Run Javascript code on Agent Loop

We have recently introduced support for Code interpreters inside of Azure Logic Apps Agent Loop, extending the support we had for Python.

When partnered with a LLM, this allow builders to express their goals or intents via natural language and obtain executable results. These capabilities become powerful in the areas of data analysis, visualizations, validations and transformations. Our first language supported for code interpreter is JavaScript, with other languages following later.

Historically, customers have had concerns about an LLM performing data analysis, calculations and transformations due to context window exhaustion which can lead to hallucinations. Code interpreters help in this regard as they can perform this analysis without filling up context windows and providing more reliable results.

You can see the code interpreter with JavaScript in action in this video from Kent Weare. After watching the video, you can deep dive in the details.

How it works

When Agent Loop evaluates code generated by an AI agent (for example, through a code interpreter), we run it inside a V8 isolate using the isolated‑vm library.

V8 is the JavaScript engine that powers Node.js and Chrome—it’s what actually executes JavaScript code. An isolate is a lightweight, independent environment within V8, with its own memory and execution context.

Running code inside an isolate gives us strong separation from the host runtime. Each execution has its own memory (“heap”) and cannot directly access the host’s memory, file system, or network. This helps ensure that agent-generated code stays contained and doesn’t interfere with the rest of the system.

This approach is not intended to be a full security sandbox, and we don’t treat it as safe for fully untrusted code. However, it provides meaningful defense-in-depth:

• Memory usage is limited per isolate, preventing a single execution from consuming all available resources
• Execution can be bounded with timeouts, allowing us to terminate long-running or infinite loops
• Failures are isolated, so crashes in agent-generated code won’t bring down the runtime process

In practice, this is about reducing blast radius. By isolating execution and enforcing limits, we make sure that code—regardless of whether it’s generated by a user or an AI agent—cannot disrupt the engine that runs it.

Use case: Expense Validations

To help illustrate, this capability, let’s take an accounts payable example built in Logic Apps Standard. Zava uses a 3^rd party expense application to capture employee expenses. The 3rd party expense application will export transactions in CSV format.

Zava has some very specific business validations that need to execute before the expenses can be processed by the ERP. To solve this problem, we will build an agentic business process in Logic Apps that includes our new JavaScript code interpreter.

Our code interpreter will be able to ingest and parse our CSV file and then apply our business validations for us. The outcome is a report that identifies both valid and invalid transactions.

Prior to uploading to the ERP (Dataverse), we will route our request to a human in the loop process for their oversight. This allows for additional control as unwinding in an ERP is always a tedious task.

Below, is a picture of our solution. Within it we can see both deterministic steps before and after our Agent action. Within our agent action, we have tools that will help our agent address our company objectives. These tools include calling a batch API to upload valid expense records to Dataverse. Another tool that will take care of uploading invalid records to a different table, our human in the loop action to seek approval from our human stakeholder and a tool that will help us obtain business knowledge from SharePoint.

You might be asking, ok where does the code interpreter come in? Within our Agent action, we will discover a toggle that allows you to enable it. The code interpreter gets invoked based upon instruction in the model.  Here is a subset of the prompt from this workflow that describes how to invoke the code interpreter.

For example:

### Step 2 -- Parse and Validate
The expense CSV data is available from the Get_file_content action. Use code interpreter to parse ALL rows from the CSV. For each row, normalize:

Category: title case
- Amount: decimal number
- SubmittedDate: ISO 8601 format (e.g. "2026-01-05T00:00:00Z")
- ReceiptAttached: convert "Yes"/"No" to true/false

Then apply the business rules from Step 1 to classify every record as VALID or INVALID.

You won’t see the code interpreter modelled as a tool within our agent action, but we see the execution outcome within our run history. In the following screenshot we can see this illustrated. Within our agent action, we can see that we are on our 4^th turn and we have executed the code interpreter action. In the code window, we can see the code that was generated for our us. This is the result of the LLM working together with the code interpreter to generate and execute this code.

Note: In this scenario, we are dynamically generating this code at runtime. This allows for ultimate flexibility if we have different source inputs and we are relying upon the LLM and code interpreter to adapt to these fluid inputs. If we were interested in a more deterministic approach we can also pass pre-written code into this action where it can also execute. This will result in less flexibility, but more deterministic behavior.

Running JavaScript code in Logic Apps Consumption Agent Loop

Logic Apps Consumption has a slightly different architecture to Logic Apps Standard. In Logic Apps Standard, we offer dedicated compute and storage for customers which provides workload isolation across customers.

When it comes to Logic Apps Consumption, we provide a multi-tenant offering allowing customers to take advantage of a lower price point due to shared resource utilization. In order to allow customer isolation, customers need to have an integration account attached to their consumption workflow. This will allow the code interpreter to run in isolated compute thus avoiding any potential disruptions to other customers.

You can provision an Integration Account by searching for Integration Accounts at the top of the Azure portal. You can select any of the SKUs available, including the Free SKU for non-production/non-SLA scenarios.

With an Integration Account created, we can associate this Integration Account with our consumption logic app by clicking on Settings – Integration Account.