Praful Jangid

Mar 7, 2026

(0 votes)

Lessons from Building Production-Ready Opal Tools

AI tools are becoming a normal part of modern digital platforms. With Optimizely Opal, teams can build tools that automate real tasks across the Optimizely platform.

Creating a basic Opal tool is fairly straightforward. You define a tool, connect it to an API or some business logic, and allow an agent to call it. Many tutorials stop there.

But in real projects, things are rarely that simple.

External APIs fail. Data might be incomplete. Requests might take longer than expected. If these situations are not handled properly, your Opal workflows can break or produce incorrect results.

In this article, we will look at a few important practices that help you build production-ready Opal tools. These include:

Proper error handling
Logging and observability
Structured responses
Performance considerations
Security practices

These are the same things we normally think about when building enterprise applications.

Understanding How an Opal Tool Works

Before going deeper, it helps to understand the typical flow of an Opal tool.

A simplified flow usually looks like this:

User Request
     ↓
Opal Agent
     ↓
Opal Tool
     ↓
External Service or API
     ↓
Response returned to the Agent

For example, imagine a marketer asking Opal:

"Check if the price updates for the latest products are correctly applied."

The agent might call a Price Validation Tool, which then:

Reads the expected price data
Calls a product API
Compares the values
Returns the result

This looks simple, but many things can go wrong along the way. That’s why good design is important.

Handling Errors Properly

One of the most common issues in production systems is unhandled errors.

Let’s say your tool calls a product API. What happens if:

the API is temporarily unavailable
the response takes too long
the product does not exist
the returned data is incomplete

If your tool simply crashes or returns a generic error, the agent will not know what to do.

A better approach is to return clear and structured error information.

Example response:

{
  "status": "error",
  "errorType": "API_TIMEOUT",
  "message": "The product service did not respond within 5 seconds.",
  "suggestedAction": "Retry the request."
}

This helps the agent understand what happened and possibly retry or suggest another action.

Example Scenario

Imagine a tool that checks whether a product exists in the system.

Instead of returning something vague like:

Error occurred

Return something useful:

{
  "productId": "P12345",
  "status": "not_found",
  "message": "Product does not exist in the catalog."
}

This makes troubleshooting much easier.

Returning Structured Data

AI agents work much better when the tool returns structured data instead of plain text.

For example, consider a tool that verifies product prices.

Bad example:

The price seems different from what we expected.

Better example:

{
  "sku": "SKU-234",
  "expectedPrice": 19.99,
  "currentPrice": 21.49,
  "status": "price_mismatch"
}

This format allows the agent to:

detect mismatches automatically
trigger additional checks
generate useful reports.

Structured responses also make it easier to reuse the tool in different workflows.

Logging and Observability

When something goes wrong in an AI workflow, debugging can be difficult if you do not have good logs.

Logging helps you answer questions like:

Which tool was executed?
What input was provided?
How long did it take?
Did the API call succeed?

A simple logging format might look like this:

Timestamp: 2026-03-07 10:12:04
Agent: ProductValidationAgent
Tool: PriceValidationTool
ExecutionTime: 2.4 seconds
Status: Success

If an error occurs:

Timestamp: 2026-03-07 10:15:10
Agent: ProductValidationAgent
Tool: PriceValidationTool
ExecutionTime: 5.1 seconds
Status: Failed
Error: API Timeout

This information becomes extremely helpful when diagnosing problems in production.

Example: SKU Price Validation Tool

Let’s look at a simple but practical example.

Suppose a team updates product prices in bulk and wants to verify that the updates were applied correctly.

A Price Validation Tool could follow these steps:

Receive a SKU number
Fetch the expected price from a spreadsheet or internal system
Call the product API
Compare the values
Return the result

Example response:

{
  "sku": "SKU-987",
  "expectedPrice": 24.99,
  "systemPrice": 24.99,
  "status": "verified"
}

If there is a mismatch:

{
  "sku": "SKU-987",
  "expectedPrice": 24.99,
  "systemPrice": 26.99,
  "status": "mismatch"
}

An Opal agent could then generate a report for the operations team.

Improving Performance

When AI agents run workflows, they may call multiple tools in sequence.

If each tool takes several seconds, the entire workflow could become slow.

Here are a few simple ways to improve performance.

Use caching

If the same data is requested multiple times, cache it temporarily instead of calling the API repeatedly.

Example:

If the tool checks the same product data multiple times during a workflow, store the response for a few minutes.

Reduce unnecessary API calls

If possible, fetch multiple items in a single request.

Instead of:

Call API for Product A
Call API for Product B
Call API for Product C

Try:

Call API once for Products A, B, and C

Run independent tools in parallel

Some checks can happen at the same time.

Example product validation workflow:

Product Validation Workflow
 ├─ Price Validation Tool
 ├─ Inventory Check Tool
 └─ Search Index Check Tool

Since these checks are independent, they can run in parallel, reducing overall execution time.

Security Considerations

Security is always important when tools interact with external systems.

A few basic practices can help avoid common problems.

Protect API credentials

Never hardcode credentials in the tool code. Instead, use environment variables or secure configuration systems.

Validate inputs

Agents may pass unexpected inputs. Always validate parameters before calling external services.

Example validation:

Check if SKU format is valid
Ensure price values are numeric
Prevent empty inputs

Limit tool permissions

Each tool should only access the resources it needs.

For example:

A content validation tool should not modify content.
A reporting tool should not update product data.

This reduces the risk of accidental changes.

Monitoring and Continuous Improvement

Once tools are running in production, it is useful to track their performance over time.

Some helpful metrics include:

tool execution time
success vs failure rate
number of API errors
most frequently used tools

These metrics can highlight areas where improvements are needed.

For example:

If a tool frequently fails due to timeouts, you might need to improve the API performance or add retry logic.

Final Thoughts

As AI becomes more integrated into enterprise platforms, the quality of the underlying tools becomes increasingly important.

A well-designed tool should not only complete its task but also:

handle failures gracefully
provide clear responses
log useful information
perform efficiently
follow security best practices

By applying these practices while building tools for Optimizely Opal, teams can create AI workflows that are reliable enough for real business operations.

Feel free to share your thought as an outcome of yout experience with implementing the Optimizely Opal Tools.

Thanks

Mar 07, 2026

Comments

Please login to comment.