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.
Upgrade if you want to be the first to receive the full articles with detailed explanations and curated resources directly in your inbox. Simplify your learning journey →
or follow us on Hugging Face, this article about Knowledge – as well as two other articles “Building Blocks of Agentic Systems” and “The Role of Profiling in Agentic Workflows” – will appear there tomorrow for free
Want a 1-month subscription? Invite three friends to subscribe and get a 1-month subscription free! 🤍
