Active Inference Applied

Karl Friston's Free Energy Principle is beautiful theory. But can you actually build something with it?

The answer is yes. Active inference—the action-oriented extension of the Free Energy Principle—is moving from blackboards to codebases, from theoretical neuroscience to working AI agents. Researchers are building robots that navigate uncertainty, language models that plan ahead, and autonomous systems that maintain coherence in complex environments, all using active inference as their underlying mathematics.

This isn't just another AI architecture. It's a principled framework for building agents that perceive, decide, and act in unified ways—systems that don't separate perception from action, planning from learning, or exploration from exploitation.

Why This Matters for Coherence

Active inference agents maintain coherence by minimizing expected free energy: they seek states that are both unsurprising given their model of the world and informative about the world's actual structure. This creates systems that balance exploitation and exploration naturally, that learn through acting, and that exhibit goal-directed behavior without explicit reward functions.

Understanding applied active inference helps us understand what coherence maintenance looks like when formalized as mathematics and implemented in code.

What This Series Covers

This series explores practical implementation of active inference for AI systems, robotics, and autonomous agents. We'll examine:

• How to construct generative models for active inference agents
• Expected free energy as the objective function for action
• Message passing and belief propagation in practice
• Available software tools from PyMDP to RxInfer
• How active inference compares to reinforcement learning
• Active inference for embodied robotics
• Hierarchical architectures for complex planning
• Integration with large language models

By the end of this series, you'll understand why the question "How do you build coherent AI agents?" has a mathematically principled answer—and how that answer is being translated into working systems.

Articles in This Series

From Theory to Code: The Active Inference Implementation Revolution

Introduction to applied active inference - why this framework is becoming the actual math behind next-gen AI agents.

IdeasthesiaRyan Collison

The Generative Model: Building World Models for Active Inference Agents

How to construct generative models for active inference - the core engineering challenge.

IdeasthesiaRyan Collison

Expected Free Energy: The Objective Function That Plans

Deep dive into expected free energy as the objective for action selection - balancing exploration and exploitation.

IdeasthesiaRyan Collison

Message Passing and Belief Propagation in Active Inference

The computational implementation of active inference through message passing - making inference tractable.

IdeasthesiaRyan Collison

PyMDP and RxInfer: The Active Inference Software Stack

Survey of available tools for implementing active inference - from research frameworks to production libraries.

IdeasthesiaRyan Collison

Active Inference Agents vs Reinforcement Learning: A Comparison

How active inference differs from and relates to reinforcement learning - advantages and trade-offs.

IdeasthesiaRyan Collison

Robotics and Embodied Active Inference

Active inference for robotic systems - how embodiment changes the inference problem.

IdeasthesiaRyan Collison

Hierarchical Active Inference: Scaling to Complex Tasks

Multi-scale active inference architectures - how to build agents that plan across time scales.

IdeasthesiaRyan Collison

Active Inference for Language Models: The Next Frontier

Integrating active inference with large language models - where the field is heading.

IdeasthesiaRyan Collison

Synthesis: Applied Active Inference and the Engineering of Coherence

Integration showing how practical active inference work illuminates theoretical claims about coherence.

IdeasthesiaRyan Collison