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Zero-Trust Semantic Infrastructure for Autonomous Cyber-Physical Systems, SCADA/MES Bridges, and Industrial AI Agent Control.
18 tools with complete schemas and descriptions, but significant quality gaps. Tool names lack clear verb-noun structure (e.g., 'iaig_route_local_slm', 'iaig_proxy_cloud_escalation' are vague). Descriptions average ~80 chars, below the 194-char baseline for A+ tools. Many descriptions lack WHEN/WHY context. Parameters are typed and mostly described, but lack actionable constraints (ranges, patterns, examples). Output schemas are not documented. Error handling is minimal, no recovery guidance or error classification. Security concerns: tools accept sensitive parameters (operator_oauth_token, biometric_token, tpm_2_signature) that should be server-injected. No audit trail or permission gates visible. Composition is poor: tools like 'iaig_sync_offline_cache' and 'iaig_throttle_tool_context' are infrastructure concerns, not user-facing actions.
Verifies if physical machinery satisfies hardcoded plant pre-conditions and safety interlocks.
Batches heavy operational datasets into compact analytical trends to save LLM tokens.
Translates high-level natural language instructions into deterministic ROS 2 Nav2 goals.
Returns localized vector-based anomaly alerts (e.g. Bearing wear index at 84%).
Fetches edge-computed text summaries of high-frequency sensor waves instead of raw numbers.
Runs incoming operator text strings through the AI Firewall to intercept prompt injections.
Vague tool names lack clear verb-noun structure. 'iaig_route_local_slm', 'iaig_proxy_cloud_escalation', 'iaig_throttle_tool_context' do not clearly convey action. LLMs cannot infer intent from names alone.
Descriptions lack WHEN/WHY context and actionable constraints. Average 80 chars vs 194-char baseline. No guidance on tool selection, prerequisites, or error recovery.
Sensitive parameters exposed as tool inputs: operator_oauth_token, biometric_token, tpm_2_signature, signed_command_payload. These must be server-injected via environment/vault, not passed by LLM.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | D | 54 | 2026-07-28+ | v2 |
Browses the ISA-95 hierarchical asset tree (Enterprise/Site/Area/Line/Cell/Asset).
Anonymizes data and pushes complex multi-site optimization problems to cloud LLM clusters.
Pulls deep historical time-series database logs for pattern and trend analysis.
Queues a human-in-the-loop approval request for a proposed industrial action.
Fetches real-time process variables from the Unified Namespace (UNS) over MQTT Sparkplug B or OPC UA.
Executes localized, low-latency diagnostic loops safely within air-gapped hardware.
Passes proposed machine commands through the physics-aware Digital Twin sandbox to evaluate safety.
Flushes and updates local tool-call logs built up during network dropouts.
Rate-limits and prioritizes tool execution to prevent resource exhaustion.
Reviews proposed parameters against an immutable mechanical safety profile.
Challenges physical hardware to prove the request originated from a trusted edge gateway TPM chip.
Binds active tool execution to an operator corporate OAuth2 session and Biometric IoT token.
No output schemas documented. LLMs cannot plan downstream calls or extract required fields. Responses are inferred from code only.
No error handling guidance. Tools return generic status strings (e.g., 'INTERLOCK_CHECK_FAILED') without recovery hints, retryability classification, or actionable error messages.