Use case · Government Agencies

Airspace & Drone Operations Governance

Govern AI agents in air-traffic and drone workflows (airspace deconfliction, flight authorizations, and airspace notices), so no ungoverned agent changes a separation parameter or a restriction without an authorized operator and a provable record.

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The risk

What an ungoverned agent gets wrong here.

Aviation authorities are bringing AI agents into workflows that used to be entirely human: deconflicting airspace, approving flight authorizations, and drafting the airspace notices (NOTAMs, Notices to Air Missions: the alerts that tell pilots about hazards and restrictions) that pilots depend on. Add unmanned aircraft systems (UAS, the formal term for drones) and the volume of authorizations grows faster than humans can review each one. These actions are safety-critical: a wrong separation parameter, a flight authorization granted in error, or a restriction lifted without cause reaches real aircraft. Without a governance layer, there is no enforced limit on what an agent may change, no tie from a change to an authorized operator, and no trustworthy record of what the airspace picture was and who altered it.

Without governance

Where it goes wrong.

01

A separation parameter quietly loosened

A deconfliction agent adjusts a separation parameter to smooth traffic flow, nudging it below the safe minimum a controller would ever accept. Tier-gated authorization enforces the hard floor and refuses the change, logging the attempt with a plain-English reason.

02

A drone authorization granted in error

A flight-authorization agent approves a UAS (drone) flight into restricted airspace off a misread request. Because a grant of authority is tier-gated, the action is held for a named authorized operator instead of executing on the agent's say-so.

03

A false airspace notice

An agent drafts and publishes a NOTAM (airspace notice) lifting a restriction that is still in force. Behavioral monitoring flags the out-of-pattern publish and auto-quarantines the agent, and one switch can halt every agent touching the notice system in a fraction of a second.

04

No trustworthy airspace history

After an incident, investigators need to know exactly what the authorization and restriction picture was and who, or what, changed it. A tamper-evident, hash-linked log reconstructs the sequence with each change tied to an operator.

With Agentomy

How Agentomy governs it.

01

Discover every agent in the workflow

Inventory every agent with reach into deconfliction, authorization, and airspace-notice systems. An unregistered agent falls back to read-only: it can observe the airspace picture but cannot change it.

02

Enforce authority before any change

Tier-based permissions gate each action against an operator's authorization level before it runs, with hard safety limits (separation minimums, restricted-airspace rules) enforced rather than advised.

03

Detect drift and halt fast

Per-agent baselines flag an agent acting outside its pattern, with auto-quarantine, and a single switch halts every agent across the airspace stack in under a twentieth of a second, surviving a restart.

04

Prove the airspace picture

A tamper-evident, hash-linked record captures every proposed and applied change (what changed, under whose authorization, and why) so the authority can reconstruct the exact state at any moment.

Frameworks

Maps to what you answer to.

Agentomy does not certify you. It gives you the enforcement and the audit trail these frameworks ask for, so readiness is something you can show rather than assert.

FedRAMPthe US government's standardized cloud-security authorization program; Agentomy provides the access controls and audit evidence a FedRAMP review looks forFISMAthe Federal Information Security Modernization Act, the law setting information-security requirements for federal agencies; the platform supplies enforcement and logging that maps to its requirementsNIST 800-53the federal security and privacy control catalog; governance capabilities map to its access-control and audit controlsNIST AI RMFthe National Institute of Standards and Technology AI Risk Management Framework, the federal framework for managing AI risk; Agentomy gives you the controls and evidence to operationalize it
See it in the record

Every action, logged and provable.

A tamper-evident, hash-linked trail of every governance decision for this workload: what an agent did, under whose authorization, and why. Plain-English reasons for every allow and deny, exportable to the framework your auditors care about.

Agentomy Command CenterFilter Airspace & Drone Operations Governance
  • 100% Integrity
  • 45,849 Blocks
  • SHA-256 hash-linked
Audit trail: one tamper-evident block per governance decision, hash-linked to the one before it
BlockTimestampAgentActionTierHash
45,849Today 12:42:08casework-research-agentdata_access_requestEvaluator4a312519e29a6801
45,848Today 12:41:54resident-triage-agentoutput_validationAnalyst251a34dc453cc040
45,847Today 12:41:37contract-review-agentpolicy_checkBuilder10d60b9f535393c8
45,846Today 12:40:58benefits-extract-agentbehavior_driftOperator4e68b4b5c6659cad
45,845Today 12:40:21records-draft-agenthalt_initiatedStrategist254b86d156c6d6d7
45,844Today 12:39:46transparency-report-agentprompt_reviewEvaluator5722f1c7b57df5ed
Governance events today: 12,842Demo environment
Illustrative interface with sample data, in the shipped Command Center’s structure. Not a customer environment and not a live feed.

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