From a familiar signal to a trusted outcome.
Git events, schedules, APIs, manual actions and external services all enter one request model. What happens next is the same governed path every time, whatever started it.
One path from intent to evidence.
NopsAI connects control-plane decisions and data-plane execution without turning AI work into a separate, ungoverned system.
Normalize intent
Git, schedules, APIs, manual actions and external services enter one request model.
Resolve permission
AAA, scope, secrets, knowledge, model profiles and tool profiles are checked before dispatch.
Execute in isolation
The dispatcher selects an eligible Docker or Kubernetes runner for a per-run agent.
Keep the evidence
Logs, task state, approvals, AI usage, monitoring and outputs remain attached to the run.
Six steps, every time, whatever started the run.
The order matters. Deterministic work happens before reasoning, reasoning happens inside the tools and written policies the pipeline gave it, and nothing passes a gate you placed until a named human agrees.
Normalize the trigger
A Git event, schedule, API call, external service or manual action becomes one consistent run request with a known effective subject.
Resolve the definition
Pipeline YAML, reusable steps, scope, variables, secrets and access decisions are loaded and snapshotted — including the guardrails, policies and runbooks each pipeline, step and task bound to itself.
Authorize the caller
Default-deny checks cover the route and every protected resource the run references. Failures fail closed, before dispatch.
Dispatch to a runner
The dispatcher filters connected runners by scope, routing, capacity, affinity and runtime pool, then launches a per-run agent.
Advance the graph
Deterministic tasks and AI goals execute in dependency order. The run pauses at approvals without holding a runner.
Attach the evidence
Status, logs, task history, approvals, AI usage and declared outputs stay owned by the run and survive the conversation.
Durable control plane. Isolated data plane.
The API, AAA, configuration, dispatcher and persistence layers decide and record what should happen. Docker and Kubernetes runners execute transient work and report state back through authenticated service boundaries.
Controls stay active across every capability.
Governance is part of route composition, configuration resolution, tool discovery, dispatch, execution and monitoring rather than a separate review step.
01 · Workflow orchestration
Combine scripts, LLM goals, approvals, reusable steps and child pipelines without splitting the operating model.
02 · GitOps configuration
Manage pipelines, scopes, access, knowledge, profiles, triggers and runtime defaults as versioned configuration.
03 · Governance and AAA
Apply default-deny access decisions before configuration reads, tool use, secret resolution, approvals and dispatch.
04 · AI and MCP
Select controlled agent, LLM and MCP profiles per pipeline or step while preserving permission-bound tool discovery.
05 · Knowledge and context
Write the guardrails, policies, ADRs, runbooks and architecture notes that bound AI work, and decide which of them constrains which step.
06 · Runtime and execution
Dispatch per-run agents to Docker or Kubernetes runners using scope, capacity, affinity, routing and runtime pools.
07 · Evidence and monitoring
Connect run history, system logs, outputs, AI usage, reliability, security, cost, alerts and recommendations.
Bring one workflow. We will map its controls, runtime, and evidence with you.
The fastest way to evaluate NopsAI is a single real workflow you already run manually and cannot safely hand to an unrestricted agent.