Agents
Tool Chaining
A pattern where an AI agent uses the output of one tool call as the input for the next, building a multi-step pipeline of actions.
In depth
A pattern where an AI agent uses the output of one tool call as the input for the next, building a multi-step pipeline of actions. For example, an agent might search for a file (tool 1), read its contents (tool 2), modify the code (tool 3), and run tests (tool 4). Each step depends on the previous result. Tool chaining is the mechanism that turns single-tool agents into capable, multi-step problem solvers.
Example
For example, an agent might search for a file (tool 1), read its contents (tool 2), modify the code (tool 3), and run tests (tool 4).
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FAQ
Common questions
What is Tool Chaining?
A pattern where an AI agent uses the output of one tool call as the input for the next, building a multi-step pipeline of actions.
Why does Tool Chaining matter for AI developers?
Tool Chaining sits in the Agents part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Where can I learn more about Tool Chaining?
Developers Digest publishes tutorials and videos that cover Agents topics including Tool Chaining. Check the blog and YouTube channel for hands-on walkthroughs.
Related
Related terms
Agents
Agent Handoffs
A multi-agent mechanism where one agent passes control of the conversation to a more specialized agent, rather than calling it as a subroutine.
Agents
tau-bench
A benchmark from Sierra that tests agents in multi-turn customer-service conversations, where a language-model-simulated user talks to an agent that has domain API tools and a written policy to follow.
Agents
Task Decomposition
The process of breaking a complex goal into smaller, manageable sub-tasks that an agent can execute individually.
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