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+# **ADK Agent Analytics: SQL-First Advanced Capabilities Design Document**
+
+## **Executive Summary**
+
+This document outlines the architecture for the ADK BigQuery Agent Analytics Plugin, transitioned to a fully SQL-native implementation. By leveraging **BigQuery AI Functions** (AI.GENERATE, AI.EMBED, AI.EXTRACT), **BigQuery Graph**, and **BigQuery ML**, we enable sophisticated agent evaluation and memory management directly within the data warehouse.
+
+This approach addresses the primary challenges in the agentic space—observability, stateful memory, and trajectory evaluation—without the overhead of external Python middleware.
+
+## **Part 1: Trace-Based Evaluation Harness**
+
+### **1.1 Background & Motivation**
+
+Agent evaluation has shifted from binary success/failure metrics to comprehensive trajectory analysis. According to recent research ([Evaluation and Benchmarking of LLM Agents](https://arxiv.org/abs/2507.21504)), modern evaluation requires examining:
+
+* **Reasoning Layer**: Planning quality and dependency handling.  
+* **Action Layer**: Tool selection accuracy and argument correctness.  
+* **Overall Execution**: Step efficiency and task completion.
+
+### **1.2 SQL-Native LLM-as-Judge**
+
+We utilize AI.GENERATE to perform "forensic" analysis of traces. This replaces the need for external evaluation harnesses.
+
+```sql
+
+-- Batch Evaluate Agent Trajectories for Efficiency and Reasoning
+CREATE OR REPLACE TABLE `{project}.{dataset}.eval_results` AS
+WITH session_trajectories AS (
+  SELECT
+    session_id,
+    STRING_AGG(
+      FORMAT("Step %d: Tool=%s, Args=%s, Status=%s", 
+             step_index, 
+             JSON_EXTRACT_SCALAR(attributes, '$.tool_name'),
+             JSON_EXTRACT_SCALAR(attributes, '$.tool_args'),
+             JSON_EXTRACT_SCALAR(attributes, '$.status')),
+      "\n" ORDER BY timestamp ASC
+    ) AS trajectory_str
+  FROM `{project}.{dataset}.agent_events`
+  WHERE event_type IN ('TOOL_STARTING', 'TOOL_COMPLETED', 'TOOL_ERROR')
+  GROUP BY session_id
+)
+SELECT
+  session_id,
+  AI.GENERATE(
+    FORMAT("""
+      You are an expert AI Agent Evaluator. 
+      Analyze the following execution trajectory against the goal of step-efficiency.
+      Trajectory:
+      %s
+      
+      Provide a structured JSON response:
+      {
+        "task_completion": float (0-1),
+        "step_efficiency": float (0-1),
+        "tool_usage_accuracy": float (0-1),
+        "critique": string
+      }
+    """, trajectory_str)
+  ) AS evaluation_json
+FROM session_trajectories;
+
+```
+
+## **Part 2: Long-Horizon Agent Memory**
+
+### **2.1 Memory Architecture**
+
+According to [Memory in the Age of AI Agents](https://arxiv.org/abs/2512.13564), memory serves as the cornerstone for long-horizon reasoning. We implement three memory types natively:
+
+| Memory Type | Description | BigQuery Feature |
+| :---- | :---- | :---- |
+| **Episodic** | Recall of past similar interactions | AI.EMBED \+ VECTOR\_SEARCH |
+| **Semantic** | Learned facts and user preferences | AI.EXTRACT |
+| **Working** | Current session context | CTE-based windowing |
+
+### **2.2 Semantic Retrieval and Vector Search**
+
+This replaces traditional RAG pipelines by keeping embeddings and retrieval logic inside BigQuery.
+
+```sql
+
+-- 1. Create a Native Vector Store
+CREATE OR REPLACE TABLE `{project}.{dataset}.trace_vector_store` AS
+SELECT 
+  session_id, user_id, timestamp, content,
+  AI.EMBED(content) AS embedding
+FROM `{project}.{dataset}.agent_events` 
+WHERE event_type = 'AGENT_COMPLETED';
+
+-- 2. Retrieve Cross-Session Context for Current User
+CREATE OR REPLACE TABLE `{project}.{dataset}.user_memory_context` AS
+SELECT 
+  base.trace_text,
+  distance
+FROM VECTOR_SEARCH(
+  TABLE `{project}.{dataset}.trace_vector_store`,
+  'embedding',
+  (SELECT AI.EMBED("How did the user want their reports formatted in the past?")),
+  top_k => 3
+) AS search_results
+WHERE user_id = 'user_99';
+
+```
+
+### **2.3 Knowledge Extraction via AI.EXTRACT**
+
+Instead of storing raw chat logs, we "compress" interactions into structured facts using AI.EXTRACT, as suggested by the [CORAL framework](https://openreview.net/forum?id=NBGlItueYE).
+
+```sql
+
+-- Extracting User Preferences into Permanent Knowledge Store
+INSERT INTO `{project}.{dataset}.user_profiles` (user_id, profile_json)
+SELECT
+  user_id,
+  AI.EXTRACT(
+    STRING_AGG(content, " | "),
+    ['preferred language', 'reporting frequency', 'technical expertise level']
+  )
+FROM `{project}.{dataset}.agent_events`
+WHERE event_type = 'USER_MESSAGE_RECEIVED'
+GROUP BY user_id;
+
+```
+
+## **Part 3: Behavioral Graph Analytics**
+
+### **3.1 Topology Analysis**
+
+Modeling traces as a graph allows us to detect structural failures like "Delegation Loops" or "Dependency Deadlocks" which are difficult to query in flat tables.
+
+```sql
+
+-- Define the Trace Property Graph
+CREATE OR REPLACE PROPERTY GRAPH `{project}.{dataset}.agent_trace_graph`
+NODE TABLES (
+  `{project}.{dataset}.agents` KEY (agent_id),
+  `{project}.{dataset}.tools` KEY (tool_name),
+  `{project}.{dataset}.sessions` KEY (session_id)
+)
+EDGE TABLES (
+  `{project}.{dataset}.delegations`
+    SOURCE KEY (parent_id) REFERENCES agents (agent_id)
+    DESTINATION KEY (child_id) REFERENCES agents (agent_id)
+    LABEL delegates,
+  `{project}.{dataset}.tool_invocations`
+    SOURCE KEY (agent_id) REFERENCES agents (agent_id)
+    DESTINATION KEY (tool_name) REFERENCES tools (tool_name)
+    LABEL calls
+);
+
+```
+
+### **3.2 Scenario: Detecting Infinite Delegation Loops (Cycles)**
+
+In multi-agent systems, agents may enter an infinite loop by delegating back and forth. BigQuery Graph identifies these cycles instantly.
+
+```sql
+
+-- Detect cycles of length 2 to 5 in agent delegations
+SELECT *
+FROM GRAPH_TABLE(
+  `{project}.{dataset}.agent_trace_graph`,
+  MATCH (a)-[e:delegates]->{2,5}(a)
+  COLUMNS (a.agent_id, "Circular Delegation Detected" as issue_type)
+);
+
+```
+
+### **3.3 Scenario: Execution Bottleneck Detection (Centrality)**
+
+Identify "Hub" tools that are central to most failing traces. If a tool has high degree centrality in sessions that end with AGENT\_ERROR, it is a systemic bottleneck.
+
+```sql
+
+-- Find tools with the highest number of calls in failed sessions
+SELECT 
+  tool_name, 
+  COUNT(*) as call_count
+FROM GRAPH_TABLE(
+  `{project}.{dataset}.agent_trace_graph`,
+  MATCH (s:sessions)-[:includes]->(a:agents)-[c:calls]->(t:tools)
+  WHERE s.final_status = 'ERROR'
+  COLUMNS (t.tool_name)
+)
+GROUP BY tool_name
+ORDER BY call_count DESC;
+
+```
+
+### **3.4 Scenario: Data Lineage & Entity Propagation**
+
+Track how a specific entity (e.g., order\_id) propagates across different tools. This visualizes the "lineage" of a data point as it is transformed by the agent.
+
+```sql
+
+-- Trace the flow of a specific entity across the tool graph
+SELECT 
+  path
+FROM GRAPH_TABLE(
+  `{project}.{dataset}.agent_trace_graph`,
+  MATCH p = (t1:tools)-[:calls*]->(t2:tools)
+  WHERE t1.output_json LIKE '%order_123%' AND t2.input_json LIKE '%order_123%'
+  COLUMNS (JSON_ARRAY_AGG(t2.tool_name) as path)
+);
+
+```
+
+## **Part 4: Predictive & Diagnostic Analytics**
+
+### **4.1 Anomaly Detection**
+
+Using BigQuery ML's AUTOENCODER, we identify sessions that deviate from "normal" behavioral patterns (e.g., unusual tool-calling frequency).
+
+```sql
+
+-- Train Anomaly Detection Model
+CREATE OR REPLACE MODEL `{project}.{dataset}.behavior_anomaly_model`
+OPTIONS(model_type='AUTOENCODER') AS
+SELECT 
+  COUNTIF(event_type = 'TOOL_STARTING') as tools,
+  COUNTIF(event_type = 'LLM_REQUEST') as llms,
+  AVG(latency_ms) as lat
+FROM `{project}.{dataset}.agent_events`
+GROUP BY session_id;
+
+-- Detect and Explain Anomalies
+WITH outliers AS (
+  SELECT * FROM ML.DETECT_ANOMALIES(
+    MODEL `{project}.{dataset}.behavior_anomaly_model`,
+    STRUCT(0.01 AS contamination),
+    TABLE `{project}.{dataset}.session_metrics`
+  ) WHERE is_anomaly = TRUE
+)
+SELECT 
+  session_id,
+  AI.GENERATE(FORMAT("Explain why this session with %d tool calls is anomalous.", tool_count))
+FROM outliers;
+
+```
+
+## 
+
+## **References & Citations**
+
+### **Academic Research**
+
+* **Evaluation**: [TRAJECT-Bench: A Trajectory-Aware Benchmark](https://arxiv.org/html/2510.04550v1)  
+* **Memory**: [CORAL: Cognitive Resource Self-Allocation](https://openreview.net/forum?id=NBGlItueYE)  
+* **Context Management**: [JetBrains Research on Long-Horizon Agents (2025)](https://blog.jetbrains.com/research/2025/12/efficient-context-management/)
+
+### **Industry Documentation**
+
+* [BigQuery AI Functions Overview](https://cloud.google.com/bigquery/docs/ai-introduction)  
+* [BigQuery ML.DETECT\_ANOMALIES](https://cloud.google.com/bigquery/docs/anomaly-detection-overview)  
+* [BigQuery Property Graph Documentation](https://www.google.com/search?q=https://cloud.google.com/bigquery/docs/graph-introduction)