Autonomous Resolution — Definition

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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: 

  1. Grounding the reply in authoritative content.

  2. Passing policy checks.

  3. Executing auditable actions.

  4. 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.

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