The Agentic Post
Breaking
Black October 2026: Every Major AI Security Incident, Explained  Â·  ARTEX: The Open-Source AI Hacking Tool Behind October’s Biggest Attacks  Â·  Chinese Hackers Used AI Agents to Hit 2,250 Microsoft 365 Accounts  Â·  PHP2Shell Is Now Being Exploited, and Attacks Began Before the Patch  Â·  OpenAI-Backed Thrive Buys ADT for $15.3 Billion to Rebuild It With AI  Â·  The AI Data Center Backlash Hits Minnesota and Australia in One Week  ·  
Home/AI Agents/Agent Frameworks
Choosing an Agent Framework

Choosing an Agent Framework

Agent Frameworks

A practical checklist for choosing an AI agent framework, covering workflow shape, provider flexibility, setup complexity, and tool connection needs.

Rather than picking an agent framework based on which one you’ve heard about most, a short, specific checklist gets you to the right choice faster.

Is your workflow a line or a graph? A largely linear or simply-branching workflow fits a simpler framework well. If it needs to loop or maintain complex state, our comparison of LangChain and LangGraph covers exactly this distinction.

Do you need multiple model providers, or one specific one? Want flexibility to switch models? A general-purpose framework fits better than a provider-native SDK. Committed to one provider? That SDK usually gives you the tightest integration.

How much configuration are you willing to do upfront? Broader frameworks generally require more setup. If you want a working result fast with less flexibility, a framework with more built-in structure serves a first project better.

If you’ll be connecting to many external tools, check MCP compatibility specifically, since it increasingly lets you avoid custom integration code regardless of framework. See LangChain’s own documentation as a starting reference.

Up Next
AI Agents in HR: A Practical Guide

AI Agents in HR: A Practical Guide

Enterprise Agents

A practical guide to deploying AI agents in HR, covering reliable use cases, hiring decision risks, and sensitive data handling.

HR is a genuinely promising but genuinely sensitive area for AI agents, since the same automation that saves real time can also introduce real fairness and compliance risk if deployed carelessly.

Initial resume screening for basic qualifications, answering routine policy questions from an internal knowledge base, scheduling and coordination for interviews and onboarding, these are the areas AI agents help HR teams reliably today.

AI systems used in hiring decisions face real, specific legal scrutiny in multiple jurisdictions around bias and fairness. Any AI involvement in screening or ranking candidates needs a human decision-maker in the loop and should be auditable for disparate impact, not run as an unsupervised black box.

HR systems hold genuinely sensitive personal data, and any AI tool touching it needs clear data handling policies. Our vendor selection checklist covers the specific data questions worth asking before adopting any HR AI tool.

AI agents genuinely help with routine HR administration, but hiring decisions and sensitive data both need real human oversight built in from the start. See McKinsey’s own research on enterprise AI adoption.