Autonomous Resolution — Definition
An autonomous resolution is a completed customer request where an AI agent - without human intervention - determines intent, gathers and grounds evidence, evaluates policies, executes required system actions (orchestrated workflows), and confirms resolution with the customer.
Autonomous resolutions require deterministic guardrails, auditable actions, and pre-deployment validation to be production-safe.
What is an autonomous resolution?
An autonomous resolution occurs when a customer’s issue or task is taken from initial request to final outcome entirely by an AI agent.
That outcome can be informational (the AI agent answers a question and the customer confirms satisfaction) or operational (the agent reads account state, writes changes - refunds, subscription updates, order edits - then confirms those changes).
A valid autonomous resolution depends on:
- Grounding the reply in authoritative content.
- Passing policy checks.
- Executing auditable actions.
- Confirming success to the customer or recording an explicit escalation.
Scope for autonomous resolution
Autonomous resolutions apply for:
- Frontline support: e.g. order status, refunds, cancellations, subscription changes, shipping queries.
- Transactional workflows: e.g. creating or changing orders, issuing credits.
- Complex, multi-step business processes: where the agent must call multiple systems, gather approvals, and coordinate steps.
- High-volume informational requests that can be closed via an answer.
These are the concrete targets for automation only when the organization can meet governance, testing and auditability requirements.
Evaluation & metrics
Primary metrics
- Resolution rate — % of conversations that the agent completes without human intervention and that meet the system’s resolution criteria (hard or soft). See resolution definition for how Fin calculates resolution rate. <link to our resolution definition>
- Involvement rate — % of conversations where the agent attempted to answer
- Escalation & handover quality — frequency and effectiveness of handoffs when needed
Safety, governance & preconditions
Before enabling autonomous resolutions at scale, organizations must implement:
- Policy checks for every state change.
- Audit logs for every decision and write.
- Simulation coverage and regression tests pre-deployment and for continuous improvements.
- Approval/override methods for human fail-safes for high-risk actions.
- Metrics & monitoring for insights, improvement recommendations and business KPIs.
Common use cases
- Refunds & billing adjustments when eligibility checks and caps can be enforced
- Subscription changes (pause, cancel, plan change) requiring reads/writes across billing and CRM.
- Order modifications and shipping updates that require system writes and confirmation.
- Complex triage and resolution workflows where the AI agent performs multiple steps and coordinates with external systems.
FAQ
Q: What’s the difference between an autonomous resolution and an automated suggestion?
A: An automated suggestion proposes an action for a human; an autonomous resolution executes the action (with policy checks) and closes the issue without human intervention. Autonomous resolutions require writes, audit trails, and guardrails.
Q: How do you define a “hard” vs “soft” autonomous resolution?
A: Hard resolution usually requires explicit customer confirmation that the issue is resolved; soft resolution is recorded when the customer exits the conversation without asking for more help.
Q: Are autonomous resolutions always desirable?
A: Not always. Only enable where the business can guide on policy enforcement, auditability and measurable positive business impact; start with low-risk tasks and scale as governance and testing mature.
7. Related definitions
- AI Agent — the system that executes autonomous resolutions.
- Guardrails / Policy Engine
- Simulations