Agentic AI Architecture Explained: How Autonomous Systems Think, Plan & Act

subrata sarkar
Aug 14
2 min read

As AI systems evolve from passive responders to proactive problem-solvers, agentic AI architecture is emerging as the backbone of this transformation. Unlike traditional models that wait for human prompts, agentic AI systems initiate actions, set goals, and adapt dynamically — much like human agents.

What Is Agentic AI?

Agentic AI refers to systems designed with agency — the ability to:

Set and pursue goals autonomously
Make decisions based on environmental feedback
Collaborate or compete with other agents
Learn and revise strategies over time

This marks a shift from reactive AI (e.g., chatbots, classifiers) to proactive AI (e.g., task-solving agents, multi-agent ecosystems).

Core Components of Agentic AI Architecture

Component	Description
Planner	Sets long-term goals and breaks them into executable steps
Executor	Carries out tasks using tools, APIs, or other agents
Memory Module	Stores context, past actions, and outcomes for learning and continuity
Critic/Evaluator	Assesses performance and suggests improvements
Environment Interface	Enables perception and interaction with external systems or users
Multi-Agent Protocols	Allows agents to collaborate, negotiate, or compete in shared environments