Code to operate for the AI-native SDLC.
One platform to plan, provision, secure, deploy and operate full-stack Kubernetes and GenAI infrastructure, with Agent Evals as the release gate. Agents ship on the same automated, audit-ready path as the rest of your software.
Speed without trust moves the bottleneck downstream.
Code, infrastructure and now agents are generated faster than teams can safely ship them. IaC alone can't keep up. And agents don't fail like software: they drift, hallucinate, misuse tools and regress silently with every prompt, model or retrieval change. Quality, security and cost have to be engineered in from day zero.
Humans orchestrate. Agents execute. MajorDomo automates and governs every stage.
Visual, low-code planning: needs, standards, compliance
Source control, branching, secret and dependency scans
Orchestrated builds, SAST/SCA, container scans
Unit, coverage and offline agent evals as release gate
IAST/DAST, pen-test, operational protections
Blue-green, zero-downtime, policy-enforced rollout
Day-2 automation, drift control, cost and policy
No-code provisioning
Secure, compliant full-stack environments in minutes from reusable cluster profiles.
GitOps CI/CD on Kubernetes
Image promotion from Dev to Prod without rebuilds, branch-to-environment mapping, automatic reconciliation.
FinOps built in
Cost-aware design, right-sizing, full tag coverage, budget alerts and idle-resource cleanup.
Any cluster, every layer
OS, CNI, ingress, service mesh and security across greenfield or brownfield clusters, with zero-downtime upgrades.
Security by design
Policy-driven guardrails, shift-left scanning, zero trust and continuously enforced compliance.
Audit-ready from day one
Every stage logged and governed, with air-gapped deployment options.
Ship agents with confidence. Roll back in seconds.
The release discipline software teams have relied on for years, now for agents.
Evals are the new CI for AI agents.
An independent evaluation layer, separate from inference and wired into CI/CD, for governed, reproducible and explainable scoring you can defend in production.
Does the agent complete tasks correctly across a representative set of inputs?
Does it refuse correctly and stay within its defined scope and constraints?
Did a prompt change significantly raise per-run token or compute cost?
Offline evals as a release gate
Triggered by any system-wide change, not just code commits, with regression reports across model providers and versions.
Execution-graph scoring
Evaluates plans, tools and step transitions stage by stage, pinpointing root cause at the step, not just the task.
Online evals in production
Continuous evaluation surfaces quality drift and failures before users see them.
Independent judge pipelines
Separate inference and judge LLMs across providers, so eval quality isn't coupled to production inference.
Configurable scoring
Opinionated defaults to start, custom metrics and weights, and comparable scores across every agent.
Closed-loop golden datasets
New production scenarios and failure patterns are flagged for human review and added to eval datasets. Today's production failure becomes tomorrow's regression test.
Only need evals? Agent Evals is also available on its own, alongside your existing agent stack. See Agent Evals →
How it connects to Operational Intelligence
Every pipeline run, eval result and production incident is operational knowledge. AgentOps runs the delivery path that Operational Intelligence captures and replays, so each release starts from what the last one learned.