Turn Zendesk Tickets into Actionable Bug Reports Automatically

Watari converts a Zendesk support ticket into a structured bug report, maps it to a specific file and function in your GitHub repository, and opens a draft pull request — without a human copying and pasting anything. The pipeline runs on every inbound ticket, filters out non-bugs before engineering ever sees them, and closes the loop back to the originating Zendesk ticket when a fix ships.

Why the Manual Path Breaks Down

The manual path from a Zendesk ticket to an engineering-ready bug is almost always lossy. A support agent reads a ticket, strips out the customer's original words, rewrites the issue in their own shorthand, and pastes a summary into Linear or Jira. Repro steps get dropped. Severity is guessed. The actual error message — buried in a screenshot or the third reply in a conversation thread — never makes it to the engineer.

On the engineering side, the backlog fills with vague titles like "checkout not working" that require a round-trip back to support before anyone can begin triaging. That round-trip costs time on both sides, and the customer is still waiting.

Watari replaces that copy-paste chain with a deterministic pipeline. The same fields — severity, repro steps, expected vs. actual behavior, customer impact — are extracted from every ticket using the same logic, regardless of which support agent handled the conversation.

How to Connect Zendesk and Start the Pipeline

Connecting Zendesk takes a single OAuth flow. No shared API keys, no manual webhook configuration, no coordination with your Zendesk admin outside of the authorization step.

Here is the exact sequence:

1. Go to Settings → Integrations in your Watari workspace and click Connect next to Zendesk.
2. Authorize the OAuth flow. Watari registers its own webhook on your Zendesk account during this step — you do not configure the webhook manually.
3. Verify the connection. Watari immediately begins listening for ticket.created and ticket.updated events from your Zendesk account.

Every inbound webhook payload is HMAC-signature verified on receipt. Payloads that fail signature verification are rejected with a 401 and never retried by Watari. Your access and refresh tokens are AES-256-GCM encrypted before they are written to the database and are never logged. See the Support tools — Zendesk and Intercom setup guide for the full OAuth walkthrough.

If you want to dry-run the pipeline before it touches your live support queue, connect a Zendesk sandbox subdomain first. The pipeline behaves identically on sandbox traffic.

How Watari Extracts a Structured Bug from a Ticket

Once a ticket arrives via webhook, the extraction step pulls a structured record from the full conversation — not just the subject line, and not just the first message.

Watari reads everything the ticket contains:

• The ticket subject and body
• The full conversation thread, in order, with author labels and timestamps
• Attachments — screenshots, screen recordings, log files
• Provider metadata: status, tags, priority, requester name and email, custom fields

From that raw material, the extraction model produces a structured bug with consistent fields:

• Title — a concise, engineering-readable summary
• Severity — P1 through P4, inferred from customer impact signals in the conversation
• Repro steps — numbered, actionable, drawn from what the customer actually described
• Expected vs. actual behavior — the two fields engineers need to understand what broke
• Customer impact — how many users are affected and in what way
• Customer's original words — verbatim, preserved so context is never lost

This is meaningfully different from a human rewrite. The extraction model reads every attachment, including screenshots, so an error message visible only in a screen recording is captured in the structured output rather than silently dropped.

Not every ticket contains a bug. Billing questions, feature requests, and how-to questions all arrive through Zendesk. Watari applies a confidence gate at the extraction step: if the ticket does not contain sufficient signal to produce a high-confidence structured bug, it does not advance through the pipeline. Only tickets that clear that gate proceed to code mapping — which means your engineering backlog sees real bugs, not support noise.

For the full extraction logic, see Ticket to bug.

How Watari Maps the Bug to a File and Function

Once a structured bug clears the extraction confidence gate, the mapping step finds the files and functions in your repository most likely to be responsible.

This requires an index of your repository. When you install the Watari GitHub App and select a repository, Watari:

1. Clones the repository
2. Parses every supported source file using tree-sitter — TypeScript, JavaScript, Python, Ruby, Go, and Rust are all supported
3. Stores function-level metadata alongside vector embeddings of each chunk in a pgvector index with HNSW (m=16, ef_construction=64)

The first index of a typical SaaS codebase completes in a few minutes. After that, the index updates incrementally on every push to your default branch.

When a bug arrives for mapping, Watari embeds the structured bug description, runs a nearest-neighbor search over the pgvector index, retrieves the top candidates, and reranks them with a language model that reads the actual source code before scoring relevance. The result is a ranked list of code_locations — specific files and functions — each carrying a confidence score.

A bug qualifies as a Mapped Bug only when both gates clear:

• The extraction step produced a high-confidence structured bug (ai_confidence ≥ 0.7)
• At least one code location was matched with high confidence (code_locations[].confidence ≥ 0.7)

Bugs that do not clear both gates do not fire the billing meter and do not proceed to PR generation. They are visible in the dashboard for manual review, but they do not pollute your engineering backlog with speculative mappings.

How the Draft PR Gets Opened — and Synced to Linear or Jira

Once a bug has confirmed code locations, Watari opens a draft pull request on the relevant branch in GitHub. The fix loop reads the source files identified during mapping, reads the bug description and repro steps, and proposes a change. The PR is opened in draft state — it is never merged automatically.

The PR description includes:

• The structured bug fields (severity, repro, expected vs. actual)
• The specific code locations that were matched
• A link back to the originating Zendesk ticket

That last item matters. The loop closes: an engineer reviewing the draft PR can click through to the original Zendesk conversation and read the customer's exact words.

If your team routes work through Linear or Jira, Watari can create a linked issue in either system at the same time the draft PR opens. Slack notifications are also available. All three routing destinations — Slack, Linear, Jira — are bundled into your plan and never add to your Mapped Bug count.

To configure routing, go to Settings → Integrations and connect your preferred destinations. The Routing and notifications guide covers the Slack channel-invite step and the Jira ADF requirement.

For the full PR generation and iteration logic, see Code to PR.

What the End-to-End Flow Looks Like

Putting it together, the path from a Zendesk ticket to an actionable engineering artifact looks like this:

1. A customer submits a ticket in Zendesk
2. Watari receives the ticket.created webhook, verifies the HMAC signature, and queues the ticket for extraction
3. The extraction model reads the full conversation and attachments, then produces a structured bug — or routes the ticket out of the pipeline if it is not a bug
4. The structured bug is embedded and matched against the pgvector index; the top candidates are reranked against actual source code
5. If both confidence gates clear, the bug is recorded as a Mapped Bug, the billing meter fires once, and the fix loop begins
6. A draft PR opens in GitHub with the proposed fix and a link back to the Zendesk ticket
7. Linear or Jira issues and Slack notifications are created if routing is configured
8. When the fix ships, Watari publishes a customer-facing RCA back to the originating ticket

Every action after step 5 — PR generation, PR iteration, RCA publication, issue tracker sync — is bundled into your plan. The meter fires once per Mapped Bug, not once per action.

Getting Started

The 10-minute quickstart walks through connecting Zendesk, installing the GitHub App, and seeing your first Mapped Bug. For teams evaluating the pipeline before going live, connecting a Zendesk sandbox subdomain first is the lowest-risk starting point.