AI Agent Architecture

Understanding the building blocks of intelligent systems

Core Components of AI Agents

Perception Module

Processes raw input data from sensors, APIs, or user interfaces. Converts diverse inputs (text, images, speech) into structured representations.

Knowledge Base

Stores domain-specific information, facts, and rules. May include databases, vector stores, or knowledge graphs for contextual understanding.

Reasoning Engine

Applies algorithms (rule-based, statistical, or neural) to process information and make decisions. Includes planning and problem-solving capabilities.

Memory Module

Maintains short-term and long-term memory of interactions. Enables learning from experience and personalization over time.

Action Module

Executes decisions through APIs, robotic actuators, or user interfaces. Includes natural language generation for conversational agents.

Learning Module

Adapts behavior based on feedback and new data. Implements reinforcement learning, fine-tuning, or other adaptation mechanisms.

Architectural Patterns

Modular Architecture

Components are independent modules communicating through well-defined interfaces. Enables flexibility and easier maintenance.

Example: Perception → Reasoning → Action pipeline

Cognitive Architecture

Models human cognition with components for perception, memory, reasoning, and learning (e.g., SOAR, ACT-R).

Example: Working memory + Production system

Hybrid Architecture

Combines symbolic AI (rules, knowledge graphs) with neural networks for robust reasoning and learning.

Example: LLM + Knowledge Graph integration

Implementation Considerations

  • Scalability: Design for horizontal scaling of computationally intensive components
  • Safety: Implement guardrails, content filters, and fallback mechanisms
  • Explainability: Maintain audit trails and decision rationales
  • Interoperability: Standardized APIs for component communication

Emerging Trends

Multi-agent systems Neuro-symbolic AI Agent swarms Self-improving agents

Reference Architecture Diagram

A typical AI agent architecture flows from perception through reasoning to action, with feedback loops for learning.