docs(tac): add NVIDIA ComputeX agent ecosystem appendix

Folds the Adel + Chris Murphy talk (youtube.com/watch?v=SVWmuJx0hHM)
into loop_engineering.md. Covers the third inflection point
(ChatGPT → DeepSeek → OpenClaw autonomous agents), agent =
model + harness, OpenShell secure runtime with policy-based
egress and secrets management, AIQ multi-agent deep research
blueprint (intent router → orchestrator → specialists →
synthesizer at 50% lower cost), NemoClaw blueprints, Nemo Relay
observability with ATIF traces, and the Verified Skills pattern.
Maps each component to TAC's existing factory infrastructure.

plans/meta-prompts/loop_engineering.md

@@ -256,3 +256,25 @@ Reference: model-routing cost-savings tutorial (YouTube `SUZwYV5JYBM`), Coinbase
 - The brainstorms/spec skills naturally produce the architect's spec → handoff to builder pattern
 - Third-party harnesses (Cursor auto mode, Not Diamond) handle this automatically if configured
 - The Agent multiplexer note's orchestrator can route planning to one pane (Tier 1) and execution to another (Tier 2)
+
+## NVIDIA agent ecosystem — Neotron, OpenShell, Hermes
+
+Reference: NVIDIA ComputeX 2026 keynote — Adel (Senior Director AI Agents) and Chris Murphy (PM NemoClaw/OpenShell) at YouTube `SVWmuJx0hHM`.
+
+**Third inflection point:** ChatGPT (text gen, 2022) → DeepSeek (reasoning, 2024) → OpenClaw (autonomous self-evolving agents, Jan 2026). We're in the autonomous agent era.
+
+**Agent = model + harness.** The harness (tooling, orchestration, APIs, data sources) is as important as the model. NVIDIA's contribution spans the full stack.
+
+**Key architectures for TAC:**
+
+| Component | What it does | TAC relevance |
+|-----------|-------------|---------------|
+| **OpenShell** | Open-source secure agent runtime — policy-based egress control, secrets management outside sandbox, human-in-the-loop policy review | Natural complement to TAC's insider-threat model. Agent never sees real keys. Already being built into Windows and Ubuntu OS. |
+| **AIQ Deep Research** | Multi-agent system: Neotron Nano (intent router) → GPT 5.2/Opus (orchestrator+planner) → Neotron 3 Super (5-6 specialist researchers) → synthesizer. #1 on Deep Research Bench at 50% lower cost. | Reference architecture for TAC's model routing. Intent router (small/cheap) decides workflow, frontier handles planning, mids handle execution. |
+| **NemoClaw Blueprints** | Open-source reference implementations combining agent + OpenShell + models/tools. Hermes and OpenClaw variants. Customizable starting points, not end products. | TAC's factory already runs Hermes (3 variants) and OpenClaw. OpenShell sandboxing could be added around existing agents. |
+| **Nemo Relay** | Agent observability + optimization. ATIF format traces, learns call patterns, hints Anthropic caching strategy, feeds KV-cache hints to Dynamo. ~30% token cost reduction. | TAC's model routing could integrate learned call-pattern hints. The ATIF trace format is human-readable and cross-platform. |
+| **Verified Skills** | NVIDIA CUDA-X libraries packaged as plain-English skill manuals, scanned for vulnerabilities, evaluated across harnesses/models, cryptographically signed. | Patterns TAC can apply: skill metadata (what a tool is good/bad for), signature verification at runtime. |
+
+**ServiceNow case study:** 90% of L1 tickets resolved by autonomous agents using AIQ blueprint. System of models (frontier for orchestration, open for specialist researchers) with human-in-the-loop for escalation.
+
+**Practical takeaway:** TAC already aligns with the system-of-models pattern (see Model routing appendix). The next step is OpenShell-style policy gating around the Hermes/OpenClaw agents in the factory — sandbox the agents that have access to internal data sources (email, forgejo, git-proxy deploy) and keep human approval on policy changes.