SDLC Automation Platform with AI Agents
What is an SDLC automation platform?
An SDLC automation platform with AI agents is a layer that coordinates specialized agents across the entire software development lifecycle, from discovery to operations, preserving context across stages, integrating existing tools, and measuring impact with governance.
DevAgents OS is an AI-native engineering platform that does not replace the tools your team already uses. It applies agentic orchestration to connect specialized agents to those tools, transforming manual stages into governed, traceable workflows with technical validation.
Coverage of the development cycle
Discovery and Requirements
- Transcription and structuring of refinement meetings
- Automatic creation of epics, stories, and acceptance criteria
- Traceability from demand to backlog
Architecture and Design
- Legacy system analysis and component mapping
- C4 Model support with agent-generated documentation
- Architectural Decision Records (ADRs)
Development and Review
- Code generation with story context and team standards
- Automated PR review with structured justification
- Inline technical documentation
Quality and Testing
- Unit, integration, and BDD/Gherkin test generation
- Coverage validation per acceptance criterion
- Reduced defect escape rate
Security and Compliance
- Continuous SAST, DAST, and threat modeling
- Dependency analysis with CVEs
- AppSec controls before merge
CI/CD and DevOps
- CI/CD pipeline generation and optimization
- Delivery failure diagnosis
- Release automation with impact visibility
Observability and Operations
- Log and operational metrics monitoring
- Agent-assisted root cause analysis
- Contextualized alerts with cycle history
Metrics and Governance
- Lead time, cycle time, throughput, MTTR, and DORA metrics
- Strategic, tactical, and operational visibility
- Continuous improvement driven by real data
Integration architecture
DevAgents OS connects to the tools your team already uses, without replacing the existing stack:
- Repositories: GitHub, GitLab, Bitbucket, Azure Repos
- Project management: Jira, Linear, Azure Boards, Confluence
- CI/CD: GitHub Actions, GitLab CI, Azure Pipelines, Jenkins
- Quality: SonarQube, Semgrep, Snyk, Checkmarx
- Observability: Grafana, Datadog, New Relic, OpenTelemetry
- AI models: OpenAI, Anthropic Claude, Google Gemini, AWS Bedrock
Deployment models
The platform can be adopted in different models, according to maturity and security requirements:
- Managed Workspace: fast activation, no dedicated infrastructure required
- Private Deployment: data and agents running on the company's own infrastructure
- Enterprise Hybrid: cloud orchestration with local execution for sensitive parts