AI Agent Architecture

The diagram below illustrates the core architecture of AI agents.

Step 1: Perception 

The agent processes inputs from its environment through multiple channels. It handles language through NLP, visual data through computer vision, and contextual information to build situational awareness. Modern systems incorporate audio processing, sensor data, and state tracking to maintain a complete picture of their surroundings.

Step 2: Reasoning

At its core, the agent uses logical inference systems paired with knowledge bases to understand and interpret information. This combines symbolic reasoning, neural processing, and Bayesian approaches to handle uncertainty. The reasoning engine applies deductive and inductive processes to form conclusions and even supports creative thinking for novel solutions.

Step 3: Planning

Strategic decision-making happens through goal setting, strategy formulation, and path optimization. The agent breaks complex objectives into manageable tasks, creates hierarchical plans, and continuously optimizes to find the most efficient approach. This includes sequential planning, tactical adjustments, and simulations to test potential outcomes.

Step 4: Execution

This layer mold plans into actions through intelligent selection, tool integration, and continuous monitoring. The agent leverages APIs, code execution, web access, and specialized tools to accomplish tasks. Advanced systems support parallel and distributed execution, with implementations extending to cloud infrastructure and edge computing.

Step 5: Learning

The adaptive intelligence component combines short-term memory for immediate tasks with long-term storage for persistent knowledge. This system incorporates feedback mechanisms, using supervised, unsupervised, and reinforcement learning to improve over time. Analytics, model management, and meta-learning capabilities enable continuous enhancement.

Step 6: Interaction

The communication layer handles all external exchanges through interfaces, integration points, and output systems. This spans text, voice, and visual communication channels, with specialized components for human-AI collaboration. The agent selects appropriate formats and delivery methods based on the context.

What makes AI agent different from automation and workflows is the feedback loops between components. When execution results feed into learning systems, which then enhance reasoning capabilities, the agent achieves truly adaptive intelligence that improves with experience.