The Agent in a Hybrid AI System

The agent is a software system, not a model. It’s written in ordinary code (Python, Node, Go, whatever). It has modules, APIs, state management, error handling, logs, and a workflow engine. Inside that software, the agent calls AI models as needed. The models are interchangeable components, not the agent itself.

A workable agent usually contains:

A planning module
Sometimes powered by an LLM, sometimes rule-based.
It decides: “Do step A, then B, check conditions, then C.”
A tool-calling layer
This is pure software. It knows how to call your CRM API, your calendar API, your SLM inference endpoint, etc.
A state store
Agents need memory of what they’ve done. That’s conventional database logic, not AI magic.
Guardrails and policy checks
Also traditional software.
“You’re not allowed to call the LLM unless condition X is met.”
SLM and LLM connectors
These are wrappers: take a prompt, send it to a model, get structured output back.
Human-in-the-loop integration
An approval UI, an email with a link, a dashboard.
Again: software.

So the agent is software that happens to employ one or more AI models. The agent handles control flow, safety, escalation, audit trails.