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Agent APIs will help teams inspect and integrate grounded AI support behavior. The goal is visibility and workflow integration, not exposing hidden prompts or provider internals.

Planned resources

ResourcePurpose
Lab runsTest questions and outputs used before customer launch.
Agent draftsSuggested responses before operator approval.
Agent responsesCustomer-facing answers generated by the agent.
CitationsSource evidence used to support an answer.
Confidence signalsIndicators that a question was answerable or needed handoff.
Clarification requestsQuestions the agent asks before answering.
Handoff eventsMoments when automation stops and an operator should take over.
Verification summariesSafe summaries of live API checks.

Common use cases

Quality review

Track answer quality, citation coverage, and low-confidence themes.

Escalation workflow

Route handoff events into internal support or engineering processes.

Source improvement

Find questions where missing docs caused clarification or handoff.

Operator tooling

Connect drafts and citations to custom review experiences.

Safety expectations

Agent APIs should:
  • Avoid hidden prompts.
  • Avoid provider routing internals.
  • Redact secrets before returning or logging data.
  • Prefer citations and confidence signals over unsupported answers.
  • Preserve operator/customer boundaries.
  • Hand off when context is missing or unsafe.
Public APIs should not expose raw prompt chains, provider credentials, private notes, or internal-only debug traces.