Platform

Platform overviewArchitectureWorkflow orchestrationGitOps configurationGovernance and AAAAI and MCPKnowledge and contextRuntime and executionEvidence and monitoring

Use cases

All use casesProduction incidentRelease preparationHotfix to productionSecurity scan triage
Why NopsAIIntegrationsSecurity

Resources

All resourcesAI agent governanceMCP governanceMCP securitySelf-hosted platforms
PricingGitHub

Company

How a run worksAboutContactBook a demo

Sales promises a feature that does not exist yet

Sales needs a fast answer; product needs control. Without a governed path, promises get made on incomplete information.

The work already crosses these systems:

SalesforceJiraConfluenceNopsAI run

From scattered checks to one governed run.

Today

An account executive asks in a channel, waits, then guesses. Product finds out after the commitment is already in a proposal.

With NopsAI

A permission-bound workflow lets sales ask roadmap status through approved tools. If the feature does not exist, a pipeline drafts the epic and pauses for PM approval before anything is committed.

Fast, grounded answers for sales. Governance stays with product.

The run, step by step.

Deterministic work first, reasoning inside the tools and written policies the pipeline bound to it, and a named human at every gate its author placed.

AAA access controlProduct analyst agentApproval gateMCP and API orchestrationAudit log
  1. Trigger

    A question from the sales assistant surface, scoped to the roles that account executives hold.

  2. Collect context

    Roadmap items, delivered scope and related opportunities are read through permission-filtered tools.

  3. Verify state

    Deterministic lookups confirm whether the capability exists, is planned or has been declined before.

  4. Reason

    A product-analyst role answers from tool results only, and never claims a commitment was made.

  5. Approve

    If a new epic is proposed, the product manager approves before it is created.

  6. Execute and record

    The answer, its sources and any created record stay in the audit log.

What the run leaves behind.

The useful part is not only the automation. It is repeatability with proof.

Trigger and subject

What started the run and which effective identity it ran as.

Authorization snapshot

Which resources were checked, and which decision each check returned.

Knowledge in scope

The guardrails, policies and runbooks that bounded each decision, stored as text with the run, plus the governance level that applied.

Tool and AI activity

Every tool call, the profile that allowed it, and the model usage it consumed.

Task history and timings

Every task, its state transitions, its duration and the logs it produced, with known secret values masked.

Approvals and outputs

Who approved, when, and the deliverables the run produced.

Pipeline runs overview showing status, run identifiers, durations and outputs.

Map this workflow against your controls.

Bring the trigger, the tools it touches, the approvers, the runtime boundary and the evidence you need to keep.