Skip to main content

What Are Agentic Systems? A Guide to Autonomous AI Agents

Agentic systems represent the next evolution of generative AI, moving beyond simple prompt-and-response interactions to create autonomous agents capable of complex problem-solving. While Large Language Models (LLMs) provide the core reasoning engine, agentic systems build a framework around them, allowing the AI to plan, execute tasks, and interact with its environment to achieve a specific goal.

By giving an LLM the ability to act, agentic systems unlock a new level of automation and capability.

  • Reasoning and Planning: An agent can take a high-level objective (e.g., "research the best laptops for video editing") and break it down into a sequence of logical steps.
  • Tool Use: It can interact with external tools like web browsers, APIs, or code interpreters to gather information, perform calculations, or execute commands.
  • Memory and Context: Agents maintain memory to learn from past actions and refine strategies. This is often implemented using vector stores, which allow the agent to efficiently retrieve relevant information from vast datasets, enabling sophisticated long-term memory.
  • Standardized Communication: Protocols like the Model Context Protocol (MCP) provide a common language for agents, models, and tools to share context and state, ensuring seamless collaboration within the system.
  • Autonomous Execution: The system can carry out its plan independently, adapting to unexpected results and making decisions without requiring step-by-step human guidance.