Data Science Features for BQ Agent Analytics

## **Core Pillars for Development**

### **1\. Automated Benchmarking & "Hill Climbing"**

Use production traces to automatically synthesize evaluation sets.

* **Action:** Identify high-signal "success" vs. "failure" traces in BQ.
* **Goal:** Create a DS-driven optimizer that tweaks Prompts/Skills and tests them against these auto-generated benchmarks to "hill climb" toward better performance.
* Extract the benchmark from traces to generate the benchmark for 3p agents and make the agent can self-improving.

### **2\. Self-Improving Agents (The "Diary" Loop)**

Enable agents to consume their own execution history via BQAA SDK.

* **Action:** Integration with the CLI allows agents to read traces, detect latency spikes, and identify reasoning gaps.
* **Goal:** Agents that proactively update their own "skills" or tool-calling logic based on past failures.

* Auto-skills
* [https://arxiv.org/pdf/2603.01145v1](https://arxiv.org/pdf/2603.01145v1)
* The central idea of AutoSkill is to treat repeated interaction experience not merely as memory, but as a source of skill formation. Instead of storing only dialogue snippets or preference records, AutoSkill abstracts reusable behaviors from user interactions and crystallizes them into explicit skill artifacts.

### **3\. Root Cause Attribution & Diagnostics**

Develop a generic attribution system to identify why agents fail.

* **Action:** Model BQAA data to distinguish between failures in **Harness/Prompting**, **System Architecture**, or **Model Capacity**.
* **Goal:** Clearer developer signals on whether to fix the prompt, the RAG flow, or upgrade the model.

- [WHERE LLM AGENTS FAIL AND HOW THEY CANLEARN FROM FAILURES](https://arxiv.org/pdf/2509.25370)
- Agent Error taxonomy, categorization, and debug framework

### **4\. Proactive Observer Agent (CA-Driven)**

Implement a "Meta-Agent" or [Conversational Analytics (CA)](https://medium.com/google-cloud/the-closed-loop-for-agent-observability-and-analysis-connecting-adk-bigquery-and-d8fe54971b35) to monitor live streams.

* **Action:** An observer that reads BQAA data in real-time.
* **Goal:** Provide proactive guidance to the primary agent or alert developers when an agent enters a reasoning loop or "drifts" from its goal.

### **5\. "Hatteras-Style" Analytics Dashboard**

A high-fidelity visualization layer for Agents, like current Hatteras support

* **Action:** Visualize cost-per-task, success rates by version, and bottleneck heatmaps.
* **Goal:** Real-time mission control for agent fleet health..

### **6\. Reasoning Bank**

Design [Long term storage](https://buganizer.corp.google.com/issues/499088135) to store distilled information about AI agent successful/failed interaction. Stored memory snippets will contain a summary of interactions, resolution steps in case of success and key learnings.

* **Actions:**
* Design Long term memory storage.
* Add functionality to synthesize and store memories.
* Provide Agentic tools to use long term memory.
* **Goal:** Provide functionality which can help to increase AI agent success rate and decrease the number of steps needed to resolve issues.