DevPlaza, an emerging leader in developer tooling innovation, set out to solve one of the most persistent challenges in modern software engineering: inefficiencies in software development lifecycles that lead to poor product quality and stunted business growth. One of the most common challenges that DevPlaza is enabling to prevent during software lifecycles is 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. This applies even more to vibe coding tools that are the latest in the current industry trends with software development.
The problem: SDLC inefficiencies and quality hits
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 foundational software development platform to change that with Al, with faster, more proactive bug detection and resolution workflows.
The solution: Early bug detection and resolution Al Workflow
As part of the larger platform, GoML and DevPlaza co-developed a modular, scalable solution for enhanced quality. The MVP integrated four purpose-built agents across the software lifecycle, along with an orchestrator/ planner agent.
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 bug detection and resolution, consistent quality enforcement, and faster resolution cycles.
Architecture: Built with Amazon Bedrock AgentCore
GoML built this agentic AI-driven software development solution using Amazon Bedrock AgentCore.
The impact: reliable code and faster shipping
DevPlaza’s AI-enabled SDLC ecosystem will lead to improvements such as:
- Improvement in unit test coverage, supported by the SonarQube AI agent
- Fewer CI/CD build failures and increased PR quality via GitHub Actions and GitHub agents, resolved proactively through logs, PR and build analyses
- Enhanced bug triage and analysis via the Jira agent, for easier tracking and better understanding from historic trends
- High developer satisfaction, with Al eliminating repetitive tasks and increasing overall product quality, in a faster and more automated manner
For DevPlaza's customers that use this Al workflow that provides enhanced software testing mechanisms enabled as part of the rest of the SDLC offering from DevPlaza, teams can focus on building features, not firefighting issues.
Key lessons for engineering leaders
Common pitfalls to avoid
- Relying solely on manual testing workflows for quality assurance
- Delaying investment in SDLC setup, and enhanced testing mechanisms including test automation with AI
- Building AI tools without developer involvement
What to do instead
- Start with an MVP
- Leverage platforms like DevPlaza for proper SDLC setup, and invest in the early bug detection Al workflow that GoML and DevPlaza developed for enhanced quality, time and cost efficiency, and streamlined SDLC cycles with structured tools (Git, Jira, SonarQube) as training inputs.
- Ensure multi-agent interoperability and observability
Ready to transform your use case with AI?
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.
