AI Agents vs. LLM Workflows

People often get confused between AI Agents and LLM workflows.

Understanding the distinction between AI agents and LLM (Large Language Model) workflows is key to choosing the right automation approach for your use-case.

What is an AI Agent?

• An autonomous software entity that can perceive, reason, and act to achieve goals.
• Handles complex, multi-step tasks by making decisions and orchestrating tools/services.
• Can adapt to changing environments and user requests.

What is an LLM Workflow?

• A predefined sequence of steps or prompts executed by a large language model (like GPT-4 or Claude).
• Typically linear and deterministic: each step follows the previous one.
• Great for repeatable, well-defined processes (e.g., data extraction, summarization, formatting).

Key Differences

Feature	AI Agent	LLM Workflow
Autonomy	High (makes decisions)	Low (follows set steps)
Adaptability	Can handle unexpected situations	Rigid, limited to defined flow
Use of Tools/Services	Orchestrates multiple tools/services	May call tools, but generally in fixed order
User Interaction	Can ask clarifying questions, replan	Usually no dynamic interaction
Example Use Case	"Book a flight and notify me on Slack"	"A button on a web page to summarize a document"

When to Use Each

• Use an AI Agent when:

  • The problem is vague and complex - requires decision-making, adaptation, or chaining multiple services.
  • The process may change based on context, user input or something else.

• Use an LLM Workflow when:

  • The problem is small, and requires a repeatable, well-defined sequence.
  • You want predictable, consistent output.
  • The process does not require dynamic decision-making.

In Saiki

Saiki CLI spins up a powerful AI Agent you can use for solving complex problems

Choosing the right tools for the job helps you get the most out of Saiki's automation capabilities.