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* chore: verify CI lint workflow and apply black/lint updates
* Overhaul autonomy docs and harden CPU chaos validation
- Add comprehensive autonomy core package (contracts, map/twin state, planner, predictor, readiness) with tests and integration points.
- Extend backend with autonomy insight endpoints and align OpenAPI coverage.
- Expand HUD and C2 surfaces with autonomy KPI, safety, recommendation, and timeline overlays.
- Normalize drone telemetry ingest confidence/health metadata and map query contract support.
- Switch node-agent to CPU-only PyTorch wheels to reduce container footprint in CI/dev runs.
- Harden chaos suite fallback by passing admin auth headers for trigger_fl.
- Fix monitoring package integration script/test wiring and refresh repository docs with a full autonomy operations guide plus updated README/C2/monitoring/changelog.
* Add concrete AI interaction implementation plan
- Turn the AI usability recommendations into a phased, ticketable plan.
- Cover command bar, structured recommendations, approval flow, model routing, metadata, mission context, and search.
- Define rollout criteria, validation strategy, and first-branch execution order for the new feature branch.
* Add shared AI interaction summary and operator UX
- add the /ai/interaction/summary backend payload with quick actions and recommendations
- wire the HUD and C2 surfaces to the shared interaction summary
- fix the app-shell fetch path, add focused C2 coverage, and refresh docs
- update the AI interaction plan to record completed work and follow-up items
* Complete AI interaction review workflow
- add interaction history and decision logging endpoints on the backend
- wire the HUD review drawer with approve, edit, reject, and undo flows
- pass review metadata through the app shell and action callbacks
- update C2, docs, and API specs to reflect the completed interaction plan
* Enhance HUD digital twin lens
Add digital twin lens panels for map freshness, route candidates, replay context, and envelope assumptions to strengthen operator twin perspective.
* Potential fix for pull request finding 'CodeQL / Unreachable code'
Co-authored-by: Copilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com>
Signed-off-by: Ryan <221235059+rwilliamspbg-ops@users.noreply.github.com>
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Signed-off-by: Ryan <221235059+rwilliamspbg-ops@users.noreply.github.com>
Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>
Co-authored-by: Copilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com>
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@@ -28,6 +28,7 @@ Traditional federated learning can look simple in a lab and fail badly in the fi
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- A small local node set exercises aggregation, policy checks, and proof verification.
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- The runtime exposes the same health path operators use in production-style demos.
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- The primary HUD and C2 view share a structured AI interaction summary so common actions stay explainable and easy to trigger.
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- The HUD now includes a review drawer and backend decision history so AI-suggested actions can be approved, edited, rejected, or undone with an audit trail.
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