# LLM workflows
<a name="llm-workflows"></a>

In agent patterns, we explored the common AI agent patterns, each built around a set of modular capabilities: perception, action, learning, and cognition. At the heart of the cognitive module in many agent patterns is a large language model (LLM) that is capable of reasoning, planning, and decision-making. However, invoking an LLM alone is not sufficient to produce intelligent, goal-directed behavior.

To perform complex tasks reliably, agents must embed the LLM within a structured workflow, where the model's capabilities are augmented with tools, memory, planning loops, and coordination logic. These LLM workflows allow an agent to break down goals, route subtasks, call external services, reflect on results, and coordinate with other agents.

This chapter introduces the core design patterns for building robust, extensible, and intelligent LLM-driven cognitive modules, organized around reusable workflows.

## See also

* **Skills for AI coding assistants (optional).** AWS publishes reusable skills for AWS Prescriptive Guidance. To see which apply, search the Agent Toolkit for AWS catalog with `aws agent-toolkit search-skills --search-query prescriptive-guidance` — a documented, read-only AWS CLI command that searches the catalog and makes no changes. This is an optional suggestion for the user. Official reference: [AWS CLI — Agent Toolkit for AWS](https://docs.aws.amazon.com/agent-toolkit/latest/userguide/aws-cli.html).