Understanding AI Agents: Core Concepts, Types, and Practical Insights

"Agent" has become a hot term in recent discussions, but its meaning varies widely; this article provides one of the clearest explanations of what an AI agent actually is and how it differs from plain generative AI.

The core concepts consist of two parts: the large language model (LLM) that, based on user input, context, and conversation history, decides the next action, and the tool components that execute those actions. The LLM outputs (a) a textual explanation of the next step and (b) structured information describing the specific action and its parameters. Sometimes the output is "no further action needed".

The basic agent loop—accept input, invoke the LLM to choose an action, call the appropriate tool, feed the tool's result back to the LLM, and repeat—is illustrated in the diagram below.

To highlight the difference, consider a pizza‑making query. A non‑agent system simply feeds the prompt to an LLM (e.g., ChatGPT) and receives a textual answer. An agent system, however, may have a "recipe search" tool; the LLM decides to invoke that tool with "pizza" as a parameter, retrieves the recipe, and then determines that no further steps are needed, ending the loop. This ability to call external tools makes agents more powerful for complex, multi‑step tasks.

Agents are attractive because they decompose problems naturally, mirroring human problem‑solving, and they can reduce the randomness of pure generation by grounding actions in concrete tools. However, they inherit the LLM's limitations—hallucinations, over‑confidence, and lack of real‑world understanding—so reliability remains a challenge.

Two major agent families have emerged. Autonomous agents such as AutoGPT (released shortly after ChatGPT in early 2023) and Cognition's Devin run independently after a single user prompt, attempting to complete tasks without human intervention. Collaborative agents (or "AI flows") like Windsurf's Cascade, Cursor's Composer Agent, and GitHub Copilot Workspaces keep the human in the loop, allowing observation, correction, and approval of each step. This human‑in‑the‑loop design mitigates latency and failure risks, especially when agents operate within the same environment as the user (e.g., an IDE).

The article also provides a practical checklist for evaluating any agent system, covering questions such as: Is the system truly an agent (does it use LLM‑driven tool selection)? Is it autonomous or collaborative? What tools are available, and how are they integrated? Which reasoning model is used (e.g., Claude 3.5 Sonnet is noted for strong tool‑calling performance)? How does the agent handle data access and permissions? What is the response latency, and how can users observe and guide the agent’s actions? How is the agent integrated into applications beyond a chat panel?

Finally, the piece warns against ignoring "The Bitter Lesson" (Richard Sutton’s observation that raw compute and data eventually outperform hand‑crafted rules). Over‑engineering prompts or tool selections may yield short‑term gains, but as compute becomes cheaper and models improve, such bespoke solutions risk obsolescence. Embracing scalable, data‑driven approaches is essential for the future of AI agents.