The Architecture of Agentic AI: What Powers the Next Era of Intelligent Automation

Source: Dev.to

What Is Agentic AI Architecture?

Agentic AI architecture is a system design where multiple AI agents collaborate autonomously to complete multi‑step tasks using planning, reasoning, tools, memory, and workflows.

Simple definition
A structural framework that enables AI agents to plan, act, communicate, and complete tasks autonomously across digital environments. It is more than repeatedly calling an LLM API or chaining prompts; it is a full system comprising:

• Planning modules
• Orchestrators
• Memory + state management
• Tool interfaces
• Agent‑to‑agent communication
• Execution layers
• Safety + governance layers

What Is Agentic Architecture?

Agentic architecture is the high‑level pattern behind autonomous systems—a way to structure agents, memory, tools, plans, and workflows so they behave like intelligent workers rather than reactive bots.

Layered Layout

+-------------------------------+
|         Agentic Layer         |
|-------------------------------|
| • Planning engine             |
| • Agent manager/orchestrator  |
| • Task decomposition logic    |
+-------------------------------+
|          Agent Layer          |
|-------------------------------|
| • Domain‑specific agents      |
| • Tool‑execution modules      |
| • Decision nodes              |
+-------------------------------+
|         Memory Layer          |
|-------------------------------|
| • Context storage             |
| • Vector DB / RAG             |
| • Session state               |
+-------------------------------+
|        Execution Layer        |
|-------------------------------|
| • APIs                        |
| • Tools                       |
| • Functions                   |
+-------------------------------+

This multi‑layer structure makes autonomous behavior possible.

What Is AI Agent Architecture?

AI agent architecture refers to the internal design of a single agent—how it observes, reasons, plans, uses tools, acts, evaluates, and updates its memory.

Key Components

• Observation (input)
• Reasoning (LLM or logic)
• Planning / Action selection
• Tool use
• Acting
• Evaluation / Feedback
• Memory updates

Simplified Diagram

+-------------------------------+
|        AI Agent               |
+-------------------------------+
| 1. Observe (input)            |
| 2. Reason (LLM)               |
| 3. Plan / Decide              |
| 4. Use Tools / APIs           |
| 5. Act                        |
| 6. Evaluate / Retry           |
| 7. Update Memory              |
+-------------------------------+

The cycle repeats until the task is complete.

What Is Agent Architecture in Artificial Intelligence?

Traditional AI (pre‑LLMs) used:

• Reactive agents
• Symbolic planners
• Rule‑based systems
• Behavior trees
• BDI (Belief–Desire–Intention) models

LLMs modernized the field by adding:

• Natural language reasoning
• Tool interfaces
• Emergent decision‑making
• Dynamic planning
• Multi‑agent collaboration

Today’s LLM agent architecture blends classical AI structures with modern LLM intelligence.

Agentic AI Architecture Diagram

A fully integrated view of a modern autonomous system:

+-------------------------------+
|        Goal / User Query      |
+-------------------------------+
               |
               v
+-------------------------------+
|      Planner / Orchestrator   |
|  (Task decomposition engine)  |
+-------------------------------+
          /      |      \
         v       v       v
+-----------+ +-----------+ +-----------+
| Agent A   | | Agent B   | | Agent C   |
| (Research)| | (Reasoning)| | (Validation)|
+-----------+ +-----------+ +-----------+
          \       |       /
           \      |      /
            v     v     v
+----------------------------------+
|   Memory & RAG Layer             |
| (retrieval, embeddings, state)   |
+----------------------------------+
               |
               v
+----------------------------+
|   Tool / Action Layer      |
| (APIs, code, databases)    |
+----------------------------+
               |
               v
+----------------------+
|   Final Output       |
+----------------------+

This structure powers modern autonomous systems.

Agentic RAG Architecture (Where Retrieval Meets Autonomy)

Retrieval‑Augmented Generation (RAG) becomes more powerful inside agentic systems.

Components

• Multiple agents retrieving from RAG
• Agents writing and updating memory
• Retrieval used for planning and validation
• Autonomous correction based on new information

Example Flow

Task → Planner → Research Agent → RAG → Draft Agent → Validator Agent → Final Output

Instead of passive lookup, agents actively interact with memory, dramatically improving accuracy and autonomy.

Why Agentic Architecture Matters

Traditional LLM applications struggle when tasks require:

• Multiple steps
• Reasoning across tools
• Data validation
• System interaction
• Collaboration
• Error correction
• Dynamic adaptation

Agentic AI architecture addresses these gaps by providing:

• A structure for autonomy
• A framework for scaling agents
• A clean separation of reasoning, memory, and action
• A predictable workflow execution layer
• A multi‑agent collaboration environment

Consequently, agentic systems are being adopted in:

• Enterprise automation
• Research pipelines
• Data engineering
• Coding workflows
• Customer operations
• Analytics workloads