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

List Of Models

Sr. No.	Model Family (Learning Type)	Model Type	Model Name	Popular Implementations / Models	Use Cases
1	Supervised Learning	Regression / Linear Models	Linear Regression	Scikit-learn LinearRegression	House price prediction, forecasting
2	Supervised Learning	Regression / Linear Models	Logistic Regression	Scikit-learn LogisticRegression	Spam detection, credit scoring
3	Supervised Learning	Tree-Based Models	Decision Tree	CART (Classification & Regression Trees)	Loan approval, diagnosis
4	Supervised Learning	Ensemble (Bagging)	Random Forest	RandomForest (Scikit-learn)	Fraud detection, risk modeling
5	Supervised Learning	Ensemble (Boosting)	Gradient Boosting	XGBoost, LightGBM, CatBoost	Ranking, fraud detection
6	Supervised Learning	Distance-Based	k-NN	Scikit-learn KNN	Recommendation, similarity search
7	Supervised Learning	Probabilistic	Naive Bayes	GaussianNB, MultinomialNB	Spam filtering, NLP
8	Supervised Learning	Margin-Based	SVM	LIBSVM, Scikit-learn SVM	Text classification, bioinformatics
9	Supervised Learning	Neural Networks	ANN	TensorFlow / PyTorch basic NN	Prediction, pattern recognition
10	Supervised Learning	Deep Learning	CNN	ResNet, VGG, EfficientNet	Image recognition, medical imaging
11	Supervised Learning	Deep Learning	RNN / LSTM	Seq2Seq, LSTM (Keras/PyTorch)	Speech, time-series
12	Supervised Learning	Deep Learning	Transformers	GPT, BERT, T5, LLaMA	Chatbots, search, translation
13	Unsupervised Learning	Clustering	K-Means	Scikit-learn KMeans	Customer segmentation
14	Unsupervised Learning	Clustering	DBSCAN	Scikit-learn DBSCAN	Anomaly detection
15	Unsupervised Learning	Clustering	Hierarchical	SciPy Hierarchical Clustering	Taxonomy, grouping
16	Unsupervised Learning	Probabilistic	GMM	Scikit-learn GaussianMixture	Soft clustering
17	Unsupervised Learning	Dimensionality Reduction	PCA	Scikit-learn PCA	Visualization, compression
18	Unsupervised Learning	Dimensionality Reduction	t-SNE	sklearn / openTSNE	Embedding visualization
19	Unsupervised Learning	Neural Networks	Autoencoders	TensorFlow / PyTorch AE	Anomaly detection
20	Unsupervised Learning	Association Rules	Apriori	mlxtend Apriori	Market basket analysis
21	Semi-Supervised	Hybrid	Self-Training	Pseudo-labeling pipelines	Medical imaging
22	Semi-Supervised	Graph-Based	Label Propagation	sklearn LabelPropagation	Social networks
23	Semi-Supervised	Neural Networks	Semi-Supervised NN	FixMatch, MixMatch	NLP, speech
24	Reinforcement Learning	Value-Based	Q-Learning	OpenAI Gym implementations	Robotics, games
25	Reinforcement Learning	Deep RL	DQN	DeepMind DQN	Gaming, control systems
26	Reinforcement Learning	Policy-Based	Policy Gradient	REINFORCE	Robotics
27	Reinforcement Learning	Actor-Critic	PPO / A2C	Stable-Baselines3	Trading, optimization
28	Optimization / Evolutionary	Evolutionary	Genetic Algorithms	DEAP library	Scheduling, optimization
29	Optimization / Evolutionary	Swarm	Particle Swarm Optimization	PySwarms	Hyperparameter tuning
30	Optimization / Evolutionary	Swarm	Ant Colony Optimization	ACO algorithms	Routing, logistics
31	Rule-Based / Symbolic	Expert Systems	Rule-Based Systems	Drools	Business rules
32	Rule-Based / Symbolic	Knowledge-Based	Knowledge Graphs	Neo4j, RDF Graphs	Search, recommendations
33	Ensemble Learning	Bagging	Bagging	Scikit-learn Bagging	Variance reduction
34	Ensemble Learning	Boosting	AdaBoost	AdaBoost (sklearn)	Classification improvement
35	Ensemble Learning	Stacking	Stacking	StackingClassifier	High accuracy systems
36	AI Systems (Hybrid)	RAG	Retrieval-Augmented Generation	LangChain, LlamaIndex + GPT	Enterprise chatbots, QA
37	AI Systems (Hybrid)	Agentic Pipelines	AI Agents	AutoGPT, CrewAI, LangGraph	Task automation, research agents

rls

To enable row-level security (RLS) in MS SQL Server (2016 and later), you need to define a security policy that uses a user-defined, inline table-valued function as a filter predicate.

Step-by-Step Guide

1. Ensure compatibility

Verify that your SQL Server instance is at least SQL Server 2016 or newer.

2. Create a schema for RLS objects (Recommended)

This practice separates your security logic from the application data.

CREATE SCHEMA Security;
GO

3. Create a predicate function

This inline table-valued function contains the logic for determining which rows a user can access. The function must be created with SCHEMABINDING.

Example: This function allows a user to see rows where the UserID column matches their username, or if they are a Manager.

CREATE FUNCTION Security.fn_securitypredicate(@UserID AS sysname)
    RETURNS TABLE
    WITH SCHEMABINDING
AS
    RETURN SELECT 1 AS result
    WHERE @UserID = USER_NAME() OR USER_NAME() = 'Manager';
GO

4. Create and enable a security policy

The security policy links the predicate function to your target table and enables the RLS enforcement.

Example: This policy applies the function to the dbo.YourTable table, using the UserID column for the filter.

CREATE SECURITY POLICY SecurityPolicy
ADD FILTER PREDICATE Security.fn_securitypredicate(UserID) ON dbo.YourTable
WITH (STATE = ON);
GO

5. Grant necessary permissions

Ensure users have SELECT permission on both the target table and the security function.

GRANT SELECT ON dbo.YourTable TO SalesRep1;
GRANT SELECT ON Security.fn_securitypredicate TO SalesRep1;
-- Repeat for other users/roles as needed

Testing the Implementation
You can test RLS by impersonating different users to verify they only see authorized data. 
sql
EXECUTE AS USER = 'SalesRep1';
SELECT * FROM dbo.YourTable; -- This will only show rows matching SalesRep1's UserID

REVERT; -- Stop impersonating
EXECUTE AS USER = 'Manager';
SELECT * FROM dbo.YourTable; -- This will show all rows
REVERT

Docker run for mac

 docker run -d -p 10000:3000 -p 11000:4000  --name ub-react -v "/Users/atharvachandras/Desktop/rajesh/DockerVolumes/React:/rajesh" ubuntu:latest tail -F /dev/null

you have to use /Users/username/Desktop as base path

/Users/username has to be used, and it is always better to use /Desktop so that it is immediately visible ( otherwise everytime you have to use search option) 

docker run -d -p 10001:3001 -p 11001:4001  --name ub-python -v "/Users/atharvachandras/Desktop/rajesh/DockerVolumes/Python:/rajesh" ubuntu:latest tail -F /dev/null

docker exec -it ub-react bash

apt-get update 

apt-get install git

apt-get install vim

/* do NOT directly run following commands, they will get older versions */

apt-get install nodejs

apt-get install npm

/* instead use following commands  */

apt-get install -y curl

apt-get install sudo

curl -fsSL https://deb.nodesource.com/setup_24.x | sudo -E bash -

sudo apt-get install -y nodejs

nodejs -v 

npm -v 

as of 08feb2026 : 24.13.0 and 11.6.2