Agent Architecture
Design AI-assisted marketing workflows with clear roles, inputs, and review gates.
AI agents should increase marketing throughput without turning the brand into generic output. The architecture must define what agents can draft, what humans approve, and what evidence every output must use.
Agent Roles
| Agent | Main job | Required input | Human gate |
|---|---|---|---|
| Research agent | Summarize market, customer, and competitor signals | Source list and research question | Source quality review |
| Persona agent | Convert signals into audience hypotheses | Segment, job, pains, triggers | Strategy review |
| Content agent | Draft outlines, posts, emails, and landing copy | Brief, voice rules, proof points | Editorial approval |
| Performance agent | Suggest creative tests and budget moves | Campaign data and constraints | Spend approval |
| Analytics agent | Explain KPI movement and anomalies | Dashboard export and context | Decision review |
Workflow Pattern
Operating Rules
- Agents never invent customer evidence, case studies, metrics, or legal claims.
- Every draft includes the source, target persona, funnel stage, and intended action.
- Brand voice and forbidden claims live in a shared context pack.
- High-risk channels such as paid ads, pricing pages, and legal-sensitive claims require explicit approval.
- Agent output is measured by decision speed and asset quality, not raw content volume.
Useful Artifacts
| Artifact | Owner | Update cadence |
|---|---|---|
| Brand context pack | Marketing lead | Monthly or after positioning change |
| Prompt library | Channel owner | Weekly during campaign cycles |
| Approved claim bank | Marketing and sales | After each new proof asset |
| Review rubric | Editorial owner | Quarterly |