AI Agents

Keep hearing that buzzword "Agentic" and wondering what the hell it means?

It's not a user facing "agent" like customer service.

What makes an LLM Agent Agentic is :

  1. Return control to the user

How it works:

Agent examples :

It's important to recognize that the tool call result may not be the end of the response—the LLM looks at what it got back, and predicts what the next token should be—some “thinking”, or commentary, or summary, or another tool call, or a “end of turn” token.

You can end up with a very long chain of tool calls.

Memory is often a tool call. Save memory, search memories, etc.

There can be many layers of agents, or multiple agents working as a team—there are tons of different architectures.

Importantly there’s a distinction between a single LLM token sequence of text, and tool calls simply executed by the ConversationContainer, vs an orchestrator (another type of ConversationContainer) that runs more deterministic logic around the calls (cf Claude code hooks),

Or multiple models validating or reacting to the inputs and outputs of the tool calls (is this an approved tool call? Is there a security risk hiding in there?)