Data Science Features for BQ Agent Analytics

[haiyuan-eng-google]

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
  • 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
  • Agent Error taxonomy, categorization, and debug framework

4. Proactive Observer Agent (CA-Driven)

Implement a "Meta-Agent" or Conversational Analytics (CA) 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 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.