DevAgents OS
An Agentic Engineering Platform for the Software Development Lifecycle
An agentic platform to accelerate the software engineering lifecycle with specialized copilots, metrics, automation, and governance.
AI speed only creates value when connected to the real engineering process: requirements, architecture, code, testing, security, operations, and metrics.
Discover which agents would make sense for your current software lifecycle.
Specialized copilots across the SDLC
DevAgents OS is a software engineering platform based on specialized agents. It coordinates copilots across the SDLC to support requirements, architecture, development, testing, security, operations, metrics, and legacy system modernization.
The differentiation is not using an isolated copilot. It is organizing agents by lifecycle stage, preserving context across tools, measuring impact, and creating a continuous improvement flow.
Isolated AI usage
- Isolated prompt, no lifecycle context
- Context lost between tools
- Manual handoff between stages
- Hard to measure real impact
Agentic engineering
- Specialized agents per stage
- Shared lifecycle context
- Integrated and orchestrated tools
- Measurable engineering outcomes
Orchestrate Agents
Coordinate specialized agents to transform demands, code, tests, deployments, and metrics into a continuous flow.
Accelerate Engineering
Reduce manual effort in requirements, documentation, code generation, testing, and technical analysis.
Govern with Data
Track efficiency, quality, bottlenecks, risks, and team evolution with clear indicators.
From discovery to operations, orchestrated by agents
Less rework, more traceability, faster decisions, and data-driven improvement.
Specialized Agents
Requirement Agent
- Meeting transcription
- Epic and story creation
- Acceptance criteria
- Exception criteria
Architecture Agent
- Legacy system understanding
- Component mapping
- C4 Model support
- Technical documentation
Code Agent
- Code generation
- Code review
- Technical documentation
- Engineering best practices
Quality Agent
- Automated test generation
- Unit and integration tests
- Gherkin and BDD
- Test documentation
Security Agent
- SAST and DAST
- Threat modeling
- Vulnerability analysis
- AppSec controls validation
DevOps Agent
- CI/CD pipeline development
- Deployment automation
- Release management
- Pipeline failure analysis
Observability Agent
- Monitoring and logs
- Operational metrics
- Root cause analysis
- Availability insights
Metrics Agent
- Productivity and quality indicators
- Lead time, cycle time, and throughput
- Bottleneck identification
- Continuous improvement support
Legacy Modernization Agent
- Legacy code reading and fragmentation
- Business rule extraction
- Modern code generation
- Traceability and functional validation
Management and Governance Layer
Strategic, tactical, and operational visibility in a single data layer.
Automation is not enough. You need to measure its impact.
Strategic
Strategic view with OKRs, objectives, progress, speed, and decision-making.
Coordination
Management of team performance, delivery volume, capacity, predictability, and tactical tracking.
Operational
Operational metrics for squads, quality, flow, bottlenecks, tests, deployments, and continuous improvement.
Continuous Improvement
An iterative cycle to evolve engineering maturity.
Implementation does not start by buying tools. It starts by understanding where engineering loses time, context, and quality.
Discover
Map processes, discover bottlenecks, identify opportunities, and understand current maturity.
Plan
Define metrics, select tools, create a baseline, and establish cycle objectives.
Activate Agent
Activate the right agent, connect tools, configure context, and execute the assisted workflow.
Measure Impact
Measure gains, compare before and after, confirm impact, and capture lessons learned.
Prioritize
Use metrics and backlog to decide the next improvement opportunity.
Legacy Modernization with Agents
The risk in modernization is not just rewriting code. It is losing business rules hidden inside old systems.
The process starts by understanding the legacy code, then moves through structural fragmentation, business rule extraction, modern code generation, and validation of functional equivalence.
Before
- Old code, hard to maintain
- Low documentation of business rules
- Dependency on legacy specialists
- High risk with every change
After
- Documented business rules
- Modern code generated and validated
- Tests and end-to-end traceability
- Human review at every stage
Legacy Code
Input of legacy code from systems with high complexity and accumulated technical debt.
Structural Fragmentation
Splitting code into smaller blocks for contextual analysis.
Business Rules Extraction
Identification and documentation of existing business rules.
Agentic Code Generation
Modern code generation supported by specialized agents.
Functional Validation
Technical and functional validation, testing, traceability, and human review.
Tool Ecosystem
DevAgents OS connects categories of tools your engineering already uses. Examples are illustrative and replaceable per enterprise environment.
AI Models and Agent Platforms
Engineering Copilots and Agentic IDEs
Repositories and Code Platforms
Project and Product Management
CI/CD and DevOps
Quality and Test Automation
Security and AppSec
Observability and Operations
Architecture and Design
Knowledge and RAG
Illustrative examples. The real composition depends on each company's stack, governance, repositories, security policies, and maturity.
Impact Metrics
Real market references combined with practical indicators to accelerate engineering, quality, security, delivery, and modernization.
The market already demonstrates relevant gains from AI applied to software engineering. DevAgents OS turns that potential into an operating model: specialized agents connected to the SDLC, stage-based metrics, tool integration, and governance to guide decisions.
Of respondents use or plan to use AI tools in the development process, per the latest edition of the annual survey.
Of developers report using AI tools at work, evidencing accelerated adoption beyond one-off experimentation.
Of professional code is already AI-generated. The share is expected to grow to 65% by 2027, per the same survey.
Of code will be AI-generated by 2027, up from 42% already verified. Review and governance will scale alongside.
In a controlled study with real developers, GitHub Copilot users completed a programming task 55.8% faster than the control group.
Estimated range of GenAI's potential impact on software engineering function productivity, per the McKinsey Global Institute.
Specific development tasks can be completed up to twice as fast with GenAI, including code generation, testing, and documentation.
Scaled adoption requires human review, traceability, and operational governance. Teams that structure this reap sustainable productivity benefits.
Of developers do not review AI-generated code with the same rigor applied to human-written code — a real risk that DevAgents OS helps address.
The references above show market maturity. DevAgents OS uses this direction to structure agents, indicators, and improvement flows applicable to each organization's real context.
Model Choice: Context-Based Fit
The choice between OpenAI, Gemini, Claude, or local models should consider stack, data, security, cost, context, and tool integration. DevAgents OS organizes this choice inside the engineering flow, not as an isolated decision.
| Model family |
|---|
| OpenAI / Azure OpenAI / Codex |
| Google Gemini / Google Cloud |
| Anthropic Claude |
| Open-source / Local LLMs |
The market shows the potential. DevAgents OS turns that potential into flow, agents, metrics, and governance for your engineering organization.
I want to accelerate my engineering with agentsReady to accelerate your engineering with agents, metrics, and governance?
We can map a real engineering flow, identify bottlenecks, choose the first agents, and establish metrics to measure impact and guide scale.
Agents amplify engineering
AI agents amplify team capability when connected to the real process: context, flow, metrics, security, and governance.
Presentation