AI Agents for Workflow Automation: 6 Patterns That Keep Work Governed

AI agents for workflow automation work best when the model handles judgment and code handles everything else. The agent reads the messy request, decides what it means, and hands the known steps to a deterministic app that validates, routes, and writes records the same way every time.

That split matters because a model that re-reads the same policy on every run costs more tokens each time and can answer differently each time. Major is built around the fix: when an agent works out a repeatable part of a process, it builds an app for that part, with its own database, scoped permissions, and logs. Reason once, run forever. If you are an ops, RevOps, support, or IT lead trying to get agents into real processes without losing control, these six patterns are the place to start.

Where agents belong in a workflow

Trigger-and-action automation handles the clean path well. A form arrives, a record is created, a message goes out. It breaks on messy input: a PDF with the wrong tax ID format, a ticket that mixes two problems, a payment that almost matches an invoice.

Those cases are model work. Everything around them is not. Schema checks, routing tables, duplicate lookups, and record updates have fixed answers, so they should run as code. The agent layer decides and builds. The app layer executes and remembers. For more on keeping state out of the context window, see durable state for agents.

The patterns below are design patterns, not deployment reports. The connectors you use depend on what your organization has connected.

Six AI agent workflow automation patterns

Intake and request routing

A form or shared inbox request arrives. The app checks required fields, writes a request record with an idempotency key, and routes by lookup table. The model only steps in when a request fits no category or fits two.

The common failure is letting the model pick the route every time. The same request then lands in different queues on different days. Keep the routing table in the app.

Vendor onboarding

The app verifies required fields, dedupes by tax or business ID, and updates the ERP or CRM record. The agent reads uploaded documents and flags conflicting terms.

Identity is the hard part. An exact ID match is code. Two submissions with slightly different legal names could be one vendor or two, and that fuzzy call goes to a person.

Customer-support triage

Account lookup and tier-based routing are deterministic. Reading the customer's actual problem and drafting a reply are model work. A human sends anything touching refunds, legal, or security language.

Watch for stale policy. If routing rules live in a prompt, nobody can diff them. In an app, a rule change is a reviewable edit.

Sales lead qualification

Region, company size, and missing-field checks run as code. The agent summarizes free-text notes and flags anything that reads like an existing customer, then the app assigns an owner.

Owner assignment breaks first. Territory rules are usually more tangled than anyone remembers, so write them down as data before the agent touches them.

Finance exception matching

Recurring records match on amount, reference, and counterparty. The unmatched remainder goes to the model, which proposes candidates with its reasoning attached. A person confirms before anything posts, and any write above a set threshold needs approval.

This pattern does not cover multi-currency reconciliation. That needs its own rules.

Engineering issue triage

Missing labels and ownership routing are code. Summarizing a stack trace and guessing the likely module is model work. Let the agent suggest a severity, but do not let it change a production incident's priority without a human.

Workflow graph or agent-built app

A workflow graph with agent steps is a reasonable way to wire a stable path, but a person builds and maintains the path, and any agent step reasons again on every run. Major takes the other route. The agent writes the repeatable path as code, a person reviews it, and the app runs the known case without calling the model.

• Who builds the path: the agent, reviewed by a person, rather than a person dragging nodes.
• Where state lives: the app's managed database, with history, rather than a run log you must connect to a store.
• Governance: SSO, permissions, and audit apply at the platform layer on every app, not tool by tool.
• Cost over repeated runs: front-loaded at build, then flat as the app handles the known path.

For selection criteria across tools, read AI workflow automation tools. For groundwork, read AI workflow automation.

Build the intake routing agent on Major

Start with intake routing. Inputs are small, the routing table is easy to write down, and a wrong route is cheap to fix. Here is the build.

1. Connectors: connect Gmail or your form tool for intake, your ticketing system for output, and Slack for escalation. Credentials are scoped to the records involved.
2. The app: a requests table in Major's managed database holding the idempotency key, status, assigned queue, and history, plus a routing-rules table and a view for the triage owner.
3. The agent: it classifies only the requests that match zero or two rules, and writes its reasoning to the record. It does not pick routes for known categories.
4. The trigger: a workflow fires on each new message or form submission. Duplicate events hit the idempotency key and do nothing.
5. Approval boundary: unclassified requests post to a Slack channel for the triage owner, who resolves them in the app.
6. Governance: RBAC controls who edits routing rules, and every action by the agent or a person lands in the audit log.

Once the routing logic is stable, the agent stops reasoning through it. Later runs spend model tokens only on the odd cases. Build your first workflow agent on Major and add the next pattern once this one holds.

What to define before you deploy any of them

Pick one workflow that is frequent, bounded, and observable, then settle these before launch.

1. The event source, and what happens on a duplicate event.
2. Required fields and their shape.
3. The idempotency key that stops a retry from writing twice.
4. Credentials limited to the records involved.
5. Where status and history are stored.
6. Retry count, spacing, and what happens after the last one.
7. An audit record of who or what took each action.
8. A named human owner for anything outside the rules.

High-impact steps in finance, HR, and legal keep a human approval, whatever the agent proposes.

Start building on Major

Build the intake routing agent, or any of the six patterns above, as a governed app with its own database, scoped credentials, and audit trail, then let the agent run it instead of reasoning through it again.

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