Architectural Patterns of Agentic AI systems

The landscape of AI agents has shifted from simple "one-shot" prompting to complex agentic workflows. These architectures determine how an LLM thinks, uses tools, and corrects its own mistakes.

Here is a breakdown of the most prominent agentic architectures and the frameworks that power them.

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1. ReAct (Reason + Act)

The Concept: ReAct is the "grandfather" of agentic design. It forces the LLM to generate a Thought (reasoning step) before performing an Action (calling a tool), and then process an Observation (result from the tool). This loop continues until the task is solved.

• Architecture: Linear loop (Thought → Action → Observation).

• Example Tool: LangChain (specifically the AgentExecutor).

• Best For: Simple, multi-step tasks where the next step depends entirely on the outcome of the current one.

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2. Graph-Based Architecture

The Concept: Instead of a linear loop, graph architectures represent the agent’s logic as a Directed Acyclic Graph (DAG) or a cyclic graph. Each node is a function or an LLM call, and the edges define the flow based on conditional logic.

• Architecture: State Machines (Nodes and Edges).

• Example Tool: LangGraph.

• Best For: Complex, "human-in-the-loop" workflows where you need fine-grained control, cycles (looping back to fix errors), and state management.

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3. ReWOO (Reasoning Without Observation)

The Concept: Traditional ReAct agents can be slow because they wait for tool outputs before planning the next step. ReWOO decouples the reasoning from the execution. It looks at a prompt and creates a full plan of execution (with placeholders) all at once, then runs the tools in parallel.

• Architecture: Planner → Worker → Solver.

• Example Tool: LangGraph (can be used to implement ReWOO patterns) or CrewAI.

• Best For: Reducing latency and token costs when tool calls are independent of each other.

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4. Multi-Agent Systems (Role-Playing)

The Concept: Instead of one "super agent," you break the task down into specialized agents (e.g., a "Researcher," a "Writer," and a "Manager"). These agents communicate with each other to complete a goal.

• Architecture: Hierarchical or Sequential collaboration.

• Example Tool: Autogen (Microsoft) or CrewAI.

• Best For: Large-scale projects like software development or comprehensive market research where different "personas" add value.

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5. Plan-and-Execute

The Concept: Similar to ReWOO but more iterative. The agent first creates a long-term plan, executes the first few steps, and then re-plans based on the results. It is less rigid than ReWOO but more structured than ReAct.

• Architecture: Planner → Executor → Re-planner.

• Example Tool: BabyAGI or AutoGPT.

• Best For: Open-ended goals where the path to the solution is highly uncertain.

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Summary Comparison Table

Architecture	Primary Logic	Speed	Complexity	Best Framework
ReAct	Iterative loop	Slow	Low	LangChain
Graph	State Machine	Variable	High	LangGraph
ReWOO	Pre-planned steps	Fast	Medium	LangGraph / CrewAI
Multi-Agent	Collaboration	Slow	High	Autogen / CrewAI

We are currently in a "Great Migration" period for agentic frameworks. As of 2026, the industry has largely moved away from "black-box" executors toward explicit, graph-based control.

Here is the re-confirmed, updated breakdown of these architectures based on the latest 2026 standards.

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1. The Migration: LangChain → LangGraph

You are correct: LangChain has officially deprecated AgentExecutor (the legacy ReAct implementation). The community has shifted to LangGraph because the old ReAct loop was too hard to debug and impossible to "steer" mid-process.

Key Note: While "ReAct" still exists as a reasoning strategy, it is no longer the architecture in LangChain. It is now just one specific way to configure a Graph.

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2. Updated Architectures (2026 Standards)

A. Graph Architecture (State Machines)

Instead of a hidden loop, every step is a Node and every transition is an Edge.

• Current Standard: LangGraph.

• The Change: LangChain 0.x used AgentExecutor (a pre-built loop). LangGraph 1.0+ requires you to define a StateGraph. You explicitly draw where the agent goes if a tool fails or if a human needs to approve a step.

• Example: A customer support bot that cycles between "Search Documentation" and "Ask User for Clarification" until a confidence score is met.

B. Event-Driven & Conversational (The New AutoGen)

While graphs are rigid, Microsoft's AutoGen (v0.4+) has moved toward an Event-Driven Architecture.

• Current Standard: AutoGen.

• Mechanism: Agents act like microservices. They publish "events" (e.g., TaskCompletedEvent), and other agents subscribe to them.

• Best For: Massive systems (thousands of agents) where a static graph would become a "spaghetti" mess of lines.

C. Role-Based Hierarchical (The CrewAI Way)

CrewAI has doubled down on the Manager-Worker architecture.

• Current Standard: CrewAI.

• Mechanism: It uses a "Process" (Sequential or Hierarchical). You define a Manager agent that acts as the orchestrator, delegating tasks to specific Roles.

• Best For: Business processes where you need a clear "boss" agent to review work before finishing.

D. ReWOO (Reasoning Without Observation)

This remains the gold standard for low-latency and cost-saving.

• Current Standard: Implemented as a specific template within LangGraph.

• Mechanism: It breaks the task into a Planner, Worker, and Solver. The Planner creates a "blueprint" with placeholders (e.g., "Find the price of $X$ and $Y$"). Tools run in parallel, and the Solver fills in the blanks.

• Status: It is now considered a "Design Pattern" rather than a standalone tool.

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Quick Reference: What to use in 2026?

If you want...	Use this Architecture	Latest Tooling
Total Control / Debugging	Graph-Based	LangGraph
Autonomous Collaboration	Event-Driven	AutoGen 0.4+
Business Workflows	Role-Based / Hierarchical	CrewAI
Speed & Low Cost	ReWOO (Planning)	LangGraph Templates