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Agents decide on their own when a particular routine is pertinent in the context of a user interaction. Therefore, it is important that the appropriate routine name, description, and its inputs are provided, as well as sufficiently described using natural language. To use Agent Routines, the large language model you choose must have been trained or fine-tuned for function calling.
Managing Agent Routines
To create, edit, or remove agent routines, simply switch to the Routines tab in the IDE when working with an agent, and then use the appropriate toolbar icons.
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Mark a routine private if it is intended to be used by other routines defined within the agent only and not by the agent itselfor for invocation from from Agent Instructions. When a routine is marked private, the AI model itself does not receive any information about it and is unable to initiate a call to that routine directly.
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Defining Output may not require the same level of granularity since the LLM can potentially infer the meaning of the data from the output response itself. For example, if a list of records is sent as output, it may not be necessary to define each record column when the data itself contains the column names as part of its JSON response.
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Access to any configured databases for retrieval, updates, summarization, and more
Web Service Consumption
Calling ILE programs and service programs
Calling Operating System Commands
Custom SQL or Node.js logic
Conditional and repeating blocks
And more
More details about using Low-code can be found here.