What We Do
Most engineering teams already use AI in pieces: copilots, point tools, isolated automations. DevAgents OS is the orchestration layer that turns fragmented AI usage into AI-native software engineering, connecting requirements, architecture, code, quality, security, delivery, and operations through specialized agents, with shared context, traceability, and governance.
How We Start
We don't begin by deploying a platform of agents across every stage. Teams start by activating one measurable workflow inside DevAgents OS, usually the handoff from requirements to engineering, where context loss costs the most.
The platform captures the baseline: where the team loses time, where rework occurs, where decisions get lost, where quality breaks down, where security arrives too late, and where metrics fail to explain what is happening.
From that baseline, we connect the first agent to your stack and measure impact before expanding across the SDLC.
How We Operate
Instead of using AI in isolation, DevAgents OS applies agentic orchestration: a layer that connects specialized agents, tools, artifacts, and metrics to the real engineering workflow, with shared context and governance.
Each agent operates in a specific stage of the SDLC, shares context with other agents, and contributes to generating technical artifacts, metrics, traceability, and operational visibility.
The Agent Catalog: Your Expansion Path
Once the first workflow is connected and impact is measured, the same orchestration model expands to the rest of the SDLC. The agents below are modules within that layer, not standalone tools, organized by the phases they cover:
- Discovery & Architecture (Requirements + Architecture): turn meetings, demands, and briefings into epics, user stories, and acceptance criteria, and support component mapping, technical decisions, architectural risks, and impact analysis.
- Build & Quality (Code + Quality + Security): support code generation, review, refactoring, and documentation, test scenarios, automation, coverage, SAST, DAST, and risk identification before production, maintaining traceability between requirements, code, and validations.
- Delivery & Operations (DevOps + Observability + Metrics): support CI/CD pipelines, deployment automation, logs, metrics, traces, alerts, and the analysis of lead time, cycle time, throughput, and bottlenecks.
- Modernization: supports legacy system analysis, business rule extraction, technical documentation, and paths for gradual modernization.
See the full agent list on the homepage.
Governance Layer
Alongside specialized agents, DevAgents OS provides a governance layer connecting technical execution, operational indicators, and management decisions, tracking OKRs, monitoring delivery, quality, security, and operational metrics, identifying bottlenecks in the flow, tracing decisions and outcomes, and prioritizing improvements.
The goal is not more bureaucracy. It is turning technical and operational data into visibility for better decisions.
Deployment Models
Managed Workspace
DevAgents OS runs as a managed workspace where your team activates agents, connects tools, tracks workflows, and measures engineering outcomes without managing the underlying orchestration infrastructure. The platform handles model selection, agent provisioning, and integrations.
Private Deployment
DevAgents OS can be deployed in your own environment, respecting security, data residency, governance, model provider, and infrastructure requirements you define.
Enterprise Hybrid
An enterprise deployment model that combines managed workspace, private deployment, support, updates, integrations, and governance according to your technical maturity, security requirements, and needs.