How deeply does Domain-Specific Small Language Models cover agent workflow?

Demonstrates the SDK call and tool shape but stops short of detailing tool-selection or result-handling internals.

Agent workflow

As explained in Domain-Specific Small Language Models

A multi-step model-and-tool interaction pattern where the model chooses tools, receives results, and iterates toward a final answer.

Uses a language model as an active participant in a conversation: the model can call Python functions as tools, receive their results, and continue producing a response. section 12.7 section 12.4

A practical local workflow typically requires:

A running LM Studio API server with a loaded model. section 12.4

Calling a multiplication tool

A model can receive a question such as “What is the result of 123 multiplied by 456?” and use the supplied multiply function during its response.

How Domain-Specific Small Language Models covers it

Cover of Domain-Specific Small Language Models

liveBook manning.com

Read Domain-Specific Small Language Models on liveBook Get the book at manning.com

Also explained in

liveBook manning.com

AI Agents and ApplicationsAn agentic workflow is a structured, multistep application process in which a large language model orchestrates predefined components within explicit boundaries and managed state.
liveBook manning.com

Spring AI in ActionAn agentic workflow pattern is a design for organizing an AI agent's actions into a coordinated problem-solving process.

Compare how 3 books explain agent workflow →

Related concepts

AI agentuses · An AI agent is a computational system that uses artificial intelligence to interpret information, select actions, and pursue goals through interaction with an environment.
small language modeluses · A small language model is a comparatively compact language model with limited parameters or capacity and typically lower computational and storage requirements than larger models.
@Agentincludes · @Agent is an Embabel annotation that declares a class to be an agent and can assign it identifying metadata.
LangGraphimplemented by · LangGraph is a framework for building stateful, graph-structured workflows and applications, including those involving language models and agents.
agent orchestrationconfigured by · Orchestration is the coordination and sequencing of agents, services, tasks, or components to carry out a larger workflow or objective.
ReActused by · ReAct is an agent technique that iteratively interleaves reasoning, actions, and observations so results can guide subsequent actions.

This explainer was generated by Manning's knowledge graph from the text of Domain-Specific Small Language Models. The section links point to the parts of the book it draws on.