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# From Weeks to Hours: Inside Wix’s Autonomous Bug-Fixing System

**[Wix Engineering](https://daily.dev/sources/wix_eng)** · 10 min read · 2 upvotes · 0 comments

## Summary

Wix Engineering describes the architecture behind Wix Orchestrator, an internal multi-agent system that autonomously investigates support tickets and produces production-ready pull requests. The system uses four specialized agents (Detector, Enricher/Wix Octocode Research, Coder, Reviewer) coordinated by an orchestrator, with a human engineer always reviewing the final fix before it ships. The core insight is that context quality, not model choice, determines fix quality: the Enricher combines static code analysis (OctoCode), database queries (Trino), live logs (Grafana), and documentation into a structured plan for the Coder agent. Schema validation with retries cut inter-agent error rates from roughly 50% to under 1%. Open problems remain around ticket classification and capturing undocumented organizational/institutional knowledge that isn't visible in code or logs.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.wix.engineering/post/from-weeks-to-hours-inside-wix-s-autonomous-bug-fixing-system>

## Questions this post answers

### How did Wix reduce the time to fix production bugs from weeks to hours using AI agents?

Wix built a system called Wix Orchestrator made of four specialized agents: a Detector that classifies incoming support tickets, an Enricher (Wix Octocode Research) that gathers static code context and live runtime data using OctoCode, Trino, and Grafana, a Coder that implements fixes in an isolated Docker container and opens a PR, and a Reviewer that independently evaluates the PR before a human approves it for production.

_Teams designing similar agent pipelines can follow ongoing architecture writeups like this on daily.dev._

### How do you reduce error rates when chaining multiple AI agents together in a pipeline?

Validating output schema at every handoff between agents and automatically retrying with the original error and context sent back to the same agent dropped the inter-agent error rate from roughly 50% to under 1%. Idempotency checks also prevent duplicate work when a ticket is already resolved or a PR is already in flight.

_Engineers building multi-agent pipelines can track practical reliability techniques like this on daily.dev._

### What is Specification Driven Development in the context of building AI coding agents?

Specification Driven Development means producing a detailed Markdown spec describing what is being built, why, and how before any code generation happens, then having an independent agent review that spec before a coding agent touches the repository. The coding agent receives the reviewed spec as its primary input rather than a vague task description, and framing it as the future maintainer improves error handling and logging in its output.

_Developers refining agent workflows for code generation can follow techniques like this on daily.dev._

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---

Tags: [#ai-agents](https://daily.dev/tags/ai-agents), [#architecture](https://daily.dev/tags/architecture), [#context-engineering](https://daily.dev/tags/context-engineering)

[View this post on daily.dev](https://daily.dev/posts/from-weeks-to-hours-inside-wix-s-autonomous-bug-fixing-system-fekzry59w)

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