Agent hype collapses when workflows are vague. This guide maps a business task to an agent-ready process with boundaries, tools, escalation, and evaluation.
Step 1 — Define the task
Write the business outcome, acceptable error rate, and who signs off—not the model persona.
| Field | Example (RFP triage) |
|---|---|
| Outcome | Route inbound RFPs to the right pod within 4 business hours |
| Error tolerance | Under 2% mis-routes on a held-out set |
| Sign-off | Sales ops manager before auto-actions in v1 |
If you cannot name the approver, the task is not agent-ready.
Step 2 — Map context and tools
List allowed data sources, APIs, and actions. Anything outside the list is out of scope for v1.
See Data Boundaries for AI Agents.
Step 3 — Design handoffs
Escalate to humans when confidence is low, policy keywords fire, or required fields are missing. Define SLAs for human review on high-value accounts.
Step 4 — Evaluate before scale
| Gate | Pass condition |
|---|---|
| Smoke | 10 historical cases classified correctly |
| Pilot | 50 live cases with override logged |
| Scale | Mis-route rate within tolerance for 30 days |
See Evaluation Hooks for AI Workflows.
Worked example: RFP triage
Before: AEs manually skim long RFPs; multi-day delays are common.
After (v1): Agent extracts scope, deadline, mandatory clauses → drafts routing recommendation → human confirms → ticket updated.
| Role | Responsibility |
|---|---|
| Sales ops | Workflow and eval set |
| IT | Integrations and audit logs |
| Legal | Policy context and blocked terms |
| AE | Confirms routing in pilot |
Agent vs automation vs human-only
| Situation | Choose |
|---|---|
| Fixed rules, no language judgment | Traditional automation |
| Language judgment, bounded tools, audit need | Agent workflow |
| High stakes, novel cases | Human with AI assist |