Cognitive Architecture
A framework or blueprint for building AI systems that mimics aspects of human cognition, including perception, memory, reasoning, learning, and action.
Why It Matters
Cognitive architectures guide the design of capable AI agents by providing structured approaches to combining perception, memory, planning, and action.
Example
An agent architecture with components for: perceiving inputs (LLM), storing knowledge (vector DB), reasoning (chain-of-thought), planning (task decomposition), and acting (tool use).
Think of it like...
Like an architect's blueprint for a building — it defines where each room (component) goes and how they connect, ensuring the whole structure works together.
Related Terms
AI Agent
An AI system that can autonomously plan, reason, and take actions to accomplish goals. Unlike simple chatbots, agents can use tools, make decisions, execute multi-step workflows, and adapt their approach based on results.
Planning
An AI agent's ability to break down complex goals into a sequence of steps and determine the best order of actions to accomplish a task. Planning involves reasoning about dependencies, priorities, and contingencies.
Reasoning
An AI model's ability to think logically, make inferences, draw conclusions, and solve problems that require multi-step thought. Reasoning goes beyond pattern matching to genuine logical analysis.
AI Memory
Systems that give AI models the ability to retain and recall information across conversations or sessions. Memory enables persistent context, user preferences, and accumulated knowledge.
Agentic AI
AI systems designed to operate with high autonomy — planning, executing, and adapting without constant human oversight. Agentic AI emphasizes independent action-taking to accomplish user goals.