Context
The client operated a complex service workflow with high request volume, inconsistent handoffs, and frequent exception handling.
Leadership needed a disciplined way to understand which parts of the workflow were appropriate for agentic AI.
Problem
The team had pressure to automate quickly, but the process mixed simple routing decisions with sensitive judgment calls and customer-facing commitments.
Workflow
The diagnostic broke the workflow into intake, classification, enrichment, recommendation, approval, execution, and follow-up loops.
- Agent-assisted intake triage.
- Evidence gathering from approved systems.
- Human approval for customer-facing commitments.
01
Triage confidence
Intake and classify
Requests are captured, normalized, and classified by urgency, customer impact, required evidence, and likely resolution path.
02
Evidence coverage
Gather operating evidence
The agent retrieves relevant policy, account history, service notes, and prior resolutions before proposing next steps.
03
Reviewer acceptance
Recommend action
Recommendations include rationale, source references, confidence indicators, and the approval path required for the task.
04
Exception quality
Route and learn
Approved actions move forward while exceptions create feedback for process changes, evaluation cases, and operating rules.
Architecture
The proposed system used a workflow orchestrator, task-specific agents, retrieval from operating knowledge, and explicit handoff states for human reviewers.
Task-specific agents
Separate agent roles support classification, evidence gathering, recommendation drafting, and follow-up rather than one broad autonomous agent.
- Classifier
- Research agent
- Recommendation agent
Human review layer
Human reviewers receive the recommendation, supporting evidence, and confidence state before external commitments are made.
- Approval queue
- Override reasons
- Reviewer notes
Feedback loop
Rejected recommendations, low-confidence cases, and exception outcomes are fed back into evaluation sets and workflow design.
- Rejected cases
- Quality review
- Process updates
Governance
Controls were designed around task criticality, customer impact, and reversibility rather than a single blanket approval rule.
- Low-risk routing could be automated.
- Recommendations required confidence and source evidence.
- External actions required human approval in the first phase.
Metrics
Success metrics focused on cycle time, rework, reviewer load, exception quality, and customer response consistency.
- Workflow steps mapped
- 47
- Automation candidates
- 11
- Cycle-time target
- -30%
Including decision points, edge cases, and manual rework loops.
Tasks suitable for assistive or semi-autonomous agent behavior.
Estimated reduction for the first operational pilot.
Roadmap
The first phase targeted internal recommendations and evidence gathering before expanding into more autonomous execution paths.
Phase 1
Internal recommendations
Deploy evidence gathering and recommendation drafts for internal users without autonomous external action.
Phase 2
Approved execution
Allow approved low-risk actions once acceptance rates, error patterns, and reviewer workload are understood.
Phase 3
Exception expansion
Expand to more complex exception handling after controls, evaluation coverage, and escalation paths prove stable.
Reflection
The diagnostic made the workflow smaller and more legible. That clarity mattered more than choosing an agent framework early.
Technical depth
System assumptions and operating controls.
Architecture diagram
The diagnostic architecture keeps the agent loop inside a governed workflow shell: classify work, gather evidence, recommend action, and route the decision to a human when impact rises.
01
Request intake
Incoming work is normalized with urgency, customer impact, and required evidence fields.
02
Evidence layer
The agent retrieves policy, account history, service notes, and prior resolution examples.
03
Recommendation layer
A task-specific agent drafts next actions with rationale, confidence, and source references.
04
Review and route
Humans approve, reject, or escalate recommendations before customer-facing action.
Agent loop explanation
Loop 1
Classify
Identify request type, urgency, likely resolution path, and whether the case is reversible.
Loop 2
Retrieve
Collect operating evidence from approved sources and attach it to the recommendation.
Loop 3
Plan
Draft the next best action, confidence state, and required approval path.
Loop 4
Escalate
Route medium and high-impact cases to a reviewer with evidence and override options.
Tool-use table
Tool
Classifier
Purpose
Assign request type, urgency, and reversibility tier.
Input
Request text, metadata, account status
Output
Triage label and confidence
Guardrail
Low confidence routes to manual review.
Tool
Evidence retriever
Purpose
Pull policy, prior cases, and account context for the case.
Input
Request classification and entity identifiers
Output
Cited evidence bundle
Guardrail
Only approved sources can be cited.
Tool
Recommendation drafter
Purpose
Prepare the proposed action and reviewer rationale.
Input
Evidence bundle, policy constraints
Output
Action recommendation
Guardrail
External commitments require human approval.
RAG and data source assumptions
Policy library
Policy owner
Policies are authoritative and include enough detail for request classification.
Prior case history
Service operations
Resolved cases are searchable and representative of current operating practice.
Account records
Customer operations
Customer status and contractual context are accessible to the workflow.
Evaluation metrics
Triage accuracy
85% agreement with reviewers
Compare classifier output against manually labeled request samples.
Evidence usefulness
80% reviewer acceptance
Review whether cited evidence supports the recommended action.
Escalation quality
Less than 10% missed escalations
Audit medium and high-risk cases for correct review routing.
Failure modes
Confident wrong triage
A request enters the wrong workflow path and receives an unsuitable recommendation.
Set confidence thresholds and sample low-frequency request types.
Unsupported recommendation
Reviewers lose trust because rationale is not grounded in source evidence.
Require cited evidence and block recommendations with weak retrieval coverage.
Escalation bypass
Customer-impacting actions move forward without proper review.
Tie escalation to impact tier and reversibility, not model confidence alone.
Human-in-the-loop checkpoints
Triage exception
Operations reviewer
Confirm unclear classifications before recommendation drafting.
External action approval
Service lead
Approve or edit customer-facing commitments.
Weekly quality review
Workflow owner
Update rules, evaluation cases, and escalation thresholds.