How to Design an AI Agent Workflow

An AI agent workflow is a design artifact.

It should be clear enough for product, engineering, security, and operations to argue about the same thing.

If only the person who wrote the prompt understands it, the workflow is not ready.

Step 1: Name the outcome#

Do not start with "build an agent."

Start with the outcome:

• Triage engineering requests.
• Draft support replies.
• Prepare incident packets.
• Qualify inbound leads.
• Review pull requests.
• Monitor billing issues.
• Collect audit evidence.

The more concrete the outcome, the better the workflow.

Step 2: Define the trigger#

What starts the run?

• Chat message.
• Slack mention.
• Webhook.
• Cron schedule.
• New ticket.
• Pull request.
• Alert.
• Manual button.

The trigger tells you what context exists at the start and what permissions apply.

Step 3: Split the work#

Use specialists only when they make the workflow clearer.

Example:

• Intake Router.
• Context Scout.
• Planner.
• Policy Auditor.
• Human Approval.
• Executor.

Do not create five agents because five looks impressive. Create agents when responsibilities need names.

Step 4: Map tools and resources#

List what the workflow can read and do.

Separate:

• Tools that act.
• Resources that provide context.
• Prompts that package reusable instructions.
• Skills that define operating behavior.

This is where MCP often fits.

Step 5: Add model routing#

Not every step needs the best model.

Use smaller models for:

• Classification.
• Formatting.
• Simple extraction.
• Cheap retries.

Use stronger models for:

• Planning.
• Complex synthesis.
• Risky reasoning.
• Ambiguous handoffs.

Routing is how you keep quality and cost from fighting each other blindly.

Step 6: Put approvals in the workflow#

Approval is not a feature you bolt on later.

Mark every step:

• Can run automatically.
• Needs human review.
• Needs explicit approval.
• Not allowed.

This is especially important for billing, production, customer messaging, and data access.

Step 7: Define evals before launch#

Write eval cases before the workflow ships:

• Happy path.
• Missing data.
• Wrong user scope.
• Tool timeout.
• Prompt injection.
• Low confidence.
• Approval required.
• Bad source.

If you cannot test the workflow, you cannot trust it.

Step 8: Turn it into architecture#

The architecture should fall out of the workflow:

• Trigger routes.
• Orchestrator.
• Queue.
• Tool layer.
• MCP servers.
• Memory store.
• Approval UI.
• Audit log.
• Eval harness.
• Observability.
• Deployment.

That is the reason to design the workflow first.

Build it in Codelit#

Try this:

Design an AI agent workflow from scratch for a SaaS internal operations agent. Include trigger, outcome, specialist agents, tools, resources, Skills, MCP servers, model routing, memory, approvals, evals, and production architecture.

Design the AI agent workflow

The workflow is where the agent becomes a system.