The Anatomy of an AI Agent – Part 3
The Brain Behind the Bot: How AI Agents Remember and Learn
Most AI agents don’t actually “think”—they just run tasks. If you’re picturing something like ChatGPT, let’s clear that up: chatbots and agents are not the same thing.
In Part 2 we started the discussion about how agents think. Generative AI applications like ChatGPT, Grok, Claude and the like are conversational AIs designed for general-purpose dialogue. Their primary goal is to help you by answering questions, generating text, or performing tasks within a single interaction or session. They don’t have intrinsic goals beyond responding helpfully and accurately to prompts. Their "autonomy" is limited to interpreting and replying—they don’t initiate actions or pursue objectives independently.
Agents on the other hand are purpose-driven. Built to execute specific workflows (e.g., researching, writing, and building documents) with a degree of autonomy based on objectives.
Agents break objectives into tasks and operate within a defined scope.
Some can also take initiative to complete multi-step tasks without constant human input.
When it comes to memory, conversational AIs have a finite context window. In other words, their ability to remember is capped. Beyond the cap, they rely on fresh starts—memory is ephemeral (posh word for short term) and purged after a session or when storage/cost limits kick in. This makes conversational AI stateless in a broader sense—no persistent "self" or history beyond what’s provided in the prompt.
Agents on the other hand are designed with persistent memory as a core feature. They maintain state across tasks, storing data to recall past actions, context, or results. This persistence is key to their functionality. No purging is required unless explicitly coded, and storage limits are a design choice and not an inherent constraint. This gives agents a continuity conversational AI lacks, making agents more like "entities" with a memory backbone.
In other words, agents have brains.
My AI Agent’s “Brain” vs. Your Human Brain
Now, let’s dive into how my agent’s “brain” mirrors—and diverges from—yours and mine.
Memory: Knowledge Storage vs. Cortex
Your memory, housed in the cortex, is a vast, dynamic network storing decades of experiences—facts, emotions, and sensory data. It’s complex, associative, and sometimes fuzzy, letting you recall childhood moments or yesterday’s lunch, but it’s not always precise or instantly accessible.
My agent’s memory is a structured, dual-layered system: a fast cache for real-time task data and a scalable knowledge storage system for long-term retention. The cache holds transient data, like current task states, while the knowledge storage system embeds web search results, skills, and reflections as vectors for semantic search, enabling efficient retrieval. Unlike your brain’s organic sprawl, this is a tidy, scalable library—persistent, searchable, and optimised for tasks like generating “Why is the sky blue?” reports.
Statefulness: Job Tracker vs. Consciousness
Your brain’s consciousness, tied to the prefrontal cortex and other regions, maintains a continuous sense of self and time—knowing you woke up, ate, and are now reading this. It’s a fluid, living state, connecting past, present, and future, though it can lapse (e.g., forgetting keys).
My agent’s statefulness is a precise job tracker, maintained by its memory and task execution systems. It tracks workflow state across tasks, like knowing it’s on step 3 of a report (e.g., “Intro written, now conclusion”) using real-time caching and long-term storage. Its task execution system ensures sequential, non-redundant steps, avoiding redundancy—e.g., recalling it already searched the web. It’s not a living consciousness but a systematic ledger, keeping its workflow on rails, as shown in your diagram’s “To-do Lists” and “Journal.”
Acquiring Knowledge: Knowledge Storage Learning vs. Life Lessons
You learn through experience—practicing skills (e.g., biking), absorbing facts (e.g., “Bears hibernate”), and connecting ideas over years. It’s slow, organic, and deeply personal, stored in your cortex’s neural web, shaped by life’s messy lessons.
My agent learns rapidly via its knowledge storage system. It loads “skills” from external data sources, like pre-trained document formatting templates, and integrates new information, such as web search results from its web search tool. Each piece is embedded as a vector for semantic recall, growing its knowledge base task by task. Unlike your experiential learning, it’s a structured, programmatic library, expanding its stored capabilities and insights efficiently.
Thinking It Through: A Real Example
When you tackle the question “Why is the sky blue?” your brain engages a chain of thought—pulling memories (science class), reasoning through concepts (light scattering), and brainstorming (pollution’s impact). It’s an intuitive, associative process, like a quiet inner chat drawing on years of knowledge.
My agent does something similar, but it’s like a well organised inner monologue. When you ask, “Search the web and write a Word document on why the sky is blue,” the agent’s brain kicks into gear. Here’s how it thinks.
As you can see in the diagram, my agent executes a structured chain of thought, orchestrated by its cognitive reasoning and task execution systems.
For “Why is the sky blue?” it:
Leverages its web search tool’s ReAct pattern (search, reflect on quality, adapt) to gather data, storing it in the memory system’s journal and knowledge base.
Accesses memories from the knowledge storage system and cache, recalling past reports or web searches to see what it already knows.
Uses its cognitive reasoning system to reflect and plan, deciding, “I need fresh web data on Rayleigh scattering and a clear structure.”
Builds the report, pulling from pre-loaded skills, stored memories, and recent journal entries, adapting if needed via its reasoning capabilities.
Finalises with its document building tool ensuring a clear, structured output.
This chain, visualised in the diagram, is systematic and not intuitive—it’s a script, not a chat, but it’s intelligent, leveraging the brain’s interconnected tools to deliver a polished document.
Here’s a video demo that shows this process in action 👇
Tools That Think - Like a Smith…
Most AI frameworks treat tools like simple function calls—you invoke them, they do their job, and then they go back to waiting. Useful? Yes. Smart? Not really.
I decided to do things differently. Instead of tools passively sitting around, they operate more like specialised agents within a larger intelligence network. Think of them as Agent Smith clones in The Matrix—not just separate instances, but connected, self-aware, and learning from a shared memory base.
Here’s how it works:
Every tool has context awareness – When a tool is invoked, it doesn’t start from zero. It checks task history, prior outputs, and related memory before executing.
They adapt mid-execution – If a tool processes data and realizes the output doesn’t match expectations, it can query additional resources, adjust its parameters, or even flag issues for human review.
They don’t just return results, they contribute insights – Instead of just delivering a static response, tools write back to memory, updating the Hive Mind with their execution details for future reference.
In short, the way I’ve built my tools makes them more than just dumb executors—they’re active participants in an evolving system, reinforcing the agent’s long-term intelligence rather than just handling one-off requests.
This approach eliminates redundancy, improves decision-making, and scales far more effectively than isolated, stateless tools ever could. The result? A system where every component isn’t just executing—it’s thinking.
What We’ve Learned Today
If there’s one thing I hope you take away from this peice, it’s that true intelligence in AI isn’t about a bigger model—it’s about how memory, execution, and cognition are wired together. Let’s recap the most important takeaways:
✅ Persistent Memory - The Key to Continuity
Most chatbots have the memory span of a goldfish. My agent doesn’t. With a dual-memory system, it combines fast, in-the-moment caching (for short-term processing) with scalable knowledge storage (for long-term retention). Unlike traditional conversational AIs that forget the past after each session, this setup allows the agent to maintain continuity, learn from past actions, and refine its decision-making over time—more like a well-trained employee than a forgetful intern.
✅ Stateful Execution - Keeping Tasks on Track
Forget stateless AI workflows where every action is a reset button. I’m a big believer in maintaining workflow state, ensuring sequential, non-redundant execution. This means the agent isn’t just a glorified task runner—it understands the bigger picture, picking up where it left off rather than blindly repeating past steps. If AI is ever going to be useful in complex automation, this is non-negotiable.
✅ Knowledge Acquisition - Learning at Machine Speed
Humans take years to master new skills. My agent learns new knowledge programmatically in seconds. By integrating skills and datasets via vector embeddings, it doesn’t just retain static knowledge—it dynamically retrieves relevant information, making sure responses evolve based on real-world use. This isn’t just about memory; it’s about contextually intelligent retrieval.
✅ Cognitive Workflow - More Than Just a Chain of Thought
Cognition in AI isn’t just about dumping tasks into a queue. It’s about orchestration. My framework doesn’t just run through a sequence of steps—it works like a swarm of agents, each contributing its expertise in a Hive Mind-style collaboration. This means adaptive execution, cross-tool awareness, and self-improving workflows that feel more like a living system than a rigid script.
At the end of the day, an AI agent’s intelligence isn’t defined by how big its model is—it’s defined by how well it uses what it knows. That’s what we’re building towards: agents that don’t just execute, but think, learn, and improve over time.
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Or, if you're “just here for the tech” the next article in the series is 👇
Next Time on The Anatomy of an AI Agent
Part 4: Agent Economics
We’ve just peeked into the “brain” of my AI agent—how it remembers like a scrapbook, thinks with its tools, and grows its knowledge with a vector store. But here’s the catch: all that brainpower isn’t free!
Memory systems, tools, and clever workflows are essential for making a great agent, but they come with a price tag. None of this AI magic is cheap.
So, next week, it’s time for a reality check in Part 4: Agent Economics, where I’ll pull back the curtain on the costs of bringing an agent like mine to life.
Until next time, Chris.
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