DevAgents OS
Your team uses AI. Every tool still works in isolation?
DevAgents OS connects specialized agents across requirements, architecture, code, quality, security, delivery, operations, and metrics. Shared context, traceability at every stage, and governance from day one.
How many handoffs happen between a requirement and the first commit? That's exactly where we start: mapping the workflow, activating the first agent, measuring the impact. Then we expand across the SDLC.
Discover which agents would make sense for your current software lifecycle.
Specialized agents across the SDLC
DevAgents OS is an AI-native engineering platform built on specialized agents. Agent orchestration coordinates these operations across the SDLC: requirements, architecture, development, testing, security, operations, metrics, and legacy modernization. Shared context, governance, and traceability are built in.
The differentiator is not using an isolated AI tool. It is agent orchestration: organizing specialized agents per lifecycle stage, preserving shared context across tools, measuring impact, and building a continuous improvement loop. Technical validation is applied at each stage.
Isolated AI usage
- Isolated prompt, no lifecycle context
- Context lost between tools
- Manual handoff between stages
- Hard to measure real impact
- No governance or traceability across the process
AI-native engineering
- Specialized agents across the SDLC
- Shared context across the lifecycle
- Tools and artifacts integrated
- Governance, metrics, and traceability
- Technical validation and accountable decisions
Orchestrate Agents
Connect requirements, architecture, code, tests, and operations into one governed workflow. Start with the handoff where your team loses the most context today.
Accelerate Engineering
Reduce rework and manual handoffs across requirements, documentation, code review, testing, and technical analysis. Structured agent assistance is available at each stage.
Govern with Data
Track efficiency, quality, bottlenecks, risk, and team evolution. Indicators are tied to real engineering outcomes and audit-ready traceability.
From mapping to expansion
Every engagement starts with one workflow, the handoff where context loss costs the most, and expands as impact is measured and validated.
Assess your engineering workflow
We map where your team loses the most time and context today. For most engineering organizations, that is the transfer from requirements to engineering.
Activate your first agent
The Requirements Agent connects to your real workflow, turning meetings and backlog inputs into structured epics, user stories, and acceptance criteria.
Integrate with your existing stack
Connects with the tools your team already uses. No stack migration required to get started.
Measure and baseline impact
Cycle time, rework reduction, and traceability coverage are tracked from the first workflow to establish a measurable baseline.
Expand across the SDLC
With impact confirmed, the same orchestration model expands to architecture, code, quality, security, DevOps, and operations.
Assess your engineering workflow
We map where your team loses the most time and context today. For most engineering organizations, that is the transfer from requirements to engineering.
Activate your first agent
The Requirements Agent connects to your real workflow, turning meetings and backlog inputs into structured epics, user stories, and acceptance criteria.
Integrate with your existing stack
Connects with the tools your team already uses. No stack migration required to get started.
Measure and baseline impact
Cycle time, rework reduction, and traceability coverage are tracked from the first workflow to establish a measurable baseline.
Expand across the SDLC
With impact confirmed, the same orchestration model expands to architecture, code, quality, security, DevOps, and operations.
Three ways to operate
DevAgents OS adapts to your team's reality, regardless of technical maturity or security requirements.
DevAgents OS operates the agents for you
We select models, providers, and integrations suited to the contracted scope. Your team focuses on the product while agents work under our operation and governance.
The platform in your environment, under your control
The platform is deployed in your own infrastructure, with validation and integration with the models and tools defined by your team. Ideal for companies with compliance and security requirements.
Combines managed service and local autonomy
Combines managed service, licensing, support, updates, and governance according to technical maturity and security requirements. The transition between formats happens as your team evolves.
Requirements to engineering: how the first workflow runs
One workflow, fully connected. Then expand to the next.
The problem
Requirements live in meetings, chat threads, and documents. By the time they reach engineering, context is lost, rewritten, or simplified. Engineering ends up interpreting what someone meant instead of building from a shared, traceable source.
How DevAgents OS addresses it
The Requirements Agent turns meeting transcripts and backlog items into structured epics, user stories, and acceptance and exception criteria. All linked to a shared context layer the rest of the SDLC builds on.
What comes out of this workflow
- •Epics and user stories traceable to the meeting they came from
- •Acceptance criteria that separate decisions from discussion
- •Exception criteria that capture edge cases before they're forgotten
What we measure
Time from transcript to structured backlog, rework avoided through clearer criteria, and traceability coverage from requirement to delivered code.
From discovery to operations, orchestrated by agents
Less rework, more traceability, faster decisions, and data-driven improvement.
Specialized Agents
Isolated AI tools don't solve the context problem. What moves the cycle is orchestration: each agent has a defined purpose, shares the same context layer, and is activated as the impact of the previous workflow is measured and confirmed.
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.
What do you actually know about the real impact of AI on your engineering today? Not estimates. Data. The governance layer connects what agents produce to what leadership needs to see: OKR progress, delivery velocity, quality trends, risk, and bottleneck visibility. No manual data collection required.
Strategic
Executive and director-level view: OKR alignment, engineering investment visibility, delivery speed, and portfolio-level risk indicators.
Coordination
Squad and program-level visibility: delivery volume, team capacity, predictability, cross-team dependencies, and tactical decision support.
Operational
Agent-level and workflow-level metrics: lead time, cycle time, throughput, rework rate, test coverage, deployment frequency, change failure rate, and MTTR. Broken down by squad, by stage, by workflow.
Metrics and charts above are illustrative examples. Your governance dashboard reflects real data from your engineering workflows from day one.
Model-aware and provider-flexible. The right model for each task.
DevAgents OS does not lock your engineering into a single AI provider. The orchestration layer selects the right model for each agent task and adapts as your stack, data requirements, and cost constraints evolve.
Each agent uses the model best suited to its task: long-context reasoning, code generation, evaluation, or orchestration. You choose the provider, the platform adapts.
Right model per task
Requirements analysis, architecture decisions, and code review demand different capabilities. The orchestration selects the right model for each agent, whether for long context, code generation, or evaluation, without manual configuration from the team.
Enterprise orchestration
Agent workflows, evaluation pipelines, observability, access control, and enterprise-grade governance. The infrastructure scales with your team, regardless of the AI provider you choose.
Flexible infrastructure
The orchestration layer deploys on the infrastructure that makes sense for your business, whether public, private, or hybrid cloud, integrated with your existing enterprise environment, security policies, and data residency requirements.
The platform is compatible with OpenAI, Anthropic Claude, Google Gemini, open-source models, and local/private models. The choice depends on context, data residency, compliance, and cost. Provider flexibility is part of the architecture, not a roadmap item.
Impact Metrics
Real market references combined with practical indicators to accelerate engineering, quality, security, delivery, and modernization.
The market already demonstrates measurable gains from AI applied to software engineering. DevAgents OS transforms that potential into an AI-native operating model: specialized agents connected to the SDLC, stage-level 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.
Market references above reflect published research. Results in your environment will depend on your team's baseline, current tooling, workflow maturity, and integration scope. DevAgents OS uses these ranges to structure agents, set baselines, and track real progress. These are not guaranteed outcomes.
Model Selection: Fit by Context
The choice between OpenAI, Gemini, Claude, or private/local models should reflect your stack, data classification, security requirements, cost profile, and integration constraints. DevAgents OS structures this decision within the engineering workflow, not as a standalone technical choice.
| 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 workflow, agents, metrics, and governance for your engineering.
I want to accelerate my engineering with agentsImprovement as an operating loop, not a project
Once the first workflow is live, the platform enters a continuous cycle: measure outcomes, identify the next highest-impact improvement, activate the next agent, and expand.
Deployment does not start by buying tools. It starts by understanding where engineering loses time, context, and quality. For most teams, that is the handoff from requirements to engineering.
Discover
Identify where the team loses time, context, and quality, and map the workflow with the highest improvement potential.
Plan
Define success metrics, select integrations, establish a baseline, and set the scope for the first agent activation.
Activate
Activate the appropriate agent, connect to your existing tools, configure shared context, and run the first assisted workflow.
Measure Impact
Compare before and after: cycle time, rework rate, traceability coverage, defect rates. Validate impact. Capture what the data shows.
Prioritize
Use governance data and the improvement backlog to select the next workflow and the next agent to activate.
Legacy Modernization with AI Agents
The risk of modernization lies not just in rewriting code. It lies in losing business logic hidden inside aging systems.
Legacy modernization becomes safer with AI-native workflows: understanding legacy code, structural fragmentation, business rules extraction, modern code generation, and functional equivalence validation. All under agent orchestration, with technical validation at critical points.
Before
- Aging codebase, hard to maintain
- Low documentation of business rules
- Dependency on legacy specialists
- High risk of regression with every change
After
- Business rules extracted and documented
- Modern code generated, validated, and traceable
- Automated tests with end-to-end traceability
- Technical validation at every critical modernization stage
Legacy Codebase
Legacy code from systems with high complexity and accumulated technical debt, ingested as the starting point.
Structural Fragmentation
The codebase is segmented into analyzable units so business rules and dependencies can be extracted without losing structural context.
Business Rules Extraction
Existing business rules are identified, extracted, and documented in structured, traceable form.
Agentic Code Generation
Modern code is generated by specialized agents, preserving the extracted business logic and maintaining traceability.
Functional Validation
Technical and functional validation confirms equivalence between the original behavior and the modernized output. Tests, traceability, and accountable modernization decisions are applied at every stage.
Tool Ecosystem
Model-agnostic, tool-aware, and governance-driven: DevAgents OS integrates with the categories of tools your engineering team already uses. Specific examples vary 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
Examples shown are illustrative. The actual composition depends on each organization's stack, security policies, data residency requirements, repositories, and maturity level.
Built by someone who lived the problem
Cristiano Ferreira
Founder · PULSEFLOW TECNOLOGIA LTDA
Cristiano has worked on both sides of the handoff DevAgents OS targets first: writing the requirements engineering has to interpret, and being the engineer who interprets requirements someone else wrote.
Want to accelerate your engineering with agents, metrics, and governance?
We can map a real engineering workflow, identify bottlenecks, select the first agents, and establish metrics to measure impact and guide scale.
Start with one workflow. Expand with evidence.
A connected workflow, with agents operating on the same context, changes the velocity and quality of engineering. Not as a promise. As a measurable outcome. Technical accountability stays with your team. DevAgents OS provides the structure to expand with evidence.
Map my engineering workflow