Ship agents you can trust, from draft to production.
DevAI is an agentic application-lifecycle and SRE platform. It combines versioned agents, prompts, skills, MCP tools, isolated sandboxes, evaluation gates, durable workflow orchestration, traces, and owner-attributed model usage in one control plane.
Create your first agent See the architectureWhy DevAI
Agents are non-deterministic software with side effects. A plausible chat reply is not evidence that an agent is safe or useful. DevAI makes the evidence loop the product:
Every promoted agent version is one that passed a pinned evaluation suite, with its quality, safety, latency, token use, and cost recorded and explainable. Nothing reaches the catalog on vibes.
What the platform does
ALM pipeline
14 orchestrated agents take work from requirement ingestion through planning, implementation, review, security scanning, tests, infrastructure, and release — with bounded review loops and hard security gates.
SRE autonomy
7 SRE agents run a discover → monitor → correlate → respond → learn loop on a schedule. Discovery auto-maps Kubernetes namespaces, services, and dependencies; incidents get correlated and answered, and the loop learns from outcomes.
Isolated sandboxes
Every candidate agent runs in an owner-scoped sandbox that pins prompt, model, dataset, tool policy, budgets, and expiry. Tools run in mock, replay, block, or deliberately-allowed real mode — side effects are a boundary, not an accident.
Evaluation gates
Versioned datasets and suites score deterministic outcomes, tool trajectories, model-graded quality, and the operational envelope. Publication is refused unless the exact tested draft passed the exact pinned suite.
Governed registry & gateway
Agents, blueprints, datasets, and suites are versioned records in a registry. All model traffic flows through AgentGateway for policy enforcement, usage attribution, and cost recording — provider keys never enter a sandbox.
Traces over scores
A score locates a problem; a trace explains it. Every run keeps the pinned prompt, model turns, tool inputs and outcomes, blocked calls, tokens, latency, and cost — in order, owned by the same principal.
Dashboards & chat
An ALM dashboard for pipeline runs, agent activity, approvals, and evaluation comparisons, plus an SRE dashboard for cluster health and incidents. A tool-calling chat agent drives the same APIs conversationally.
Multi-provider, multi-SCM
Adapters keep vendors swappable by configuration: multiple LLM providers behind one gateway, GitHub / GitLab / Azure DevOps behind one SCM interface, and pluggable memory, storage, and event backends.
The lifecycle in one paragraph
You author an agent as a small YAML manifest, validate it, and create a sandbox that pins everything the run depends on. You evaluate it against a versioned dataset — happy paths, prompt injection, tool failures, refusals, boundaries — and read the failing traces instead of tuning aggregates. When a candidate beats its baseline on an unchanged dataset, you publish: the gate verifies the evaluation run belongs to you, tested this exact draft, used the suite's dataset version, and met every threshold. Only then does the agent appear in the catalog, stamped with the evidence that admitted it.