Claude-Powered Engineering Agent Cut Code Review Turnaround from 12 Hours to 1 Hour
- Claude
SERVICES
EXPERTISES
Project Context
Solution
Outcome
About Client
The client is Nightingale, a healthcare technology company specializing in value-based care. Its integration-heavy software platform relies on data from numerous external sources and services.
The company's engineering teams maintain integrations with different systems, making accurate access to project architecture, integration-specific knowledge, historical decisions, and implementation context important to everyday development and QA work.
As the platform evolved, much of this operational knowledge became distributed across Jira tickets, Confluence documentation, and individual engineers.
Business challenge
The client's engineering teams were already using Claude Code to accelerate development, but AI adoption remained fragmented.
Critical knowledge about the platform’s many external integrations was distributed across Jira, Confluence, and individual engineers, while code review, QA, and onboarding still relied heavily on manual work.
The challenge was to turn individual AI productivity gains into a shared, context-aware development workflow that could improve delivery across the whole team.
Approach
Binariks shifted AI assistance from individual developer usage to a shared engineering agent embedded into the client's existing software delivery workflow.
Instead of giving every developer another isolated coding assistant, Binariks consolidated project context from Jira and Confluence and made it available to one engineering agent capable of participating directly in development, code review, QA, and knowledge retrieval. The approach focused on four principles:
- Shared project context rather than isolated developer prompts
- Integration with existing workflows rather than introducing another standalone tool
- Human review and reassignment within the existing delivery process
- Reusable project knowledge for engineers and QA across both teams
Implementation
Ticket-to-PR Automation
Development tickets can be assigned directly to the engineering agent. The agent:
- receives the assigned ticket;
- uses available project and integration context;
- implements the requested change;
- opens a pull request in GitHub;
- performs code review and testing;
- reassigns the ticket to the originating developer.
This brings AI directly into the existing development lifecycle rather than requiring developers to copy tasks manually between tools.
Automated Revision Workflow
When a change is required, developers can tag the agent in a ticket comment and request a revision.
The agent receives the additional context and continues working within the same delivery workflow.
Confluence-Grounded Project Knowledge
The agent is connected to the client's Confluence knowledge base. Developers and QA specialists can query it about:
- project architecture;
- integrations;
- implementation history;
- existing project knowledge.
This reduces repeated knowledge discovery and dependence on individual engineers for common project questions.
AI-Assisted Code Review
Developers implementing tickets manually can request an independent review from the engineering agent. This adds an AI review stage before human review and helps surface issues earlier in the pull-request workflow.
QA Support
QA specialists can provide use cases for the agent to execute and then evaluate the resulting output. This extends the agent beyond coding into software validation workflows.
Value Delivered
The Claude-powered agent turned fragmented, individual AI usage into a shared engineering workflow integrated with the tools the team already used.
Since deployment, the agent has handled 1,420 tickets end-to-end and is used by 15 engineers and 5 QA specialists across two teams.
- 19h → 11h average cycle time from ticket assignment to PR ready for review
- 12h → 1h code review turnaround
- 16h → 4h onboarding time for developers new to the integrations codebase
- 90% of PRs now receive an agent review before human review
Beyond the measured improvements, the agent created a shared source of project knowledge. Developers and QA can query architecture, integrations, and project history directly instead of repeatedly searching documentation or relying on senior engineers.
It also extended AI support beyond coding: the agent can implement Jira tickets, open pull requests, review manually written code, execute QA use cases, process revision requests, and return completed work to developers within the existing delivery workflow.
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