Practical Guide to LLM in Enterprise
Learn about contextualization strategies and prompting techniques.
← Back to LLM & PromptingAgentic AI represents the next frontier in artificial intelligence: autonomous systems that can reason, plan, and execute complex tasks with minimal human intervention.
Unlike traditional AI that responds to single queries, agentic systems can break down goals into steps, use tools, and adapt their approach based on results.
Agents can independently complete multi-step workflows, from research and analysis to content creation and system interactions, without constant human guidance.
AI agents can interact with external tools, APIs, databases, and applications, extending their capabilities far beyond text generation.
Advanced agents can decompose complex problems, create execution plans, and adjust their strategies based on intermediate results and feedback.
Multiple specialized agents can work together, delegating tasks to each other and combining their expertise to solve problems no single agent could handle alone.
Several major frameworks have emerged to help developers build agentic AI applications. Each has its own philosophy, strengths, and ideal use cases.
A low-level agent orchestration framework and runtime developed by LangChain. Designed for users with advanced needs requiring a combination of deterministic and agentic workflows, heavy customization, and carefully controlled latency.
An open-source standard by Anthropic designed to connect AI applications to external systems. Think of it as a USB-C port for AI applications, providing a standardized way to interface with data sources, tools, and workflows.
A library designed to simplify the creation of agentic applications that can use additional context and tools, delegate tasks to specialized agents, stream partial results in real-time, and maintain full traces of actions and decisions.
The choice of framework depends on your specific needs: LangGraph excels at complex, stateful workflows requiring fine-grained control; MCP is ideal when you need standardized connections to many external tools and data sources; OpenAI Agents SDK is the natural choice for applications built primarily on OpenAI models with a need for real-time feedback and traceability.
| Feature | LangGraph | MCP | OpenAI SDK |
|---|---|---|---|
| Workflow Complexity | High | Medium | Medium |
| Tool Integration | Custom | Standardized | Built-in |
| Learning Curve | Steep | Moderate | Gentle |
| Model Agnostic | Yes | Yes | No (OpenAI) |
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