Dashboard Intelligence: RAG Automation & Audit Semantics

Topology Visualization Meets Reality

Placeholder UIs are the enemy of trust. In earlier versions of our Intelligence Hub, the pipeline visualization was largely static. In the v0.5.0 Dashboard Intelligence release, we replaced these static elements with dynamic node-edge topological generators.

The intelligence bubbles now accurately trace *real* orchestration workflows, explicitly mapping the active agents (e.g., Mike AI, Legal Ops, Neural Auditor) to the actual Document Ingestion and Aggregation events.

Grounded RAG Insights

We also significantly upgraded the underlying RAG (Retrieval-Augmented Generation) infrastructure:

• Confidence Scores & Boundaries: The Intelligence Hub now renders high-fidelity Vector Search summaries complete with probabilistic Confidence Scores and explicit corpus boundaries.
• Traceability: We've injected exact clause snippet extractions directly into the Strategic AI Recommendations, ensuring users can verify the model's logic instantly.

Semantically Honest Audit Trails

A secure platform demands honest logging. Manual editor saves now produce semantically honest audit events. We scrubbed "System" as the actor, explicitly naming the human user instead. We replaced ambiguous "suggested" vs. "original" metadata keys with rigid before and after states.

Furthermore, every manual save now generates a beautiful indigo "Pre-Edit Snapshot" block directly in the audit trail, showing the first 120 characters of the before text. This gives legal teams immediate inline context without requiring them to open the full Version History modal.

By combining dynamic agent mapping with rigorous audit semantics, we are raising the bar for how human operators observe and verify multi-agent systems at scale.