Martín HounieProduct · Engineering · AI
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AI Lab

AI Lab

Experiments in agents, automation and AI-native product engineering.

AgentsHarnessAutomationConnected AIProductEngineeringKnowledge

The Lab lets me show how I think without pretending every experiment is a client case study. What you see here is honest exploration — not promises.

Statuses

I use honest statuses. I never upgrade an idea into experience: if something isn’t built yet, I say so.

Prototype
Experiment
Design note
Coming soon
Active experimentExperiment

Agent-ready Repository

How structure, instructions, tools and feedback can make a codebase easier for an agent to work with — without losing human review along the way.

HypothesisMost friction with agents comes from poor context, not model limits.
In progressConventions, persistent instructions and review gates on a real repository.
Human roleReviews every change before it merges. The agent executes, the person decides.
#context#instructions#feedback
Other experiments
HarnessDesign note

Remolk Engineering Harness

Making a real product environment (Remolk) easier for AI agents to understand, modify and verify. Designed around a real product, not a demo repo.

#context#instructions#review#real product
Connected AIComing soon

Connected Knowledge Assistant

An assistant that searches company information across approved data sources, with clear permissions and a human in the loop.

#search#MCP#permissions
ProductComing soon

AI Product Discovery Workflow

How AI can support research, requirements and product decisions — while keeping a human accountable for the decisions.

#discovery#research#decisions
Anatomy

How I document each experiment

Every Lab article follows the same structure, so the reasoning is clear — not just the result.

01Problem
02Hypothesis
03Architecture
04Context
05Tools
06Agent
07Guardrails
08Human role
09Result
10Failure modes
11Lessons

Want to apply this on your team?

The Lab is where I test ideas. In your company I turn them into real, reviewed, reliable workflows.

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