✓What You'll Learn
Large language models and agentic AI are frequently confused. Deploying an LLM for a task that requires agentic capabilities leads to frustrating limitations. This guide gives you the clarity to choose correctly.
Large language models and agentic AI are frequently confused — not surprising, since agentic AI systems are typically built on top of LLMs. But the distinction matters enormously for business decision-making. Deploying an LLM for a task that requires agentic capabilities leads to frustrating limitations. Deploying a full agentic system for a task that a simple LLM could handle leads to unnecessary complexity and cost. This guide gives you the clarity to choose correctly.
Large Language Models: Capabilities and Limits
A large language model is a machine learning model trained to predict and generate text. Given a prompt, an LLM generates a response — a single pass through the model's parameters produces a single output. The model has no persistent memory of prior interactions (beyond what is included in the current context window), no ability to take actions in external systems, and no mechanism for self-correction or iteration. What LLMs do exceptionally well: language understanding and generation, summarisation, translation, question answering from provided context, first-draft content creation, and single-step analytical reasoning.
Agentic AI: What LLMs Cannot Do Alone
Agentic AI builds on LLMs by adding the architecture required for autonomous, multi-step goal pursuit: persistent memory across interactions, tool access (web search, databases, APIs, code execution), planning and task decomposition, self-evaluation and iteration. An agentic system using an LLM as its reasoning core can search the web for current information, query your database for customer records, execute code to run calculations, draft an email, send it, and wait for a response — all as steps in pursuing a single goal, without human direction at each step.
Decision Framework: LLM vs Agentic AI
| Use Case Characteristic | Use LLM | Use Agentic AI |
|---|---|---|
| Number of steps required | 1–2 steps | 3+ sequential or parallel steps |
| External system access needed | No | Yes |
| Requires iteration/self-correction | No | Yes |
| Goal is clearly defined at start | Either | Required |
| Memory across interactions needed | No | Yes |
| Actions have real-world consequences | No | Yes (requires governance) |
| Implementation complexity preference | Low | Willing to invest |
Practical Examples
Use an LLM when: you need to generate a first draft of a blog post from a brief; summarise a document; answer a question from provided text; or translate content between languages. These are all single-pass, no-action-required tasks that LLMs handle natively. Use an agentic AI system when: you need to research a prospect and personalise outreach based on that research; autonomously resolve a customer support ticket that requires accessing account data and processing a refund; or monitor your business data and send weekly performance summaries with AI-generated insights to your leadership team. Each of these requires multiple steps, tool access, and autonomous decision-making that LLMs alone cannot provide. The complete agentic AI guide provides the foundational context for understanding the full capability spectrum.