. It reflects how many real-world use cases have the right content, rules, integrations, guardrails, and testing in place so the agent can respond accurately and safely.

High coverage requires clear intents, grounded knowledge, validated workflows, and safe, policy-aligned actions.

A use case is considered “in coverage” when the AI agent has:

• Authoritative, grounded content to answer relevant questions.

• Clear intent definitions with positive and negative examples, boundaries, and disambiguation guidance.

• Deterministic guardrails and policy checks for any operational actions, including constraints on data access and system updates.

• Tooling and workflow integrations required to read or write system states.

• Pre-deployment validation, including simulations and regression tests.

• Defined escalation logic to handle out-of-scope or ambiguous cases.

A “covered” use case does not guarantee that it will be autonomously resolved—it guarantees the agent can handle the scenario safely and reliably.

Scope for Coverage

Organizations typically map coverage across varying layers:

1. Informational coverage

The agent can answer questions related to:

• Policies

• Documentation

• Product features

• Troubleshooting guidance (non-actionable steps)

• Process explanations

This only requires grounded information and intent clarity.

2. Procedural coverage

The agent can guide or triage:

• Account verification

• Eligibility checks

• Multi-step troubleshooting

• Data collection

• Routing to the correct team

This requires structured workflows and reliable transitions, but does not involve system-write operations.

3. Operational coverage

The agent can execute tasks involving system reads/writes:

• Subscription changes

• Refunds or credits

• Order updates that require authenticated system writes

• Account modifications

• Ticket creation or status updates

This requires policy enforcement, workflow integrations, auditability, and testing.

Coverage expands as the business provides more validated content, more integrations, and more confidence in guardrails.

Evaluation & Metrics

Primary metrics

• Coverage rate — % of defined intents or workflows that the agent is equipped to handle

• Intent mapping completeness — % of expected real-world intents with classification, examples, and routing

• Knowledge completeness — % of required content available, accurate, and grounded

• Workflow readiness — % of operational workflows fully tested and connected to the agent

• Intent distribution coverage — % of high-volume intents that are fully covered relative to their share of incoming traffic.

Supporting metrics

• Gap analysis (% of user messages mapped to uncovered intents)

• Regression test pass rate

• Safety or policy compliance rate

Coverage metrics ensure the agent is prepared, even before measuring resolution performance.

Safety, Governance & Preconditions

Before marking a workflow as “covered,” organizations should ensure:

• Policy checks exist for any action that modifies system state.

• Audit logs track all decisions, writes, and escalations.

• Simulation coverage validates behavior across variations and edge cases.

• Regression tests ensure updates do not break previously covered workflows.

• Fallback and escalation paths are defined for ambiguous or risky inputs.

Governance protects against partial or incomplete coverage that could lead to unsafe actions.

Common use cases for Coverage

Coverage assessment is essential for:

• Support automation programs moving toward autonomous resolution, where coverage maturity is required before enabling full automation

• Mapping customer intent across email, chat, in-app messages, and self-serve

• Identifying high-volume gaps the agent should learn next

• Prioritizing integration or knowledge work

• Tracking readiness as AI capabilities expand

Coverage provides the roadmap for scaling AI agents safely and effectively.

FAQ

How is coverage different from resolution rate?

Resolution rate measures what the agent has successfully completed with users. Coverage measures what the agent is capable of completing. High coverage is a prerequisite for high, safe resolution rates.

Can a use case be covered without being automated?

Yes.  Coverage can exist at the informational or procedural level even when operational automation is not yet implemented. Coverage typically expands in progressive layers.

What limits coverage?

Usually:

• Missing documentation Key knowledge, steps, or policies aren’t captured in a form the agent can use.

• Undefined or poorly scoped intents The agent doesn’t have clear intent definitions, examples, or boundaries, which makes it unclear when a query should be recognized, how it should be handled, or where its limits are.

• Lack of system integrations The agent cannot read or write required data, making operational tasks or personalized responses impossible.

• Unclear policy boundaries Rules, approvals, risk thresholds, and exceptions aren’t documented or encoded, preventing safe autonomous action.

• Insufficient testing or safety requirements Missing validation, simulation, or guardrail checks leave gaps in quality or reliability.

Coverage grows as these gaps are identified, clarified, and addressed.

How often should coverage be reassessed?

Coverage should be reviewed on a recurring schedule—typically monthly or quarterly—as well as any time new workflows, policies, or product changes are introduced. Because agents rely on grounded content, policy alignment, and validated workflows, even small changes in a business process or system integration can create new coverage gaps if not reassessed.

Does increasing coverage require adding more automation?

No. Increasing coverage does not automatically require enabling autonomous actions. Organizations can expand informational or procedural coverage first—improving clarity, knowledge depth, and intent definitions—without activating any new system-write capabilities. Coverage maturity often grows ahead of automation adoption so teams can validate guardrails and ensure safe readiness before enabling operational workflows.