How to approach AI agents in 2026 (with one example end-to-end)
The best way to build agents is to treat them like a virtual employee: define the job, set boundaries, give safe access, run probation, measure outcomes, then promote slowly.
The Focus is on outcome as agentic solution.
We’ll walk through an example: a Sourcing Agent inside a procurement department.
The continuous example: the Sourcing Agent (Procurement)
What it does (v1): turns a sourcing request into a ready-to-run Sourcing Pack (RFQ draft + supplier shortlist + evaluation matrix + risk checklist).
What it doesn’t do (yet): award suppliers, create POs, commit spend, or change supplier master data.
Step 1 — Pick one closed-loop, low-risk use case
Don’t start with “run procurement end-to-end.” Start with one loop that has clear inputs/outputs.
✅ Example (closed-loop v1): “Sourcing Pack Builder”
- Input: intake form (category, specs, location, budget range, timeline, required certifications)
- Output: Sourcing Pack (RFQ draft, shortlist, evaluation matrix, risk/compliance checklist)
- Done criteria: procurement manager can launch sourcing with minimal edits
Role Card (make it real)
- Name: Sourcing Agent
- Job: prepare Sourcing Packs
- KPIs: time-to-first-pack, pack completeness score, approval rate
- Escalation: unclear specs / new supplier / policy exception → human review
- Must NOT do: award/approve spend/PO creation
Step 2 — Validate data & integration readiness (GO / NO-GO)
Before the agent “starts work,” set up the desk and systems.
✅ Example: what the Sourcing Agent needs access to (read-only first)
- Supplier master + supplier performance notes
- Category spend history (e.g. last 6–12 months)
- Approved supplier lists + policy thresholds
- RFQ templates + standard clauses
- Risk/compliance checklist rules
GO / NO-GO checks (simple)
- Can it reliably fetch the right suppliers for this category + region?
- Are templates and policies accessible and up to date?
- Can you run a full test from intake → draft pack in a sandbox?
If any check fails: stop and fix prerequisites.
Step 3 — Run a supervised pilot (read-only / human approval)
Now you let the Sourcing Agent operate—like a new hire on probation.
✅ Example: pilot workflow
- Buyer submits intake
- Sourcing Agent generates a Sourcing Pack
- Manager approves / edits
- Feedback is captured (what was wrong, what was missing)
Pilot metrics
- Speed: time from intake → first pack
- Quality: % packs approved with minor edits
- Safety: # policy issues caught before approval
Step 4 — Define the outcome as the agentic solution (measure outputs, not clicks)
This is the key mindset shift: your “agentic solution” is not the model, tools, or prompts—it’s the completed outcome.
✅ Example: outcome units for the Sourcing Agent
- 1 Sourcing Pack Delivered (RFQ + shortlist + matrix + risk checklist)
- 1 Supplier Shortlist Approved
- 1 RFQ Ready to Launch (still requiring approval)
Why this matters
- It keeps incentives aligned (“deliver approved packs”)
- It makes value obvious (time saved, fewer cycles, higher compliance)
- It makes scaling easier (you can forecast “packs per week”)
Step 5 — Harden AgentOps + governance before write access (promotion)
Only after the pilot is stable do you “promote” the Sourcing Agent.
✅ Example: promotion path
- Read-only: drafts only
- Approval mode: pre-fill SRM/RFQ event after approval
- Limited write: allowed for low-risk categories under a threshold
- Expanded scope: more categories, more autonomy, still governed
Controls to add before write access
- Audit log (inputs used, sources, decisions, approvals)
- Guardrails (policy checks, supplier eligibility checks, threshold checks)
- Observability (quality, cost, failure modes, overrides)
- Kill switch + rollback process
Wrap-up: the 2026 playbook in one sentence
Build agents like virtual employees: role → desk access → probation → outcome-based performance → promotion with controls.
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