A Quick Overview of Agentic AI Frameworks: Tools for Building Autonomous Systems

Agentic AI frameworks let machines think, act, and improve on their own. This overview compares LangChain, Auto-GPT, Semantic Kernel, and others. It covers key features, best practices, and real-world applications. This is a fast, clear breakdown of today’s top agentic tools.

Agentic AI frameworks empower systems to reason, plan, and execute multi-step tasks autonomously by combining large language models with external tools, memory, and iterative feedback loops Pluralsight LangChain Blog. Leading frameworks such as LangChain, Microsoft’s Semantic Kernel and AutoGen, and open-source solutions like Auto-GPT and BabyAGI each offer distinct trade-offs in modularity, scalability, and production readiness Introduction | 🦜️🔗 LangChain Microsoft for Developers. Emerging interoperability protocols like Google’s Agent2Agent (A2A) and Anthropic’s Model Context Protocol (MCP) are setting standards for multi-agent collaboration, while best practices—spanning clear iterative prompting, robust governance, and headless automation—ensure reliability and security in production Home- Google Developers Blog Home.

Introduction to Agentic AI

Agentic AI refers to systems that can autonomously plan and carry out tasks by making decisions, interacting with external environments, and adapting over time, rather than simply responding to single-step prompts IBM - United States LangChain Blog. These agents leverage techniques like chain-of-thought prompting, retrieval-augmented generation (RAG), and tool-calling APIs to extend model reasoning into practical workflows, maintaining context across long-running processes arXiv ML Journey.

Major Agentic AI Frameworks

LangChain

LangChain is a Python framework for building agentic applications by chaining LLM reasoning with external tools, APIs, and memory modules Introduction | 🦜️🔗 LangChain. It provides built-in abstractions for tool loading, memory management, and multi-step reasoning, making it ideal for use cases like conversational assistants, document analysis pipelines, and code generation agents Analytics Vidhya.

from langchain import OpenAI, Tool
from langchain.agents import initialize_agent, load_tools

llm = OpenAI(temperature=0)
tools = load_tools(["wikipedia", "llm-math"], llm=llm)
agent = initialize_agent(tools, llm, agent_type="zero-shot-react-description")

agent.run("What is the population of France plus 10?")

Microsoft Semantic Kernel & AutoGen

Semantic Kernel

Semantic Kernel is an enterprise-ready SDK that integrates LLMs and knowledge stores into applications, supporting Python, C#, and Java Microsoft Learn GitHub. Its Process Framework enables stateful, long-running workflows with human-in-the-loop options, distributable via Dapr or Microsoft Orleans Microsoft for Developers.

from semantic_kernel import Kernel
from semantic_kernel.connectors.ai.open_ai import AzureChatCompletion

kernel = Kernel()
kernel.add_service("chat", AzureChatCompletion("https://...openai.azure.com/", "YOUR_KEY"))
result = kernel.run("Generate a sales summary", skills=["SalesSkill"])
print(result)

AutoGen

AutoGen is an open-source, event-driven framework for prototyping distributed, multi-agent applications, built by Microsoft’s AI Frontiers Lab Microsoft for Developers. It uses an event bus for agent coordination, supports C# and Python, and integrates with Microsoft Orleans for scalability, with plans to converge with Semantic Kernel in 2025 Microsoft for Developers.

Open-Source Autonomous Agents

Auto-GPT

Auto-GPT decomposes high-level goals into iterative subtasks, executing and refining them through memory-augmented feedback loops GitHub WIRED. It excels at autonomous research assistance, market analysis, and content automation by chaining LLM “thoughts” into action sequences GitHub.

BabyAGI

BabyAGI is a minimalist, self-building agentic framework that dynamically manages a task queue and creates or updates tools based on progress, offering a lightweight entry point for autonomous workflows GitHub Easy With AI. Its simplicity and low computational footprint make it suitable for edge deployments and rapid experimentation GitHub.

ReAct Framework

The ReAct framework interleaves chain-of-thought reasoning with external tool calls, enhancing an agent’s capacity to plan, act, and revise decisions in real time IBM - United States arXiv. Implementations using LlamaIndex and Gemini demonstrate how ReAct agents can maintain transparency and handle complex decision trees Analytics Vidhya.

Other Emerging Frameworks

Atomic Agents

Atomic Agents is a Python-centric, atomicity-driven library for building extremely modular agentic workflows, where each component (prompt, schema, memory) is fine-tuned independently for clarity and maintainability brainblend-ai.github.io shakudo.io.

CrewAI

CrewAI offers a standalone framework for orchestrating multi-agent “crews” with event-driven flows, enabling role-based collaboration and production-grade monitoring through both code and no-code interfaces GitHub Learn R, Python & Data Science Online.

Google A2A & Anthropic MCP

Google’s Agent2Agent (A2A) protocol defines an open HTTP-based standard for agent-to-agent discovery, task management, and secure communication, complementing Anthropic’s Model Context Protocol (MCP) for tool and context access Home- Google Developers Blog Home- Google Developers Blog. A2A empowers developers to combine agents from different vendors and manage them across platforms, while MCP standardizes how agents fetch tools and data during inference DevRel Guide.

Best Practices for Agentic AI

Governance & Accountability: Adopt clear lifecycle responsibilities—including developers, deployers, and users—with pre-deployment risk assessments and audit trails as outlined by OpenAI’s governance guidelines OpenAI.
Iterative Prompting & Validation: Provide explicit expected outputs (visual mocks, test cases) to guide agent iterations, leveraging headless CI modes for automated verification as recommended by Anthropic’s Claude Code practices Home.
Security & Least-Privilege Access: Enforce least-privilege tool and data access, integrate human-in-the-loop checkpoints, and use standardized protocols like A2A and MCP to ensure authenticated, auditable agent interactions Home- Google Developers Blog.
Headless Automation & CI Integration: Use headless modes (claude -p, --output-format stream-json) to embed agentic tasks into CI/CD pipelines for reproducible, non-interactive workflows Home.

Comparison & Recommendations

Framework	Modularity	Scalability	Production-Readiness	Community Support
LangChain	High	Medium	Beta	Large
Semantic Kernel	Medium	High (Orleans/Dapr)	Production (v1.0+)	Enterprise
AutoGen	High	High (Orleans)	Preview	Growing
Auto-GPT	Low	Variable	Experimental	Very Large
BabyAGI	Low	Low	Experimental	Niche
ReAct	N/A (Pattern)	Depends on Hosting	Pattern-agnostic	Research-oriented
Atomic Agents	High	Medium	Emerging	Small
CrewAI	Medium	High	Available	Growing

For rapid prototyping and open-source flexibility, LangChain or Auto-GPT are strong starting points; for enterprise-grade stability and support, Semantic Kernel and converging AutoGen are recommended; for interoperable multi-vendor ecosystems, ensure A2A/MCP compliance; and for research-grade reasoning, leverage ReAct and atomic frameworks.

Agentic AI is rapidly maturing—choosing the right framework depends on your performance needs, integration targets, and support requirements. Rigorous governance, secure protocols, and iterative validation will unlock agents’ full potential.

There are many frameworks and best practices books out-there and they're all interesting and evolving fast. This is just the beginning.

Stay tuned.

‍

Cohorte Team

April 24, 2025