VI – The Non‑Technical Builder Mindset

A common trap is jumping straight into tools. Instead, think like a process engineer. The same applies here:

• Think in steps, not tools: Write out the workflow on paper before choosing an agent.
• One agent per real problem: Instead of spinning up ten agents, start with one to handle your weekly newsletter or a specific research flow.
• Use templates: Start from existing agent templates (most platforms offer them — Relevance AI, Notion, Genspark, Lindy, Lovable) and tweak roles, instructions and inputs.
• Make it boringly reliable: Simple, narrow agents outperform clever, fragile ones. Tighten context and scope if they break.

<aside> ⚠️

Common mistakes to avoid:

• Giving vague instructions and expecting the agent to "figure it out"
• Skipping the workflow mapping step and jumping straight into tools
• Running too many agents at once before any of them are reliable
• Expecting 10x results in week one (it compounds over time, not overnight) </aside>

VII – Building & Refining Your 2026 Stack

Here's the part where most people mess up: they try to build five agents on day one.

I know because I did it. It's chaos.

Half of them break, you forget what each one does, and suddenly you're spending more time fixing agents than doing actual work.

Don't do that. Build slow, build boring, and let results compound.

Phase 1 – Solve ONE problem first (Week 1)

Go back to Section V.

Pick the job category that hurts most — Research, Content, Planning, or Documentation.

Now pick ONE agent type that fits. If you hate research rabbit holes, start with a Deep Research agent. If content drafts drain you, try a Workspace agent like Notion AI.

Set it up. Run three test cases (I call these "golden tests"). If it passes, you've got your first reliable teammate. If it fails, tighten the context and try again. Don't move on until this one agent is boringly reliable.

<aside> 🧪

What's a "golden test"?

A golden test is a real task you've done before where you already know what a good output looks like. You give the agent the same input and compare its output to your benchmark. If it matches (or beats) your standard, it passes. If not, you tighten the prompt, add more context, and run it again.

Three passing golden tests = a reliable agent.

</aside>

Phase 2 – Run a real workflow (Week 2)

Now put it to work on something that matters.