Autonomous Agents

🧭 Summary

Most modern AI systems treat intelligence as something stored inside a model.

A neural network is trained on massive datasets, its weights are adjusted, and those weights become the system’s knowledge. When the model produces an output, we interpret that output as the result of the intelligence encoded inside those parameters.

But this perspective has a limitation.

Once training is complete, the model is largely static. It does not improve through its own actions, and it does not adapt based on the outcome of its behavior unless we retrain it.

How to design a pipeline that turns vague goals into smart prompts

🧪 Summary

Why spend hours engineering prompts when AI can optimize its own instructions. This blog post introduces a novel approach toward creating a self-improving AI by treating prompts as programs. Traditional AI systems often rely on static instructions rigid and limited in adaptability. Here, we present a different perspective: viewing the Large Language Model (LLM) as a prompt compiler capable of dynamically transforming raw instructions into optimized prompts through iterative cycles of decomposition, evaluation, and intelligent reassembly.

How a self-evolving AI learns to reflect, score, and rewrite its own reasoning

🧪 Summary

What if an AI could think not just solve problems, but reevaluate its beliefs in the face of new information?

In this post, we introduce a system that does exactly that. At the core of our pipeline is a lightweight scoring model called MR.Q, responsible for evaluating ideas and choosing the best ones. But when it encounters a new domain, a new goal, or a shift in task format, it doesn’t freeze it adapts.

🔧 Summary

The General Reasoner paper shows how we can train LLMs to reason across domains using diverse data and a generative verifier. In this post, I walk through our open-source implementation showing how we built a modular reasoning agent capable of generating multiple hypotheses, evaluating them with an LLM-based judge, and selecting the best answer.

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🧠 What We Built

We built a GeneralReasonerAgent that:

• Dynamically generates multiple hypotheses using different reasoning strategies (e.g., cot, debate, verify_then_answer, etc.)
• Evaluates each pair of hypotheses using either a local LLM judge or our custom MR.Q evaluator
• Classifies the winning hypothesis using rubric dimensions
• Logs structured results to a PostgreSQL-backed system

All of this was integrated with our existing stephanie framework, which includes: