3 simple AI agent workflows you can set up this week (no code)

Most "AI agent" content is either a framework tutorial or a vague promise that agents will run your company. What I wanted, and couldn't find, was something in between: concrete, boring, useful workflows, organized by the job you actually do.

So I started collecting them. Here are three simple ones that cut down on repetitive work, with the tools and steps laid out so you can copy them.

1. Lead research and personalized first lines (Sales)

The problem: Reps spend hours researching prospects before writing a single outreach email, and the "personalization" often ends up as "I saw you work at {company}."

The workflow:

1. Export a target list from Apollo.io.
2. Import it into Clay and enrich each row with company and website data.
3. Add an AI column that writes a one-line opener grounded in that enrichment data, not invented.
4. Review a sample by hand before anything goes out.

Why it works: The AI only writes from facts you pulled in, so openers stay accurate. The human review step catches the weird ones. Tool costs are modest, and a small batch is easy to test in an afternoon.

2. Support ticket triage and tagging (Support)

The problem: Agents manually read, tag and route every ticket. Urgent issues sit in the same queue as password resets.

The workflow:

1. Trigger on every new Zendesk ticket (Zapier works fine).
2. Send the ticket text to an AI step that classifies category, urgency and language.
3. Write the tags and priority back to the ticket.
4. Route anything marked urgent straight to an on-call group.

Why it works: Classification is something language models do well and cheaply. You aren't letting AI answer customers yet, just sorting the pile, so the downside of a mistake is small. This is an easy first agent project for most support teams.

3. Receipt and invoice capture for freelancers (Personal finance)

The problem: Receipts pile up in your inbox, and tax time turns into a scavenger hunt.

The workflow:

1. Filter receipt emails into a dedicated Gmail label.
2. A Zapier zap sends each one to an AI step that pulls out vendor, date and amount.
3. Save the attachment to a Google Drive folder.
4. Append a row to a Google Sheets expense log, and skim it once a week.

Why it works: The agent does the tedious typing, and you still review the log. That "AI drafts, human checks" pattern is the safest place to start with anything involving money.

The pattern behind all three

Notice what these have in common:

• One narrow job. Not "automate sales," just "write a first line."
• Grounded inputs. The AI works from data you supply, not its imagination.
• A human checkpoint where mistakes would be costly.
• Off-the-shelf tools. No custom models, no infrastructure.

If you're trying to figure out where AI agents fit in your work, start there: pick the most repetitive, lowest-risk step in your week and wrap one of these patterns around it.

Where these come from

I've been collecting workflows like these in a free how-to guide, Roar Workflows, organized by job: sales, support, marketing, ops, personal finance and more. Each one lists the problem, the tools, the steps, and a rough cost and difficulty. It's free and there's no signup.

👉 Browse them here: https://roarworkflows.com

If you've built a workflow that saves you time every week, I'd love to hear about it in the comments. The best ones will get added to the guide (with credit).