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
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 terms
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Task Decomposition
The process of breaking a complex goal into smaller, manageable sub-tasks that an agent can execute individually.
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Backpressure (Agent)
A flow-control mechanism that prevents an agent pipeline from overwhelming downstream systems.
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Tool Use
A model capability where the LLM can invoke external tools - running code, searching the web, reading files, calling APIs - as part of generating a response.
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