DevPlaza, an emerging leader in developer tooling innovation, set out to solve one of the most persistent challenges in modern software engineering: identifying and resolving bugs early in the development lifecycle. As software complexity grew, traditional QA and code review pipelines could no longer scale, leading to production failures, wasted developer hours, and delayed releases.
The problem: fragmented workflows across the SDLC
Engineering teams spend a lot of time managing the lifecycle, from commits to identifying bugs across different systems, Jira tickets, GitHub pull requests, CI/CD logs, and static code analysis tools. Critical issues are often discovered late in the development cycle, impacting release velocity and user experience.
DevPlaza is building a software lifecycle tracking tool to change that with AI.
The solution: SDLC copilot
As part of the larger tool, GoML and DevPlaza co-developed a modular, scalable copilot solution for SDLC. The MVP integrated four purpose-built agents across the software lifecycle.
Git agent
- Validates pull requests with AI-powered checks
- Automatically analyzes code diffs to flag potential bugs
GitHub actions agent
- Parses CI/CD logs to detect and diagnose failures
- Recommends automated fixes for common error patterns
Jira agent
- Provides AI-driven task and bug triage
- Analyzes historical issue data to forecast failure types
SonarQube agent
- Detects code vulnerabilities and test gaps
- Delivers AI-backed suggestions to improve test coverage and maintainability
Together, these agents formed the backbone of DevPlaza’s new SDLC + AI pipeline, enabling early detection, consistent quality enforcement, and faster resolution cycles.
Architecture: Built with Amazon Bedrock AgentCore
GoML built an agentic AI-driven SDLC copilot using Amazon Bedrock AgentCore.
The impact: reliable code and faster shipping
DevPlaza’s AI-enabled SDLC ecosystem led to improvements across the board during pilots:
- Improvement in unit test coverage, supported by SonarQube AI insights
- Fewer CI/CD build failures, resolved proactively through log analysis
- High developer satisfaction, with AI eliminating repetitive review tasks
With software testing with AI now embedded into DevPlaza’s SDLC, teams can focus on building features, not firefighting issues.
Key lessons for engineering leaders
Common pitfalls to avoid
- Relying solely on manual workflows for quality assurance
- Delaying investment in SDLC automation with AI
- Building AI tools without developer involvement
What to do instead
- Start with an MVP
- Leverage structured tools (Git, Jira, SonarQube) as training inputs
- Ensure multi-agent interoperability and observability
Ready to transform your SDLC with AI?
Let GoML help you bring intelligent testing and automation into your SDLC.
Reach out for an executive AI briefing to explore how a custom AI copilot can help you build better, faster, and more confidently, just like we did with DevPlaza.
