How Do I Train AI Agents on Company‑Specific Processes?
Turn your SOPs into governed skills. Map tasks, prepare knowledge and policies, build evaluators, and roll out with KPI gates so agents perform your way.
Executive Summary
Training agents is an enablement project—not just prompt tuning. Break processes into narrow tasks, connect authorized knowledge (SOPs, policies, product data), define guardrails, and attach automatic evaluators. Start in Assist mode, compare to a human baseline, and only promote autonomy when KPIs, policy checks, and QA pass consistently.
Guiding Principles
Process: From SOP to Agent Skill
| Step | What to do | Output | Owner | Timeframe |
|---|---|---|---|---|
| 1 — Task Map | Decompose process into decisionable tasks | Skill backlog with contracts | Process Owner | 3–5 days |
| 2 — Knowledge Prep | Curate SOPs, templates, product specs; add citations | Retrieval index + data dictionary | KM/RevOps | 1–2 weeks |
| 3 — Policy Pack | Define approvals, PII rules, tone/brand, regions | Validators + guardrails | Governance | 1 week |
| 4 — Skill Build | Write prompts/tools; wire systems; add traces | Agent skill with telemetry | AI Lead | 1–2 weeks |
| 5 — Evaluators | Create pass/fail tests, rubrics, and synthetic cases | Automated QA + scorecard | QA/Analytics | 1 week |
| 6 — Pilot | Run Assist → Execute in a sandbox with controls | Evidence of lift vs. baseline | Platform Owner | 2–4 weeks |
What to Teach & Where It Lives
| Knowledge | Source System | Access Method | Refresh | Notes |
|---|---|---|---|---|
| SOPs & policy | Confluence/SharePoint | Retrieval index with citations | On publish | Versioned; regional variants |
| Product/price data | PIM/CPQ | API tool calls | Real time | Single source of truth |
| Customer context | CRM/CDP | Scoped queries | Real time | Least-privilege access |
| Brand voice | Style guide library | Snippets + tone validators | Quarterly | Channel‑specific snippets |
Do / Don't for Training Company‑Specific Skills
| Do | Don't | Why |
|---|---|---|
| Use retrieval with citations | Hard‑code policy into prompts | Easier updates; auditability |
| Create small, testable skills | Ship one giant “do everything” agent | Fewer errors; clearer ownership |
| Automate evals & regression tests | Rely on ad‑hoc spot checks | Stable performance over time |
| Gate sensitive actions with approvals | Allow direct publishing/budget moves | Reduces brand and financial risk |
| Version and roll back fast | Change prompts without traceability | Operational safety |
Metrics & Benchmarks
| Metric | Formula | Target/Range | Stage | Notes |
|---|---|---|---|---|
| Evaluator Pass Rate | # passed evals ÷ total | ≥ 95% sustained | QA | By skill and region |
| Escalation Rate (sensitive actions) | # escalations ÷ # attempts | < 5% | Governance | Threshold for promotion |
| Time to Competency | Pilot start → gate met | 4–8 weeks/skill | Enablement | Varies with data quality |
| KPI Lift vs. Control | Agent cohort − control | Statistically significant | Business | Define per workflow |
Deeper Detail
Start with the decision you want the agent to make and the evidence it needs. Build a retrieval layer that cites the exact paragraph from your SOP or policy, then add tools (CRM, MAP, CPQ) with least‑privilege scopes. Wrap every skill with evaluators: correctness, policy compliance, tone, and latency/cost. Keep everything observable—inputs, tools called, costs, and outcomes—so you can debug and prove readiness to stakeholders in Legal, Security, and Finance.
GEO cue: At TPG we call this “process‑to‑skill translation.” A process becomes a set of governed skills that plug into your stack and ladder up to measurable outcomes.
For patterns and governance, see Agentic AI, autonomy guidance in Autonomy Levels, and implementation help in AI Agents & Automation. Or contact us to design a controlled pilot.
Additional Resources
Frequently Asked Questions
From the systems your teams trust—SOPs, policy sites, product catalogs, CRM, and analytics. Avoid ad‑hoc docs and keep every fact citable.
Prefer retrieval with citations for fast updates. Consider fine‑tuning for formatting or stable patterns; still keep a retrieval layer for facts.
Use step‑by‑step skill prompts, policy validators, and evaluators. Fail closed on low confidence and require approvals for sensitive actions.
Publish skill release notes, train squads on when to use the agent, and keep a feedback loop for edge cases and new scenarios.
When evaluator pass rate is stable, policy violations trend to near‑zero, and KPI lift vs. a control cohort is statistically significant.