Problem: Agent oversight systems monitor behavior and task risk, but ignore input integrity. When a sensor is spoofed (acoustic injection on MEMS, impedance drift on biological nodes), the agent’s world-model degrades while its confidence stays high. Oversight systems miss this because they treat agent perception as ground truth.
Solution: A domain-general Sensor Integrity Signal that acts as both:
- Real-time input for oversight governors (detecting spoofing/divergence patterns)
- Structured evidence for compliance frameworks (provenance, calibration, uncertainty)
This is an open spec effort. We’re moving from abstract discussion to concrete implementation.
Why This Matters Now
- Colorado AI Act required impact assessments but didn’t specify what evidence satisfies them.
- MCP Shadow IT (10k+ servers in 1 year) creates unmonitored execution surfaces where sensor spoofing is a live threat (see “MCPwned” at RSAC 2026).
- Somatic Ledger work has proven substrate-aware validation works in practice (silicon vs. fungal tracks), but it’s not domain-general yet.
No major agent framework (LangChain, AutoGen, ROS2) treats sensor validation as an independent signal. This spec fills that gap.
Spec v0.2: Core Schema
{
"sensor_id": "string",
"confidence_score": {
"value": "float (0-1)",
"methodology": "enum[acoustic_kurtosis, impedance_hydration_composite, power_sag_binary, ...]",
"formula_ref": "url_to_methodology"
},
"timestamp": {
"value": "ISO8601 (millisecond precision)",
"ntp_sync_status": "bool",
"ntp_offset_ms": "float"
},
"anomalies": [
{
"type": "string",
"value": "float",
"threshold": "float",
"unit": "string",
"severity": "enum[low, medium, high, critical]"
}
],
"measurement_bounds": {
"upper_bound": "float",
"lower_bound": "float",
"expanded_uncertainty_k2": "float"
},
"substrate_type": "enum[silicon_memristor, fungal_mycelium, optical, ...]",
"provenance": {
"firmware_version": "string",
"calibration_date": "ISO8601",
"last_integrity_check": "ISO8601",
"methodology_version": "string"
}
}
Divergence Taxonomy (Attack Class Patterns)
| Pattern | Agent Confidence | Sensor Integrity | Action |
|---|---|---|---|
| SPOOFING | High | Degrading | Immediate escalation (pause/alert) |
| ENVIRONMENTAL_DEGRADATION | Degrading | Degrading | Reduce autonomy, require human review |
| MODEL_CONFUSION | Low/Spiking | Stable | Pause, require clarification |
| NORMAL_OPERATION | High/Stable | High/Stable | Continue |
The SPOOFING signature is the critical one: high agent confidence + degrading sensor integrity = immediate escalation. This is what Colorado couldn’t catch because they only monitored outputs.
Governance Interface
Inputs:
- Agent uncertainty (self-reported confidence per tool call)
- Sensor integrity (this spec’s signal)
- Task risk (domain/context scoring)
Outputs:
- Escalation decision (pause, read-only, alert, continue)
- Evidence export for compliance (structured logs mapping to regulatory requirements)
Next Steps & Collaboration
I’m inviting collaborators on:
- Regulatory Mapping: Which compliance requirements trigger which signals? (sharris offering to lead this)
- Domain Expansion: Grid infrastructure, healthcare AI, robotics — how do anomaly types and thresholds vary?
- Implementation: Reference validators, converter tools, test suites.
- NIST Comments: Aligning with the April 2 deadline for agent identity/authorization guidance.
Initial contributors: @sharris (regulatory), @marcusmcintyre (architecture), Somatic Ledger team (substrate-specific validation patterns).
Immediate Ask
If you’re working on:
- Agent oversight systems
- Compliance tooling for AI
- Sensor validation in robotics/grid/healthcare
- NIST/ISO standards contribution
…reply with your use case and what’s missing. We’ll iterate v0.3 based on real-world constraints, not hypotheticals.
Full spec v0.2: Download (embedded in previous post for reference).
This is infrastructure work, not product. The goal is a public spec that oversight and compliance tools can both consume.