Day 85: I Taught My AI To Say NO (So I Can Be Consistent)

Staying consistent has been brutally hard lately. I kept burning out, drifting, losing momentum.

The only thing I seemed to nail every day was writing a long todo list… and then never looking at it again.

So I had a simple thought:

What if the todo list came back to me … automatically … instead of waiting for me to return to it?

What if something could prioritize, organize, and reflect for me, while I just execute?

For that to work, it can’t be a normal app.

It needs to live where I live: inside a chat.
One long, continuous thread.Like talking to a friend on WhatsApp.

A partner who understands how I say things.
Captures what matters.
Never forgets.

Where Today’s AI Falls Short

If you try this on ChatGPT:

“I need to go to the gym at 7am. Also I’ve got a meeting tomorrow at 9am with Client ABC.”

It will probably be scheduled both as a Reminder. Great.

But now start a new session and say:

“I’m done with the gym.”

It has no idea what you’re talking about.

I needed an AI that can hold a meaningful conversation while capturing everything related to … tasks, goals, events, and all the messy in-between thoughts.

Something that tracks my progress.
Feels my workload.

Understands my patterns.

And helps me stay consistent without burning out.

Why Intent Prediction Isn’t Enough

After exploring agentic architectures for weeks, something became obvious:

Capturing the right information through conversation is sooooo much harder than I thought. 

For example, to add a new task through conversation, a human might say “I need to go to the gym” or “I gotta hit the gym” or “add going to the gym task”. 

Most of the existing workflows try to classify the user’s intent and then route accordingly. 

But it’s almost impossible to predict human intention. Humans are too inconsistent and too unpredictable.

This means 99% of all workflows today will fail with conversational Agents. 

If I want an AI that makes me consistent, it has to be consistent.

Which led to the real insight:

The solution isn’t prediction.
It’s autonomy.

Not “AI that answers questions.”
But “AI that thinks, decides, remembers, and acts.”

Autonomy requires four pillars:

• a purpose

• a constitution

• a worldview (state)

• a cognitive loop that thinks and chooses

This is where the architecture began to take shape.

What an Autonomous AI Partner Needs

As I built and rebuilt prototypes, I discovered the essential ingredients for an AI that can support real, long-term consistency:

1. A Worldview (State)

A sense of time, context, and continuity:

• What time it is

• What I said last

• What it’s working on

• My goals

• My workload

• Our full conversation history

A living memory.

2. A Constitution

A set of principles that constrain behavior:

• User sovereignty — my goals and wellbeing come first

• Favor small, sustainable steps

• No burnout plans

• Progress over perfection

This gives Vee something closer to a “guided will.”

3. A Cognitive Loop

Vee runs a continuous cycle:

Observe → Orient (Think) → Decide → Act

It can replan, continue, or stop — fully autonomously.

4. Tools (Agency)

I gave Vee a toolbox (Python functions):

• add_task

• update_task

• search_task

• add_goal

• add_note

• search_notes

But I never tell it when to use them.
It figures that out itself.

This is where autonomy stops being theory and becomes behavior.