The 3-Phase System for Building Your First AI Agent (Without Coding)

Jul 9

5 min read

Building your first AI agent can feel overwhelming. Where do you start? What tools do you need? How do you even know if what you're building will work?

Chase Hannegan of Chase AI, a former Marine Corps pilot who transitioned into AI development, has cracked the code on making AI accessible to non-technical people. His secret? A simple 3-phase system that breaks down any AI project into manageable pieces:

Planning, Execution, and Fine-tuning.

This isn't just theory—it's the exact framework he uses with clients and teaches in his community. Here's how it works.

Phase 1: Planning (Measure 5,000 Times)

The biggest mistake people make with AI projects? Jumping straight into building without defining what success actually looks like.

"You need to define success and you need to define it explicitly," Chase emphasizes. "I think a great way to do that is borrowing stuff they do in tech—come up with a PRD, like a product requirements document, even if it's not a product."

The PRD Approach

Your planning document should include:

End State Definition: What does the finished product look like? Be specific. Instead of "an AI that helps with email," write "an AI agent that automatically sorts incoming sales emails, drafts personalized responses based on the inquiry type, and flags urgent requests for immediate attention."

Feature Breakdown: Break your end state into distinct features:

Feature A: Email classification by type (sales, support, partnership, etc.)
Feature B: Automated response drafting based on email category
Feature C: Urgency detection and flagging system

Sub-features: Each main feature gets broken down further:

Feature A1: Connect to email inbox
Feature A2: Analyze email content for keywords
Feature A3: Apply appropriate tags

Why This Matters

This breakdown serves a crucial purpose: you're going to attack this project in different blocks. By planning thoroughly upfront, you've already mapped out your execution phases. You can build and test Feature A before moving to Feature B, which dramatically increases your chances of success.

The Research Phase

Once you've defined your solution, don't reinvent the wheel. Chase's next step is always the same: "Going on to something like Perplexity and asking: 'Here's my problem, here's what I want to solve, here's what I brainstormed as a solution. Has anyone else done this?'"

The goal isn't to copy someone's work, but to start from 50% instead of 0%. As Chase puts it: "I don't want to run the whole marathon. Get me to mile 13.1 and I can start from there."

Even if you can't find a perfect match, seeing what others have built gives you inspiration and helps you understand what's possible—especially crucial when you're new to AI development.

Phase 2: Executing you AI Agent (Cut with AI)

Here's where Chase's system gets interesting. Because you've "measured 5,000 times" in the planning phase, you can now "cut with AI" confidently.

The Block-by-Block Approach

Remember those features you defined in planning? Now you build them one at a time:

Start with Feature A only
Build it completely
Test it thoroughly
Make sure it works before moving on
Then tackle Feature B

"If you just try to do it all as one big project, good luck. It's gonna fail," Chase warns. "Especially if you think AI's gonna do this all for you, which it can, but you have to really steer it and guide it."

The AI-as-Intern Mindset

Chase has a perfect analogy for working with AI during execution: treat it like a "genius idiot intern."

"It knows way more than you do, but it's also really dumb. It doesn't know how to use its brain power. You need to steer it, you need to give it blinders, and you need to direct it on the right path."

In practice, this means:

Give maximum context: Drop files, code, screenshots—anything relevant
Be specific about what you want: Don't say "fix this," say "this email sorting isn't working—it's categorizing partnership emails as sales emails"
Provide examples: Show the AI what good output looks like
Test each piece: Don't assume something works just because AI built it

Phase 3: Fine-tuning (Troubleshoot Like a Pro) your AI Agent

The fine-tuning phase is really about systematic troubleshooting. And here's Chase's not-so-secret weapon: AI itself.

When his clients hit problems, they often run straight to him for help. His response? "You know what I'm gonna do with your question, right? I'm just gonna go to AI and I'm gonna ask it probably better than you will."

The Art of AI-Powered Troubleshooting

Effective troubleshooting with AI requires what Chase calls "somewhat of a skill":

Context Setting:

"Here's what I want to do..."
"Here's where I'm at..."
"Here are my problems..."

Specific Problem Description: Instead of: "My email agent isn't working" Try: "My N8N email agent successfully connects to Gmail and receives new emails, but the OpenAI node is returning an error 'insufficient context' when trying to classify the emails. I'm using GPT-4 with a 500-character system prompt that says..."

Include Everything Relevant:

Screenshots of error messages
Your system prompts
Sample data that's causing issues
What you expected vs. what happened

The Cycle Continues

The beauty of Chase's 3-phase system is that it's cyclical. Once you complete fine-tuning on Feature A, you go back to planning for Feature B. Each cycle teaches you more about what's possible and refines your approach.

"It's just a cycle," Chase explains. "What's success? Break it down into pieces, look for other examples, and then troubleshoot along the way."

Real-World Application: The Personal Assistant

Let's see how this system works with Chase's recommended first project—a personal assistant AI agent:

Phase 1 - Planning:

End state: An AI that can send emails, check my calendar, and research topics
Feature A: Email sending capability
Feature B: Calendar integration
Feature C: Internet research tool
Research: Find existing personal assistant tutorials

Phase 2 - Execution:

Build Feature A only (email sending)
Test with simple "send email to [contact] saying [message]"
Confirm it works reliably
Move to Feature B (calendar)
Test calendar checking independently
Integrate with existing email feature
Continue building...

Phase 3 - Fine-tuning:

Email agent sometimes sends to wrong contact → investigate contact database integration
Calendar tool shows wrong timezone → adjust timezone settings in system prompt
Research function returns irrelevant results → refine search parameters

Why This System Works

Chase's 3-phase approach succeeds because it addresses the three main failure points of AI projects:

Unclear objectives (solved by detailed planning)
Overwhelming complexity (solved by block-by-block execution)
Getting stuck on problems (solved by systematic troubleshooting)

Most importantly, it gives non-technical people a clear roadmap for building something that actually works.

Your Next Steps

Ready to apply this system? Start here:

Pick your simplest AI project idea
Write a one-page PRD defining exactly what success looks like
Research similar projects for inspiration and starting points
Build just the first feature
Test it thoroughly before adding anything else

Remember: the goal isn't to build the perfect AI agent on your first try. The goal is to build something that works, learn from the process, and iterate from there.

As Chase reminds us: "You have to be willing to suck at something for a while." But with the right system, that learning curve gets a lot less steep.

What's your first feature going to be?