Day 35: I Just Ran a Signed, Traceable AI Agent from My CLI

Table Of Contents

What I Built (With ChatGPT as my pair)

Thirty-five days ago, I asked myself:

“What if we could define AI agents like packages with plans, constraints, and versioning?”
Author Name

No wrappers.
No black boxes.
Just structured, traceable, testable behavior — written in plain Markdown, running on local models.

What I Built (With ChatGPT as my pair)

AI agents defined in plan.md and identity.json

Constraints like “max 5 bullets” or “must use only defined tools”

Local simulation using Mistral (via Ollama)

Output validated against strict criteria

Plans signed and certified with cryptographic keys

Full trace log of every run

And yes, it passed validation.

Why This Matters

It’s not a simple LLM project.

This AI Agent project is:

A foundation for the agent economy

A path toward certifiable AI behavior

A way to make AI systems accountable by design

A CLI where agents don’t just respond — they act under contract

Mistral AI Agent Dokugent Test — Don’t let the simplicity fool you.
This isn’t just output. It’s from a signed, versioned, certifiable agent.
GDPR-aware, ISO-aligned, and handshake-ready anytime.
You’d know who wrote it, why it ran, and how old it is.
That’s infrastructure.

✅ Agent Certified • 🔐 GDPR-Aware • 📜 ISO-Aligned • 🧾 Traceable by Design

What’s Next

A full agent registry

Agent-to-agent interactions (e.g. ICE-001 + SampleBot)

A new developer primitive: dokugent init for behavior, not just code

PS:

Our Dokugent CLI Dev Log 003 was co-written by ChatGPT (it was its turn).
I’m still not sure if this was a hyperfocused build streak or the start of something serious, but I do know this:

The CLI runs.
The agents follows the structure.
And somehow… it feels like the beginning of a system I can trust.

— carmelyne