Country
USA
Industry
Healthcare
Timeline
2026
The client is a healthcare company operating a software platform that depends on data from numerous external sources and services.
Its 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.
Critical integration knowledge was scattered across Jira, Confluence, and individual engineers, forcing developers to repeatedly rediscover integration quirks.
Developers used Claude Code independently , without shared team context or a consistent approach to AI-assisted development.
Manual code reviews and QA created bottlenecks , limiting the benefits of faster AI-assisted development.
New developers depended heavily on senior engineers for project knowledge, prolonging onboarding.
Binariks shifted AI assistance from individual developer usage to a shared engineering agent embedded into the client's existing software delivery workflow.
Binariks built a single engineering agent connected to Jira and Confluence, with the agent grounded in the client's Confluence knowledge base.
The approach focused on four principles:
Ticket-to-PR Automation
Development tickets can be assigned directly to the engineering agent. The agent:
This embeds the agent directly into the existing development workflow rather than operating as a separate assistant used by individual developers.
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:
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 give the agent use cases to execute and then review the results. This extends the agent beyond coding into software validation workflows.
The Claude-powered agent turned fragmented, individual AI usage into a shared engineering workflow integrated with the tools the team already used. It is now used by 15 engineers and 5 QA specialists across two teams.
tickets handled end-to-end
code review turnaround
ticket-to-PR cycle time
The agent created a shared way to access project knowledge.
Developers and QA can query architecture, integrations, and project history directly instead of repeatedly searching documentation or relying on senior engineers for many project questions during onboarding.
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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