How to Build Agentic Workflows That Actually Work

Everyone's talking about AI agents. Few people are building ones that actually work.

The difference between an agent that saves you 10 hours a week and one that creates 10 hours of cleanup? A framework. Not a fancy one. A simple, repeatable structure that turns a vague idea into a reliable system.

I teach this six-part framework:

The Six Parts of Every Agentic Workflow

1. Task. Start with a goal, not a chore. Don't tell your agent "check my email." Tell it "manage my inbox, keep it at inbox zero, flag anything urgent, follow up on important threads, and use your judgment." The more context you give, the better the output. Think of it like onboarding a new team member. You wouldn't just say "do stuff." You'd explain the mission.

2. Tools. What does the agent need access to? Email? Your CRM? A calendar? A Slack channel? These are the connectors, APIs, and context sources your agent plugs into. No tools, no results.

3. Trigger. What starts the process? Maybe it runs every morning at 8 AM. Maybe you kick it off with a message. Maybe an incoming event fires it automatically. Every workflow needs a clear starting gun.

4. Input. What does the agent receive when it starts? In the inbox example, the input is your messy, unchecked email. Every agentic process takes something in and transforms it into something better.

5. Steps. This is where most people get lazy, and where most agents fail. Write out your process step by step. Every decision point. Every "if this, then that." Start with a standard operating procedure. If you can't write it down clearly, an agent definitely can't follow it.

6. Output. What does the finished job look like? Define "done." What does a well-managed inbox look like at the end? What report gets generated? What message gets sent? If you can't describe success, your agent can't deliver it.

The Biggest Mistake: Automating Too Early

Here's where companies blow it. They hear "AI agent" and immediately try to automate everything. No documentation. No testing. No refinement. Just vibes and a prompt.

That's like hiring someone on Monday and leaving for vacation on Tuesday.

Here's the path I recommend:

1. Start with a manual process. Do it yourself.
2. Document every step.
3. Have someone else follow your documentation.
4. Watch where they get stuck. Refine the process.
5. Semi-automate it. Build a custom GPT or a simple tool.
6. Test that version for a while. Iron out the kinks.
7. Now fully automate. Build the agent.
8. Keep refining. Train it. Don't set it and forget it.

Most businesses skip straight to step seven and wonder why their agents don't work. The process has to be battle-tested by humans before you hand it to a machine.

Why This Matters Right Now

AI agents aren't coming. They're here. The question isn't whether you'll use them. It's whether you'll use them well.

The companies winning with AI right now aren't the ones with the fanciest models. They're the ones with the clearest processes. Good inputs. Clear steps. Defined outputs. That's it.

Simple wins. Every time.

"The secret of getting ahead is getting started." — Mark Twain

If you want to go deeper on building AI systems that actually do the work, my new book DONE: Let AI Do Your Work So You Can Live Your Life launches May 20th. It's the playbook for turning AI from a novelty into a workhorse.