[UPDATE] Day 18 building my AI agent — I finally got out of the chaos

[UPDATE] Day 18 building my AI agent — I finally got out of the chaos

link to previous post:
Day 16 building my AI agent — this one isn’t pretty

Quick recap:
I’m building a personal AI agent to help with procrastination — something that doesn’t just remind you, but actually keeps you accountable.

Last update, I was completely lost.

I spent ~6 days stuck in architecture, overthinking everything, not knowing what to build next.

It was messy.

The last 2 days though… something clicked.

And ironically, what got me out of it wasn’t more AI.

It was a mind map and pure logic.

I literally sat down and mapped:

• what I want the system to do
• what’s actually possible to build
• and how information should flow

That changed everything.

Where I landed:

I’m no longer relying on “just agents” to figure things out.

Now there’s a decision engine (pure code) at the core.

How it works (simplified):

Every input — whether it’s:

• something the user says
• or an autonomous action (like a reminder, insight, or nudge)

Goes through the same pipeline.

At the center of it, there are 2 things:

→ Context
→ User State

User State is something I introduced that changed the game.

It’s basically a structured representation of the user:

• habits
• past behavior
• failures
• consistency

And it generates a kind of “maturity score” (0–100).

So the system knows:

Is this someone who needs:

• more pressure?
• less friction?
• simpler actions?

Then the decision engine uses:

Context + User State → decides what should happen

This can result in:

• a response
• a reminder
• a nudge
• a suggestion
• or no action at all

“But what about AI?”

Still there — but its role changed.

Before: it was trying to decide everything
Now: it translates intent

Example:

If the user says:

“I have a consultation Friday”

A rigid system wouldn’t understand that.

Now the AI interprets the intent and converts it into a structured action.

Then the decision engine executes it reliably.

This solved a big problem:

→ AI hallucination / inconsistency

Now:

• AI doesn’t decide outcomes
• It just translates

And the system becomes way more predictable.

What about agents?

Still exist.

But now they’re specialists, not the brain.

• workout agent
• running agent
• (future) nutrition, hydration, etc.

Each one handles its own domain, with its own logic and improvements over time.

Another thing I added:

Context collection (without friction)

Because users don’t know what the system can do.

So now, while using it, small pop-ups appear with quick questions like:

“What are your main activities?”

• gym
• running
• swimming
• cycling

This feeds the system without requiring the user to “set everything up”.

Why this matters:

Before, the system was inconsistent.

Sometimes it worked really well.
Sometimes it completely failed.

Now the base is… stable.

Not perfect.

But predictable.

And that’s a huge step.

Where I am now:

• Core architecture feels solid
• Simple use cases already work well
• Specialized agents are the next focus

Now it’s about polishing each domain:

• gym
• running
• hydration
• nutrition

Because each one is a different world.

Next step:

I’m going to spend the next ~14 days using it in my own routine.

Real usage:

• gym
• hydration
• trying (and failing) to run consistently

That’s where I’ll know if this is actually usable — or just a cool idea.

If things go well, that should be enough to get to an MVP.

if you can criticize, comment on the ideas, or even roast me I’d really appreciate it — all feedback is valuable
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