🦸🏻#8: Rewriting the Rules of Knowledge: How Modern Agents Learn to Adapt

In one of the previous episodes, we've covered the critical role of profiling in agentic workflows, exploring how agents build awareness of their identity, behavior, environment, performance, and resources. Profiling emerged as the connective tissue linking knowledge, memory, and action, transforming agents from static systems into dynamic collaborators capable of nuanced decision-making. Making them, a digital personality.

In this episode, we’ll shift our focus to knowledge – the foundation of this digital personality’s expertise. How does an agent "know" what it knows? What are the mechanisms behind its expertise, and how do they influence its behavior? Let’s see. Prepare yourself for a fascinating deep dive into history!

What’s in today’s episode?

• Are Agents Still Knowledge-Based?

• From Explicit Knowledge to Learned Representations

• John McCarthy’s “Programs with Common Sense”

• What Does Knowledge Look Like Today?

  • Structural Knowledge: Building Connections

  • Meta-Knowledge: Knowing What You Know

  • Heuristic Knowledge: Learning the Rules of the Game

  • The Convergence of Knowledge Forms in Modern Agents

• Balancing Knowledge-Based and Learned Systems

• The Mechanics of Knowledge

• The Historical Foundation: A Tale of Two Frameworks

• Concluding Thoughts

• Resources that were used to write this article (we put all the links in that section)

_{We apologize for the anthropomorphizing terms scattered throughout this article – let’s agree they are all in ““.}

Are Agents Still Knowledge-Based?

The concept of “knowledge-based agents,” as defined in Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig, marked a turning point in AI. Their vision was clear and logical: agents perceive their environment, make decisions, and act on those decisions in a neat, procedural loop. It’s a beautifully organized system – but one built for a world where things don’t change much.

Today’s world doesn’t play by those rules. Agents aren’t confined to fixed sequences or predictable settings anymore. Instead, they’ve shifted from following procedural knowledge to a more declarative approach: defining outcomes, not steps. Imagine you’re telling an agent, “I need a cake,” and it figures out the rest – whether it’s grabbing ingredients, finding the recipe, or even ordering from a bakery.

This leap is why modern agents thrive in messy, unpredictable environments. They’re no longer following static rules but adapting to the moment, learning on the fly, and collaborating dynamically.

From Explicit Knowledge to Learned Representations

A key distinction lies in how modern agents manage knowledge.