🦸🏻#9: Does AI Remember? The Role of Memory in Agentic Workflows

Memory – or more precisely, memories – is a key building block of an agentic workflow, closely associated with knowledge and profiling. But its deserves its own spotlight because it operates at a different level of granularity and function than “knowledge” and “profile.” While profiling defines how an agent interprets who it is (its character, it’s “avatar”), what it does (its behavior models), and where it operates (its environment), and while knowledge provides the facts or learned representations that guide decisions, memory is the dynamic record of experience that threads these elements together and actively participates in decision-making. Memory has been studied for decades, yet we still don’t fully understand how to make LLMs remember things consistently. Current AI systems can retrieve information, summarize past interactions, or even store selective details, but they lack a stable, structured memory that persists reliably over time. Today, we have a lot on our plate: we will explore a forgotten paper that may offer insights from the past, explain the different types of memory and their roles in agentic workflow, learn how the components come together in practice, clarify how models with memory mode “remember” things, and ask ourselves: how generative AI is transforming the nature of memory itself. Let’s start.

What’s in today’s episode?

• SOAR’s Legacy in Agentic Memory Systems: A Bridge from Cognitive Models to AI Agents

  • Declarative and Procedural Knowledge

  • Chunking

  • Subgoaling and Hierarchical Problem-Solving

• SOAR’s Legacy and Its Resonance with Modern AI

• Types of Memory that We Operate With Today

  • Long-Term Memory

  • Short-Term Memory

  • Bringing It All Together

• Memory and Generative Agents 

• How ChatGPT “Remembers” Things: Understanding Memory Mode

• Concluding Thoughts: AI Influence on Human Memory

• 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 ““.}

SOAR’s Legacy in Agentic Memory Systems: A Bridge from Cognitive Models to AI Agents

In 1987, Allen Newell, Paul Rosenbloom and John Laird proposed SOAR – an architecture for general intelligence. Today we continue to lock horns over the definition of general intelligence, but the authors of SOAR had a clear view: they meant that general intelligence is the ability of a system to handle a full range of cognitive tasks, employ diverse problem-solving methods, and continuously learn from experience.

When the SOAR architecture was introduced, it was a bold attempt to create a unified theory of cognition, blending problem-solving, learning, and memory into a single framework. SOAR's solution was elegant and introduced a structured approach to memory that resonates with modern AI agent architectures. By distinguishing between working memory (for immediate cognitive tasks) and long-term procedural memory (for learned rules), SOAR anticipated the challenges of building systems that retain, recall, and refine knowledge over time. While modern agentic AI relies more on statistical learning and vector-based retrieval than explicit production rules, the fundamental question of how systems remember and improve remains central – making SOAR a relevant conceptual ancestor to today's AI frameworks.

Declarative and Procedural Knowledge

One of SOAR’s key innovations was distinguishing between two types of knowledge. Declarative knowledge, consisting of facts and information, is held in working memory and represents the system’s current understanding of its environment. Procedural knowledge, in contrast, is embedded in long-term memory as production rules that dictate the system’s actions. This clear separation enabled SOAR to manage immediate problem-solving tasks while building a lasting repository of strategies for future use.

Another hugely important feature was →

Chunking