Connect your API key Pro auto-connects locally when you launch with `npx thumbgate pro`. Otherwise paste your `THUMBGATE_API_KEY` manually.

# 🔍 Operations Dashboard

Updated: 2026-04-20 · by Igor Ganapolsky

What's happening right now? Search memories, inspect active checks, manage your team, and export training data. This is your control plane for AI agent behavior.

🔍 Search — find any memory 🛡️ Gates — what's blocking 👥 Team — org metrics 📤 Export — DPO training data
📊 Demo Mode — sample data. Pro unlocks your personal dashboard with search, DPO export, and gate analytics. Start Pro now
Total Feedback
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👍 Positive
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👎 Negative
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Active Gates
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## 💬 Chat with your data

Ask about your gates, blocks, feedback, and lessons — answered locally from your own data, no cloud. (An optional BYO model only kicks in for open-ended questions.)
Powered by local dashboard data. Optional Gemini/Perplexity keys only expand open-ended analysis.

### 🆕 Since Last Review

No review checkpoint yet. Mark the current dashboard as reviewed to start seeing only new changes.

New feedback
0
events since checkpoint
New negatives
0
fresh review risk
New lessons
0
promoted since checkpoint
New blocks
0
gate hits since checkpoint

### Checkpoint

Waiting for dashboard data...

### Newest items

No new review activity yet.
🔍 Search Memories
🛡️ Active Gates
👥 Team
🏢 Governed Data Chat
🧬 AI Inventory
🧩 Generated Views
⚙️ Policy Origins
🧱 Gate Templates
📊 Insights
📦 Export
Enter a query to search your memories

## Active Pre-Action Checks

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## Shared Team Reliability

See which agents drift, which gates save the team the most time, and whether the shared workflow is getting safer.

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### Highest-Risk Agents

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### Top Blocked Gates

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### Predictive Watchlist

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### Agent Surface Inventory

See which tools, policy sources, and MCP surfaces are actually active before you widen rollout.

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### Observed Tools

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### Policy Sources

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### Background Agent Mode

Queued and scheduled agents need explicit checkpoints before they touch code, money, or customer systems.

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### Governance Pressure

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### Run Types

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### Regulated Buyer Proof

Show where policy came from, how many runs were reviewed, and which proof artifacts are ready when buyers ask for auditability.

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### Policy Origin Proof

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### Latest Replay Artifacts

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## Governed Data Chat

Ask questions over your local ThumbGate data (lessons, raw feedback via LanceDB vectors, gate stats, receipts). Uses local retrieval plus your configured LLM, including open-source/local endpoints through `THUMBGATE_LOCAL_LLM_ENDPOINT`. Dialogflow is not the chatbot layer; DFCX/Vertex cards are optional deployment-readiness checks for buyers who run their own Google agent stack.

### Chat With Local ThumbGate Data

Connect your dashboard, then ask about local ThumbGate data.

### Runtime Readiness

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The chat itself is local RAG plus your LLM. Open-source/local models are the default enterprise path; DFCX/Vertex readiness is optional evidence for teams that already operate Google agents in their own tenancy.

## AI Component Inventory

Enterprise evidence for AI/ML usage: provider SDKs, agent frameworks, vector databases, Vertex/Gemini/Dialogflow CX references, and local model artifacts. Export this as ML-BOM proof before claiming an AI system is production-ready.

Scanning AI inventory...

### Detected Components

Scanning components...

### Export Evidence

CLI export:

    npx thumbgate ai-inventory --format=cyclonedx --output=.thumbgate/ai-mlbom.json

API export:

    GET /v1/dashboard/ai-inventory?format=cyclonedx

Waiting for inventory evidence...

## Generated Hosted Views

A constrained JSON spec builds opinionated review dashboards from approved cards, lists, and callouts.

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## Policy Origins

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### Layer Precedence

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### Routing Preview

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### Resolved Setting Origins

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## Curated Gate Templates

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## 📊 Feedback Insights & Lesson Pipeline

How your thumbs-up/down signals turn into lessons that prevent repeat mistakes.

### 💸 Estimated tokens saved

Computed from your real blocked-action count × 2,000 input + 600 output tokens per avoided retry, priced at a Sonnet-heavy blend (80% Sonnet 4.5 / 15% Opus 4.6 / 5% Haiku 4.5). Conservative estimate — actual savings may be higher.
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— tokens · from — blocked calls

### Feedback → Lesson Pipeline

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### Feedback Trend (30 days)

### Lessons Generated (30 days)

### Gate Effectiveness (14-day audit)

### Highest-ROI Next Actions

These recommendations come from real feedback, risk patterns, and delegation pressure in your current runtime.

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### How ThumbGate Learns

👎👍
1. You React
Thumbs-down on a bad action, thumbs-up on a good one
🧠
2. Lesson Distilled
ThumbGate extracts what went wrong and how to avoid it
🛡️
3. Gate Promoted
Repeated lessons auto-promote into pre-action checks
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4. Mistake Blocked
Gates intercept the same mistake before it happens again

## Export Training Data (DPO)

### What is DPO?

Direct Preference Optimization is a technique for fine-tuning LLMs using human preference data. Your 👍/👎 feedback is converted into training pairs:

👍 "chosen" — the response that worked
👎 "rejected" — the response that failed

Use these pairs to fine-tune any model (OpenAI, Llama, Mistral) so it actually learns from your corrections — not just blocks mistakes, but stops making them.