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Customer supportEnrich and routeTeam

Bug-to-customer impact mapper

Connect open bugs with affected accounts, support evidence, recurrence, and delivery risk before triage.

What this workflow does

Connect open bugs with affected accounts, support evidence, recurrence, and delivery risk before triage. It resolves identity, enriches the case with approved evidence, and shows why the proposed route is appropriate before anything is assigned.

What you gain

The team fixes issues using customer evidence instead of volume or intuition alone.

What the AI agent changes

How this worked before

Rule-based routing handled only clean, predefined fields and broke when the request was incomplete or written in natural language.

What the AI agent changes

The agent can interpret unstructured context, gather missing evidence, explain its recommendation, and pause when confidence is low.

Agent trigger

A new customer-facing bug appears or the scheduled triage starts.

Inputs

  • Process context: bug reports, support cases, affected accounts, product telemetry, recurrence, and delivery commitments
  • Approved policies, ownership, and exception rules

Agent flow

  1. 1

    Resolve the case identity and validate source freshness.

  2. 2

    Enrich the case with approved context and show every scoring signal.

  3. 3

    Prepare an evidence-backed impact rank, affected segment, confidence, and accountable owner with confidence and exceptions.

  4. 4

    Create the approved assignment and notify its owner.

Human decisions

After step 3

The accountable process owner approves an evidence-backed impact rank, affected segment, confidence, and accountable owner.

Outcome

  • Reviewed customer-impact bug board
  • Evidence, exceptions, and audit trail

Guardrails

  • Do not route low-confidence identity matches automatically.
  • Use an allowlist of destinations and writable fields.

Risks and mitigations

A wrong identity match can send a case to the wrong owner.

Require deterministic identifiers and expose the routing rationale.

Sources and evidence

Related AI agent workflows