Robotics & Autonomous Systems

When software hallucinations become physical safety hazards.

Cryptographic governance for autonomous agents that operate in the physical world. When the output isn't text but motion, force, and physical interaction — governance must be enforcement, not monitoring.

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The problem

Autonomous systems make decisions with physical consequences.

Autonomous systems — mobile robots, unmanned vehicles, collaborative robots, industrial manipulators, drone swarms — are deploying AI planners that make real-time decisions with physical consequences. A software agent that hallucinates a navigation path through a restricted zone, issues a motor command outside safe parameters, or accepts an unauthorized instruction from a compromised peer creates risks that cannot be remediated after the fact.

Most AI governance tools focus on text. In robotics, the output isn't text — it's motion, force, and physical interaction with the world. The governance question isn't “did the AI say something inappropriate?” — it's “was this physical action authorized, and can you prove it decades from now?”

How Kevros helps

Cryptographic governance for physical actions.

Pre-Execution Authorization
Every agent action — navigation commands, manipulator trajectories, sensor access, peer-to-peer task delegation — requires a cryptographic release token before execution. If denied, no token is issued and the fail-closed architecture prevents execution.
Post-Quantum Command Chain Integrity
Robots and autonomous systems have operational lifetimes measured in decades. ML-DSA-87 (FIPS 204) post-quantum signatures ensure governance evidence remains verifiable in 2040 and beyond. Professional and Enterprise tiers.
Hash-Chained Provenance for Safety Investigations
Every governance decision is recorded in a tamper-evident provenance ledger. When an incident investigation or safety audit needs to reconstruct the sequence of authorized actions — the evidence chain is cryptographically complete.
Multi-Agent Trust for Swarms
In multi-robot systems, agents verify the identity and authorization of peer agents before accepting task delegation, sharing sensor data, or coordinating actions. Peer trust verification without a central authority.
Behavioral Drift Detection
Autonomous systems operating in unstructured environments face continuous environmental change. Kevros Professional and Enterprise tiers detect behavioral drift before it manifests as unsafe physical actions.
Code examples

Real SDK examples for robotics governance.

Official examples using the Kevros SDK. Governance for robotics applications uses the standard SDK and API protocols. All code on this page is the canonical reference.

Autonomous Navigation — Verify Before Executing Movement

from kevros_governance import GovernanceClient client = GovernanceClient(agent_id="amr-warehouse-014") # AI planner proposes a navigation waypoint result = client.verify( action_type="navigation_command", action_payload={ "waypoint": {"x": 12.4, "y": 8.7, "z": 0.0}, "velocity_mps": 1.5, "planner": "nav2_bt", "zone": "warehouse_floor_b", }, agent_id="amr-warehouse-014", ) if result.decision.value == "ALLOW": # Execute movement with governance proof send_to_nav_controller( waypoint=(12.4, 8.7, 0.0), release_token=result.release_token, ) elif result.decision.value == "CLAMP": # Movement parameters modified by governance clamped = result.applied_action send_to_nav_controller( waypoint=(clamped["x"], clamped["y"], clamped["z"]), release_token=result.release_token, ) elif result.decision.value == "DENY": # Halt — no release token issued halt_and_report(reason=result.reason)

Drone Swarm — Peer Trust Before Task Delegation

from kevros_governance import GovernanceClient, IntentType client = GovernanceClient(agent_id="swarm-coordinator") # Check trust of a worker drone before delegating a task peer = client.verify_peer("drone-scout-007") if peer.get("trust_score", 0) >= 0.9: # Bind the delegation intent bind = client.bind( agent_id="swarm-coordinator", intent_type=IntentType.AI_GENERATED, intent_description="Delegate area survey to drone-scout-007", command_payload={ "task": "area_survey", "delegate_to": "drone-scout-007", "survey_area": {"lat": 38.03, "lon": -78.48, "radius_m": 200}, }, goal_state={"survey_complete": True}, ) # Verify the delegation action itself result = client.verify( action_type="task_delegation", action_payload={ "delegate_to": "drone-scout-007", "task": "area_survey", "trust_score": peer.get("trust_score"), }, agent_id="swarm-coordinator", ) if result.decision.value == "ALLOW": delegate_task( drone="drone-scout-007", task=bind.intent_id, token=result.release_token, ) else: # Insufficient trust — do not delegate reassign_task("area_survey")

Manipulator Safety — Govern Tool Operations

from kevros_governance import GovernanceClient client = GovernanceClient(agent_id="cobot-assembly-arm-03") # AI planner wants to pick up a component result = client.verify( action_type="manipulator_action", action_payload={ "action": "pick_and_place", "target_object": "pcb-assembly-unit", "gripper_force_n": 15.0, "workspace_zone": "station_7_collaborative", "human_proximity": True, }, agent_id="cobot-assembly-arm-03", ) if result.decision.value == "ALLOW": execute_pick_place(token=result.release_token) elif result.decision.value == "CLAMP": # Force or speed may have been reduced execute_pick_place( token=result.release_token, params=result.applied_action, ) elif result.decision.value == "DENY": # Safety boundary violation — stop and alert safe_stop() alert_operator(reason=result.reason) # Record the action for safety audit client.attest( agent_id="cobot-assembly-arm-03", action_description="Pick and place PCB assembly at station 7", action_payload={ "action": "pick_and_place", "target_object": "pcb-assembly-unit", "gripper_force_n": result.applied_action.get("gripper_force_n", 15.0) if result.decision.value == "CLAMP" else 15.0, "outcome": "completed" if result.decision.value != "DENY" else "denied", }, )

Long-Lifetime Evidence — Compliance Bundle for Safety Audit

# Generate evidence bundle spanning an entire operational period bundle = client.bundle( agent_id="amr-warehouse-014", time_range_start="2026-01-01T00:00:00Z", time_range_end="2026-06-30T23:59:59Z", include_intent_chains=True, include_pqc_signatures=True, include_verification_instructions=True, ) print(f"Six months of governance evidence:") print(f" Records: {bundle.record_count}") print(f" Chain integrity: {bundle.chain_integrity}") print(f" Bundle hash: {bundle.bundle_hash}") # Present to safety auditor, insurer, or regulatory body

How Kevros fits in a robotics stack.

Kevros is not a real-time safety controller. It is a governance layer that sits between the AI planner and the execution layer:

AI Planner (Nav2, MoveIt, custom) → Kevros Verify → Release Token → Safety PLC/Controller → Actuators

The safety PLC or hardware interlock remains the last line of defense. Kevros adds a governance layer before the safety controller, providing authorization verification before commands reach the safety layer, cryptographic evidence that governance was applied, and tamper-evident records for post-incident investigation.

Kevros governance calls are network calls to the gateway. For latency-sensitive robotics applications, the Managed App deploys within your Azure tenant. For edge deployments with intermittent connectivity, the fail-closed architecture ensures that no action executes without verification — if the gateway is unreachable, the default is denial.

Relevant capabilities by tier.

CapabilityStarterProfessionalEnterprise
Pre-execution verification (ALLOW/CLAMP/DENY)
Cryptographic release tokens
Hash-chained provenance ledger
Peer trust verification
ML behavioral drift detection—
Post-quantum ML-DSA-87 signing—
SIEM export——
Compliance evidence bundles——
Compliance note

Kevros does NOT replace safety PLCs.

Kevros provides technical governance controls. It does not replace safety PLCs (IEC 61508 / SIL-rated), hardware emergency stops, safety-rated sensors and light curtains, or certified functional safety systems (ISO 13849, IEC 62443).

Kevros governance calls are network calls to the gateway — they are not real-time safety controllers. In safety-critical environments, Kevros provides an additional layer of authorization verification and evidence recording, not a substitute for certified safety controllers.

See our full compliance posture at /compliance.

What Kevros does NOT do in robotics

Precision about our capabilities.

Kevros verifies whether an AI agent is authorized to take a physical action. It does not:

Control robotic actuators or motor controllers directly
Replace safety PLCs, SIL-rated controllers, or hardware emergency stops
Perform real-time sensor fusion or machine vision
Generate navigation paths or motion plans (it governs actions based on plans)
Replace functional safety systems (IEC 61508, ISO 13849)
Provide a real-time operating system or deterministic control loop
Act as a native ROS 2 node or directly intercept ROS 2 topics (governance uses the SDK API)
Developer Note

Kevros integrates with robotics applications through the standard Python SDK:

  • Install via pip install kevros. Your ROS 2 node calls client.verify() and uses the release token in your execution logic.
  • Governance boundaries are configured server-side through the gateway.
  • Isaac Sim and Gazebo integration is through standard ROS 2 Python.

All code examples on this page are the canonical reference.

Govern autonomous actions with cryptographic proof.

Schedule a briefing. We'll walk through how Kevros governs physical actions, produces safety investigation evidence, and integrates with your existing robotics stack. Or deploy the Free Trial on Azure and test it yourself.

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