How Do I Train AI Agents on Company‑Specific Processes?

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

Teach tasks, not topics—make skills small and testable

Prefer retrieval over memorization; keep sources authoritative

Add policy validators before and after tool calls

Version prompts, datasets, and skills with change logs

Measure on one revenue/experience scorecard

Treat every skill like a product: owner, contract (inputs/outputs), SLOs, tests, and release notes.

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

Agentic AI Overview Autonomy Levels for Marketing AI Agents AI Agents & Automation Contact TPG

Frequently Asked Questions

Where should training data come from?

From the systems your teams trust—SOPs, policy sites, product catalogs, CRM, and analytics. Avoid ad‑hoc docs and keep every fact citable.

Fine‑tune or use retrieval?

Prefer retrieval with citations for fast updates. Consider fine‑tuning for formatting or stable patterns; still keep a retrieval layer for facts.

How do we keep agents from drifting off process?

Use step‑by‑step skill prompts, policy validators, and evaluators. Fail closed on low confidence and require approvals for sensitive actions.

What about change management?

Publish skill release notes, train squads on when to use the agent, and keep a feedback loop for edge cases and new scenarios.

How do we know it’s ready for production?

When evaluator pass rate is stable, policy violations trend to near‑zero, and KPI lift vs. a control cohort is statistically significant.