Structured Output from Flow Agent Actions in Winter '26 : [email protected] (Bob Buzzard)

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October 13, 2025
Structured Output from Flow Agent Actions in Winter '26
by: [email protected] (Bob Buzzard)
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### Summary of the Content The author reflects on their experience with the new features introduced in the Credera Winter '26 release webinar, particularly focusing on the custom agent actions in Salesforce. Initially, the author was confused about how these actions worked, especially regarding complex data types. After upgrading to the Winter '26 version, they tested the functionality using a simple screen flow that incorporated an AI Agent Action, specifically Copilot for Salesforce. The author successfully created a flow that retrieves opportunities linked to an account, processes them into a JSON structure, and uses an AI Agent to calculate the total amount of these opportunities. They discovered that the Salesforce platform automatically converts the output from the AI into a structured format, eliminating the need for manual coding of complex data types. The conclusion emphasizes the ease with which low-code developers can now utilize AI to transform unstructured data into structured formats, streamlining processes without the need for extensive coding knowledge. However, the author acknowledges that their example was simplistic and suggests that AI should be used for more complex tasks, such as analyzing customer sentiment, rather than basic calculations. ### Key Details - The author initially struggled to understand the new feature. - They successfully tested the AI Agent Action without prior coding effort. - The platform converts unstructured AI output into structured data automatically. - Low-code solutions now enable developers to handle tasks previously requiring Apex coding. - The author advises using AI for more complex processes rather than simple calculations. ### Additional Context This feature represents a significant advancement in Salesforce's capabilities, allowing users with minimal coding experience to leverage AI for data processing. The integration of AI into low-code platforms is a growing trend, enabling businesses to streamline operations and enhance decision-making through sophisticated data analysis. ### Hashtags for SEO #Salesforce #AI #LowCode #Automation #DataProcessing #Credera #Winter26 #AIAgent #CustomerSentiment #TechInnovation


Image created by ChatGPT 5o based on a prompt by Bob Buzzard


Introduction

When preparing for the Credera Winter '26 release webinar, the release notes for this feature gave me significant pause for thought. Not because it was an awesome change that I'd been waiting ages for, nor that it was something out of left field that I couldn't wait to try. Instead it was because I didn't understand how it worked. The release notes talked about custom agent actions returning specific fields, so did that mean it was the action itself that returned complex data types? There was only one way to find out.

Giving it a Go

Once I'd waited for my Agentforce developer edition to be upgraded to Winter '26 I was able to try out this new functionality. In order to understand how much effort I had to put in around my actions, I started off putting in zero effort. Masterful inactivity has always served me well!

The first thing I tried was a simple screen flow, with the first element an AI Agent Action, as this was the key to defining structured output. 


First crack out of the box and I have a winner! Without even having to create a custom action, Copilot for Salesforce is available as an AI Agent Action. Clicking into this showed that the new Structured Output functionality was available with this action.


After a few false starts (the AI Agent Action wouldn't accept collections of records etc) I had a simple flow that would take in an account Id, retrieve the opportunities associated with the account, convert them to a simple JSON structure and ask the AI Agent to calculate the total amount of the opportunities. For my AI Agent Action, I give a relatively simple prompt grounded with the opportunity information.

and for my structured output, I specified a single field - the total amount.


Executing this in debug mode gave me the answer to my first question - did that mean it was the action itself that returned complex data types? No, in this case I'm simply sending a request to an LLM and it will give a text response. The Salesforce platform handles the conversion to structured output.



And I can then use that structured output like I would any other complex object, in this case in a screen displaying the total.


Note that the "container" for the structured output is actually a Dynamic Apex Class that you can access through setup:


Conclusion


To answer my earlier question:

Did that mean it was the action itself that returned complex data types? 

It did indeed - there was no need for me to create anything outside of the action for the results to be stored in, I just defined the field and used natural language to explain what should be stored in there. The platform created an Apex class to store the information and populated it from the LLM output.

This is pretty cool - it allows low code to convert the unstructured output from an LLM into structured output for use in downstream processing. Prior to this feature an Apex developer would likely have been needed to help, but now it can all be handled by a low coder.

Of course this is a terrible example. If I really wanted the total, why wouldn't I calculate it while iterating the records, rather than pulling together a bunch of text and then incurring the expense and time overhead of an LLM callout - Agents Augment Automation, they don't replace it. 

I chose this example as it was easy to put together to prove the concept. In the real world I'd only use LLMs to handle tasks that regular automation couldn't, like figuring out the customer sentiment from a bunch of activities associated with the Opportunities. More work to set that up though, and harder to explain.

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October 13, 2025 at 10:16PM
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