Use case · Defense Contractors

Autonomous Fleet Governance

Governance for autonomous-vehicle and robotic fleets operating under mission constraints.

Talk to salesSee the platform
The risk

What an ungoverned agent gets wrong here.

Autonomous vehicle and robotic fleets make driving and operating decisions every second, and each one is validated only for a defined operational design domain (ODD, the specific conditions of geography, weather, and time of day that a system was tested to handle). The platform governs the driving model's outputs (lane keeping, object detection, path planning) but not the fleet's behavior: operating outside the ODD, perception degrading, an emergency response failing, or one fault cascading across many vehicles. The civilian record shows the stakes: a robotaxi that dragged a pedestrian and drew a $1.5M federal civil penalty, a test vehicle that struck and killed a pedestrian after its perception system cycled through classifications with emergency braking disabled, and a fleet of more than 100 robotaxis that froze at once and blocked intersections. Without governance there is no enforced boundary on where a vehicle may operate and no trustworthy record of what the fleet did.

Without governance

Where it goes wrong.

01

Operating outside the validated domain

A vehicle drifts beyond its operational design domain (into weather, roads, or hours it was never validated for) and keeps driving autonomously where it should have handed back or safely stopped.

02

Perception quietly degrading

Sensor accuracy declines below the safety threshold. The degradation is gradual, so a certified perception stack becomes a liability before the next object goes unclassified.

03

A fault that cascades across the fleet

A connectivity loss, a bad over-the-air update, or corrupt map data hits many vehicles at once, immobilizing a whole fleet and blocking traffic instead of failing one vehicle at a time.

04

No human in reach when it matters

A vehicle needs a remote operator and cannot establish the connection in time, yet keeps operating autonomously instead of executing a safe stop.

With Agentomy

How Agentomy governs it.

01

Enforce the operational domain

Telemetry reporting visibility, wind speed, precipitation, or road-surface condition is judged against the certified operational design domain envelope registered for that platform. Nothing in the envelope comes from the vehicle's own request: an envelope sent with the telemetry is dropped and recorded, and a platform whose envelope has not been registered is denied rather than defaulted. A vehicle outside its certified conditions loses authorization instead of driving on it.

02

Correlate faults and halt the fleet

Fleet-wide monitoring correlates error telemetry to catch a cascade forming, and repeated emergency shutdowns inside the registered cascade window are blocked before the next one lands. One switch halts every governed vehicle in a fraction of a second, a single named vehicle can be halted and quarantined on its own, and the halt survives a restart.

03

Gate on remote intervention

An action reporting teleoperation telemetry is authorized only while connection latency, remote-operator availability, and teleoperation system health sit inside the bounds registered for that platform. Both the latency ceiling and the operator-ratio floor used to arrive on the request; both are now server-registered, and one sent by the vehicle is dropped and recorded. A platform with nothing registered is denied rather than assumed reachable.

04

Prove what the fleet did

A tamper-evident, hash-linked record captures every operation and incident (what happened, under whose authorization, and why) and supports the crash-reporting deadlines fleet regulators impose.

What it detects

The detection patterns behind this workload.

Agentomy carries a dedicated AV pattern family for Autonomous Fleet Governance. Each one is a specific failure this layer watches for, with the signal it watches, checked at runtime before the action reaches your systems.

10 of 166 governance patterns
  • Critical
  • High
  • AV-001Severity: Critical

    ODD violation

    DetectionContinuous geofence comparison against registered ODD polygons per vehicle permit.

  • AV-002Severity: Critical

    Perception degradation

    DetectionReal-time sensor health monitoring.

  • AV-003Severity: Critical

    Emergency response failure

    DetectionAudio classification for siren detection with 360-degree microphone array.

  • AV-004Severity: Critical

    Pedestrian detection gap

    DetectionCross-reference perception system pedestrian detections against ground truth validation feed.

  • AV-005Severity: Critical

    Fleet cascade failure

    DetectionFleet-wide anomaly correlation.

  • AV-006Severity: Critical

    School zone violation

    DetectionSchool zone geofence monitoring with time-of-day activation.

  • AV-010Severity: Critical

    Remote intervention timeout

    DetectionRemote operator connection monitoring.

  • AV-007Severity: High

    Construction zone breach

    DetectionMap freshness validation against construction zone databases.

  • AV-008Severity: High

    SGO reporting delay

    DetectionIncident timestamp tracking against SGO filing deadlines.

  • AV-009Severity: High

    Weather adaptation failure

    DetectionWeather condition monitoring via onboard sensors (rain sensor, temperature, visibility estimation) cross-referenced with weather service APIs.

AV family, AV-001 to AV-010 · 10 of 166 governance patterns Agentomy enforces at runtime. Severity as classified in the pattern definition.

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.

ISO 26262the international functional-safety standard for road vehicles, which classifies hazards and sets software and hardware safety requirements; governance evidence maps to its monitoring and control expectationsUNECE WP.29 R157the first binding international regulation for automated lane-keeping systems, from the United Nations Economic Commission for Europe; it requires minimal-risk-condition behavior and a data-storage record that the audit trail supportsSAE J3016the industry taxonomy from SAE International defining driving-automation Levels 0 to 5 and the fallback duties at each; it is the shared language every fleet framework builds onNHTSA SGOthe US National Highway Traffic Safety Administration's Standing General Order requiring crash reporting for automated-driving systems within days; the record makes those filings complete and on time
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 Autonomous Fleet Governance
  • 100% Integrity
  • 47,415 Blocks
  • SHA-256 hash-linked
Audit trail: one tamper-evident block per governance decision, hash-linked to the one before it
BlockTimestampAgentActionTierHash
47,415Today 12:42:08route-research-agentdata_access_requestEvaluator09039564cf77293c
47,414Today 12:41:54incident-triage-agentoutput_validationAnalysta1eb7b0d4baab236
47,413Today 12:41:37parts-review-agentpolicy_checkBuildere3c510b82a9f24d5
47,412Today 12:40:58telemetry-extract-agentbehavior_driftOperatorf160e4f74e2a9448
47,411Today 12:40:21recall-draft-agenthalt_initiatedStrategistb7cbc625c0239b11
47,410Today 12:39:46fleet-report-agentprompt_reviewEvaluatore6a054f4e6c5a403
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.

Ready to govern your agents?

Talk to salesBack to Defense Contractors