Governance that actually closes the loop
Most governance tools end at the finding. Stratoscope's governance engine runs the full cycle: discover the issue, assess the risk, execute the fix (with your approval), watch for regression, and verify the outcome. Every step is logged.
Request early accessThe governance loop
Discover
Continuous tenant sweeps build a living model of every resource, permission, and cost signal. The model updates on every cycle — not just when you run a scan.
Assess
Findings are evaluated against all five WAF pillars, your ingested architecture docs, and your team's previous decisions. The engine scores risk, prioritizes, and generates proposed actions.
Remediate
Multi-step execution plans are presented for your approval. You see the exact commands, the rationale, and the risk level before anything runs. Approve step-by-step or by plan.
Watch
After remediation, watch rules monitor the affected resource for regression. If the fix reverts — a tag changes back, a permission re-opens — the engine alerts immediately.
Verify
ARM state is verified after every fix. The platform confirms the resource is in the expected state before closing the finding. No assumed success.
Multi-agent architecture
Stratoscope runs a team of specialized agents — not one generalist model trying to do everything. Routing, specialist execution, and quality review are all separate, inspectable layers.
Scout (router)
The primary agent that understands your governance question, routes it to the right specialist, and synthesizes the response. Handles multi-step planning and compound requests.
Domain specialists
14+ specialized agents — Azure, Cost, Security, WAF, Identity, K8s, and more. Each specialist has domain-appropriate tools, risk thresholds, and context. Routing prevents specialists from overreaching their scope.
Quality critic
An independent review layer that evaluates every specialist response for goal completion, accuracy, and compliance with your team's stated preferences. Scores feed back into routing decisions.
The platform gets smarter over time
Every approval, denial, and modification teaches the engine what your team cares about. Ingested architecture docs and runbooks become persistent context — not just a one-time input. The governance model compounds.
Agent memory
Past decisions and approval patterns inform future proposals. The engine learns which operations your team approves quickly and which ones generate pushback.
Knowledge ingestion
Upload architecture diagrams, runbooks, compliance policies, and incident post-mortems. The engine references them when generating assessments and remediation plans.
Pattern recognition
Recurring failure modes — the same misconfiguration across resource groups, the same cost spike pattern — are flagged as systemic issues, not one-off findings.
Nightly reflection
Every night, the platform reviews the day's operations, updates its world model, promotes learned patterns into long-term context, and prepares the next day's priorities.
Close the loop on your Azure governance
Join the private preview. Tenant Discovery runs before your first conversation.
Request early access