Reference architectures

Internal AI agent examples

These are the kinds of systems I think are worth building first. They are narrow enough to evaluate, valuable enough to matter, and shaped around a real workflow instead of a vague promise of autonomy.

Support prep agent

Improves support replies by preparing a draft, relevant account context, and a likely next action before a human sends anything.

Trigger: A new ticket arrives or an existing ticket is escalated.

Context: Ticket history, CRM/account state, internal docs, prior product usage signals.

Output: Issue summary, response draft, escalation cue, confidence signal.

Review: Support rep edits or approves before sending.

Why it works: Great first bet when the team repeatedly rebuilds the same context by hand.

Internal research agent

Creates a cited brief from docs, notes, transcripts, and links so operators are not starting from a blank page.

Trigger: A team member asks a repeated internal question or needs a prep brief before a meeting or decision.

Context: Docs, transcripts, notes, wiki pages, uploaded files, URL content.

Output: Structured brief, source citations, open questions, recommended next step.

Review: Operator checks synthesis before acting on it.

Why it works: Good when the value is in saving prep time and making context assembly more consistent.

Account briefing agent

Assembles the current state of a customer account before calls, renewals, and escalations.

Trigger: A renewal, high-risk account event, QBR, or customer escalation.

Context: CRM, support history, billing signals, usage data, open issues.

Output: Meeting brief, risk flags, likely talking points, suggested next actions.

Review: Account owner or CSM reviews before using it externally.

Why it works: Works well when context is spread across systems and the team wastes time gathering it repeatedly.

Content workflow agent

Turns a brief and source pack into a structured first draft plus a checklist for production.

Trigger: A new content brief is approved.

Context: Brief, notes, source links, previous content, brand constraints.

Output: Outline, draft, open questions, production checklist.

Review: Editor or marketer reviews and tightens before publication.

Why it works: Useful when the team wants a stronger first pass, not hands-off publishing.

Internal knowledge copilot

Helps a team query internal knowledge safely without exposing raw model behavior as the whole UX.

Trigger: A user asks a repeated internal how-do-I or where-is-this question.

Context: Docs, policies, SOPs, help center, internal notes, permissions-aware data.

Output: Answer, cited sources, missing-info flags, suggested follow-up actions.

Review: Usually light review, with escalation for low-confidence cases.

Why it works: Best when permissions, source quality, and retrieval freshness are taken seriously.

Agent-enabled product workflow

Adds structured AI help inside a customer-facing product while keeping application rules and bounded actions in control.

Trigger: A user initiates a specific product task.

Context: Product state, user context, account data, docs, internal APIs.

Output: Recommendation, draft, plan, or queued action request.

Review: User confirms or app rules gate the action.

Why it works: Best when the workflow is narrow enough to evaluate and the fallback path is clear.

How I choose the first workflow

Pick the workflow with repeated drag, not the most fashionable AI idea.

Choose a use case where someone can judge output quality fast.

Keep the first version narrow enough that failure modes are visible.

Prefer systems that prepare work before systems that act on their own.

The shared pattern

The workflow already exists.

The context can be assembled deliberately.

The output shape is known in advance.

A human can review the result quickly.

The first version assists before it tries to act independently.

Want help choosing one of these patterns?

Send me the workflow you are considering and I'll tell you whether it looks like a strong first internal agent, an AI product feature, or something that should stay a manual process for now.