Generative AI Vs Agentic AI Vs AI Agents

Generative AI, AI agents, and agentic AI differ mainly in how far the system goes beyond producing text—and how it handles tasks that require outside information or multi-step workflows. At the foundation are large language models (and large image models), trained on massive datasets to generate new content—text, images, audio, or video frames—based on prompts. In a typical generative AI application, the user supplies instructions (a prompt), and the model responds with the requested output. The key constraint is that the model’s knowledge is limited to what it learned during training; without external connections, it can’t reliably answer “current” questions like today’s news or live sports results.

That limitation is where AI agents come in. An AI agent uses the LLM as a decision-making layer for tool use. When a user asks for information the LLM can’t produce from its training data, the system can perform a “tool call” to an external data source—such as an internet search API (the transcript uses Tavily as an example). The agent identifies which tool is needed, calls it, receives the external response, and then summarizes that information into a final answer. In this setup, the agent is effectively solving a specific task end-to-end: request → tool call → external response → synthesized output.

Agentic AI expands the idea from single-task tool use to coordinated, multi-step execution. Instead of one agent handling one job, an agentic system breaks a complex goal into subtasks and assigns them to multiple agents that work together. The transcript uses an example: converting a YouTube video into a blog post. One agent can fetch the transcript, another can draft the title, a third can write the description, and a fourth can produce the conclusion. These agents can run in parallel where appropriate, then their outputs are combined into the final blog. Crucially, agents can also pass information between each other—for instance, the description-writing agent may need the title produced by another agent.

Agentic AI also allows human feedback to be inserted into the workflow, giving people a way to review, correct, or steer outputs during execution. The practical takeaway is that AI agents focus on completing a particular task by calling external tools when needed, while agentic AI orchestrates multiple agents to collaborate on a larger workflow toward a shared goal. Understanding that distinction matters because it determines how systems are designed: prompt-driven generation alone, tool-augmented task completion, or multi-agent coordination for complex real-world processes.