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 ?Total thumbs-up and thumbs-down signals captured from your AI agent interactions.
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👍 Positive ?Successful outcomes where the agent's work met expectations (thumbs-up).
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👎 Negative ?Unsuccessful runs or errors that we need to learn from to prevent repeat failures (thumbs-down).
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Active Gates ?Pre-action checks actively monitoring and blocking known-bad tool calls before they run.
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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 ?Shows new feedback, negative signals, lessons, and blocks registered since your last baseline checkpoint.

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

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### 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

### 📈 Feedback Trend (Last 30 Days)

Enter a query to search your memories

## Active Pre-Action Checks

🛡️ How Pre-Action Gating Works (Interactive Flow Diagram)
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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.

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### Detected Components

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### Export Evidence

CLI export:

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

API export:

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

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## 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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### 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 flags mistakes at the gate, but stops making them.