How do you turn raw error payloads into clean, meaningful ticket summaries without touching a line of code?
Engineering teams rely on fast, accurate error context to resolve issues efficiently. Rollbar does a great job capturing rich payload data at the moment an error occurs, but getting that data into your issue-tracking workflow can still require manual triage—especially if you want clean, human-readable summaries in Jira.
In this guide, we’ll walk through how to automate the entire flow using:
- Rollbar Webhooks to push error data in real time
- Zapier to receive and process the payload
- Jira to automatically create actionable issues
The result:
An automated pipeline that converts noisy error payloads into well-structured, concise Jira tickets your team can actually use.
Why Automate This Workflow?
Rollbar payloads are extremely detailed, containing stack traces, request metadata, environment information, and custom attributes. This is invaluable for debugging—but far too verbose for a Jira summary.
Most teams want something like:
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A short, readable title
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Key error details
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Reproduction hints
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Relevant request context
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Severity and frequency signals
By connecting Rollbar → Zapier → Zapier AI → Jira, we can automatically:
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Receive webhook payloads instantly
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Parse and summarize them using AI
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Generate standardized Jira issues
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Reduce manual triage time to zero
Step 1: Configure a Webhook in Rollbar
In Rollbar, navigate to
Project Settings → Notifications → Webhooks
Paste your Zapier Webhook URL (we’ll generate this in the next step)
Choose the triggers you want, most commonly:
- New Items
- Reactivated Items
- Critical/High severity errors
Rollbar will now send full JSON payloads to Zapier whenever the selected events occur.
Step 2: Create a Zap and Receive the Webhook in Zapier
In Zapier, create a New Zap.
For the trigger, select Webhooks by Zapier:
- Choose Catch Hook
- Zapier will generate a unique URL—paste this back into Rollbar’s Webhook settings
- Send a test notification from Rollbar so Zapier can sample the payload
You now have the full Rollbar error payload flowing into your Zap.
Step 3: Use Zapier AI to Extract & Summarize Payload Data
This is where the magic happens.
Add an Action Step → “AI by Zapier”
Feed the entire Rollbar payload from the previous action into the AI input box
Provide a detailed prompt to process the data. For our example, we used:
Create a concise yet comprehensive ticket summary and description detailing the error based on an analysis of the provided JSON payload. The summary must be articulate, well formatted so it is easy to read with appropriate whitespaces, and limited to a maximum of 1000 characters, ensuring clarity and precision and ease of understanding. The summary must emphasize actionable remediation steps to address the identified error. Don't add the link to Rollbar in the summary.
In addition, copy the ticket title into the designated title output field and transfer the UUID to the uuid output field.
Generate the link to the rollbar item using uuid in the link output field, with the format: https://rollbar.com/item/uuid/?uuid={uuid} where {uuid} is the uuid from the payload.Make sure you add the output fields indicated in the prompt.
Zapier AI will now reliably turn messy error payloads into readable, standardized fields you can plug directly into Jira.
Step 4: Create the Jira Ticket Automatically
Add a new action: Jira Software Cloud → Create Issue
Map your AI-generated fields from the previous action to the corresponding fields in Jira. For our example, we added the summary and link to the Jira description field and kept the original title of the error.
After you publish the Zap, every time Rollbar detects an error, your Zap will create an enriched, clean ticket that your engineering team can immediately act on.
Final Thoughts
This automation turns a once-manual triage step into a seamless, intelligent workflow. Rollbar captures the full error context. Zapier AI extracts what matters. Jira receives actionable, standardized tickets.
Your developers spend less time sifting through raw payloads—and more time fixing what matters.
