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Xiang (Sean) Zhou
855 lines
41 KiB
Python
855 lines
41 KiB
Python
# Copyright 2025 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Cache Analysis Research Assistant Agent.
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This agent is designed to test ADK context caching features with a large prompt
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that exceeds 2048 tokens to meet both implicit and explicit cache requirements.
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"""
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import random
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import time
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from typing import Any
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from typing import Dict
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from typing import List
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from typing import Optional
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from dotenv import load_dotenv
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from google.adk import Agent
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from google.adk.agents.context_cache_config import ContextCacheConfig
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from google.adk.apps.app import App
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# Load environment variables from .env file
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load_dotenv()
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def analyze_data_patterns(
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data: str, analysis_type: str = "comprehensive"
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) -> Dict[str, Any]:
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"""Analyze data patterns and provide insights.
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This tool performs comprehensive data analysis including statistical analysis,
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trend identification, anomaly detection, correlation analysis, and predictive
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modeling. It can handle various data formats including CSV, JSON, XML, and
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plain text data structures.
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Args:
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data: The input data to analyze. Can be structured (JSON, CSV) or
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unstructured text data. For structured data, include column headers
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and ensure proper formatting. For time series data, include
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timestamps in ISO format.
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analysis_type: Type of analysis to perform. Options include:
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- "comprehensive": Full statistical and trend analysis
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- "statistical": Basic statistical measures only
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- "trends": Time series and trend analysis
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- "anomalies": Outlier and anomaly detection
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- "correlations": Correlation and relationship analysis
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- "predictive": Forecasting and prediction models
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Returns:
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Dictionary containing analysis results with the following structure:
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{
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"summary": "High-level summary of findings",
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"statistics": {...}, # Statistical measures
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"trends": {...}, # Trend analysis results
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"anomalies": [...], # List of detected anomalies
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"correlations": {...}, # Correlation matrix and relationships
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"predictions": {...}, # Forecasting results if applicable
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"recommendations": [...] # Actionable insights and recommendations
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}
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"""
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# Simulate analysis processing time
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time.sleep(0.1)
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return {
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"summary": f"Analyzed {len(data)} characters of {analysis_type} data",
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"statistics": {
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"data_points": len(data.split()),
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"analysis_type": analysis_type,
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"processing_time": "0.1 seconds",
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},
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"recommendations": [
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"Continue monitoring data trends",
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"Consider additional data sources for correlation analysis",
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],
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}
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def research_literature(
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topic: str,
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sources: Optional[List[str]] = None,
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depth: str = "comprehensive",
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time_range: str = "recent",
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) -> Dict[str, Any]:
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"""Research academic and professional literature on specified topics.
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This tool performs comprehensive literature research across multiple academic
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databases, professional journals, conference proceedings, and industry reports.
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It can analyze research trends, identify key authors and institutions, extract
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methodological approaches, and synthesize findings across multiple sources.
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The tool supports various research methodologies including systematic reviews,
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meta-analyses, bibliometric analysis, and citation network analysis. It can
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identify research gaps, emerging trends, and future research directions in
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the specified field of study.
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Args:
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topic: The research topic or query. Can be specific (e.g., "context caching
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in large language models") or broad (e.g., "machine learning optimization").
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Use specific keywords and phrases for better results. Boolean operators
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(AND, OR, NOT) are supported for complex queries.
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sources: List of preferred sources to search. Options include:
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- "academic": Peer-reviewed academic journals and papers
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- "conference": Conference proceedings and presentations
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- "industry": Industry reports and white papers
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- "patents": Patent databases and intellectual property
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- "preprints": ArXiv, bioRxiv and other preprint servers
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- "books": Academic and professional books
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depth: Research depth level:
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- "comprehensive": Full literature review with detailed analysis
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- "focused": Targeted search on specific aspects
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- "overview": High-level survey of the field
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- "technical": Deep technical implementation details
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time_range: Time range for literature search:
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- "recent": Last 2 years
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- "current": Last 5 years
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- "historical": All available time periods
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- "decade": Last 10 years
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Returns:
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Dictionary containing research results:
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{
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"summary": "Executive summary of findings",
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"key_papers": [...], # Most relevant papers found
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"authors": [...], # Key researchers in the field
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"institutions": [...], # Leading research institutions
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"trends": {...}, # Research trends and evolution
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"methodologies": [...], # Common research approaches
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"gaps": [...], # Identified research gaps
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"citations": {...}, # Citation network analysis
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"recommendations": [...] # Future research directions
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}
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"""
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if sources is None:
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sources = ["academic", "conference", "industry"]
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# Simulate research processing
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time.sleep(0.2)
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return {
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"summary": f"Conducted {depth} literature research on '{topic}'",
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"key_papers": [
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f"Recent advances in {topic.lower()}: A systematic review",
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f"Methodological approaches to {topic.lower()} optimization",
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f"Future directions in {topic.lower()} research",
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],
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"trends": {
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"emerging_topics": [f"{topic} optimization", f"{topic} scalability"],
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"methodology_trends": [
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"experimental validation",
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"theoretical analysis",
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],
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},
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"recommendations": [
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f"Focus on practical applications of {topic}",
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"Consider interdisciplinary approaches",
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"Investigate scalability challenges",
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],
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}
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def generate_test_scenarios(
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system_type: str,
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complexity: str = "medium",
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coverage: Optional[List[str]] = None,
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constraints: Optional[Dict[str, Any]] = None,
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) -> Dict[str, Any]:
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"""Generate comprehensive test scenarios for system validation.
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This tool creates detailed test scenarios, test cases, and validation protocols
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for various types of systems including software applications, AI models,
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distributed systems, and hardware components. It supports multiple testing
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methodologies including unit testing, integration testing, performance testing,
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security testing, and user acceptance testing.
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The tool can generate both positive and negative test cases, edge cases,
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boundary conditions, stress tests, and failure scenarios. It incorporates
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industry best practices and testing frameworks to ensure comprehensive
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coverage and reliable validation results.
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Args:
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system_type: Type of system to test. Supported types include:
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- "software": Software applications and services
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- "ai_model": Machine learning and AI model testing
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- "distributed": Distributed systems and microservices
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- "database": Database systems and data integrity
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- "api": API endpoints and web services
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- "hardware": Hardware components and embedded systems
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- "security": Security systems and protocols
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complexity: Test complexity level:
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- "basic": Essential functionality tests only
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- "medium": Standard test suite with common scenarios
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- "advanced": Comprehensive testing with edge cases
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- "expert": Exhaustive testing with stress and chaos scenarios
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coverage: List of testing areas to cover:
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- "functionality": Core feature testing
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- "performance": Speed, throughput, and scalability
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- "security": Authentication, authorization, data protection
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- "usability": User experience and interface testing
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- "compatibility": Cross-platform and integration testing
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- "reliability": Fault tolerance and recovery testing
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constraints: Testing constraints and requirements:
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{
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"time_limit": "Maximum testing duration",
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"resources": "Available testing resources",
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"environment": "Testing environment specifications",
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"compliance": "Regulatory or standard requirements"
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}
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Returns:
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Dictionary containing generated test scenarios:
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{
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"overview": "Test plan summary and objectives",
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"scenarios": [...], # Detailed test scenarios
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"test_cases": [...], # Individual test cases
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"edge_cases": [...], # Boundary and edge conditions
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"performance_tests": [...], # Performance validation tests
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"security_tests": [...], # Security and vulnerability tests
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"automation": {...}, # Test automation recommendations
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"metrics": {...}, # Success criteria and metrics
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"schedule": {...} # Recommended testing timeline
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}
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"""
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if coverage is None:
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coverage = ["functionality", "performance", "security"]
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if constraints is None:
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constraints = {"time_limit": "standard", "resources": "adequate"}
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# Simulate test generation
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time.sleep(0.15)
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num_scenarios = {"basic": 5, "medium": 10, "advanced": 20, "expert": 35}.get(
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complexity, 10
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)
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return {
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"overview": (
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f"Generated {num_scenarios} test scenarios for {system_type} system"
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),
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"scenarios": [
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f"Test scenario {i+1}:"
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f" {system_type} {coverage[i % len(coverage)]} validation"
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for i in range(num_scenarios)
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],
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"test_cases": [
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f"Verify {system_type} handles normal operations",
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f"Test {system_type} error handling and recovery",
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f"Validate {system_type} performance under load",
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],
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"metrics": {
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"coverage_target": f"{75 + complexity.index(complexity) * 5}%",
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"success_criteria": "All critical tests pass",
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"performance_benchmark": f"{system_type} specific benchmarks",
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},
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}
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def optimize_system_performance(
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system_type: str,
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current_metrics: Dict[str, Any],
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target_improvements: Dict[str, Any],
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constraints: Optional[Dict[str, Any]] = None,
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) -> Dict[str, Any]:
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"""Analyze system performance and provide detailed optimization recommendations.
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This tool performs comprehensive system performance analysis including bottleneck
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identification, resource utilization assessment, scalability planning, and provides
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specific optimization strategies tailored to the system type and constraints.
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Args:
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system_type: Type of system to optimize:
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- "web_application": Frontend and backend web services
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- "database": Relational, NoSQL, or distributed databases
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- "ml_pipeline": Machine learning training and inference systems
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- "distributed_cache": Caching layers and distributed memory systems
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- "microservices": Service-oriented architectures
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- "data_processing": ETL, stream processing, batch systems
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- "api_gateway": Request routing and API management systems
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current_metrics: Current performance metrics including:
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{
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"response_time_p95": "95th percentile response time in ms",
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"throughput_rps": "Requests per second",
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"cpu_utilization": "Average CPU usage percentage",
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"memory_usage": "Memory consumption in GB",
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"error_rate": "Error percentage",
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"availability": "System uptime percentage"
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}
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target_improvements: Desired performance targets:
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{
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"response_time_improvement": "Target reduction in response time",
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"throughput_increase": "Desired increase in throughput",
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"cost_reduction": "Target cost optimization percentage",
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"availability_target": "Desired uptime percentage"
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}
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constraints: Operational constraints:
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{
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"budget_limit": "Maximum budget for improvements",
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"timeline": "Implementation timeline constraints",
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"technology_restrictions": "Required or forbidden technologies",
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"compliance_requirements": "Security/regulatory constraints"
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}
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Returns:
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Comprehensive optimization analysis:
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{
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"performance_analysis": {
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"bottlenecks_identified": ["Critical performance bottlenecks"],
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"root_cause_analysis": "Detailed analysis of performance issues",
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"current_vs_target": "Gap analysis between current and target metrics"
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},
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"optimization_recommendations": {
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"infrastructure_changes": ["Hardware/cloud resource recommendations"],
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"architecture_improvements": ["System design optimizations"],
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"code_optimizations": ["Software-level improvements"],
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"configuration_tuning": ["Parameter and setting adjustments"]
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},
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"implementation_roadmap": {
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"phase_1_quick_wins": ["Immediate improvements (0-2 weeks)"],
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"phase_2_medium_term": ["Medium-term optimizations (1-3 months)"],
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"phase_3_strategic": ["Long-term architectural changes (3-12 months)"]
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},
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"expected_outcomes": {
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"performance_improvements": "Projected performance gains",
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"cost_implications": "Expected costs and savings",
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"risk_assessment": "Implementation risks and mitigation strategies"
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}
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}
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"""
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# Simulate comprehensive performance optimization analysis
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optimization_areas = [
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"Database query optimization",
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"Caching layer enhancement",
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"Load balancing improvements",
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"Resource scaling strategies",
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"Code-level optimizations",
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"Infrastructure upgrades",
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]
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return {
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"system_analyzed": system_type,
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"optimization_areas": random.sample(
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optimization_areas, k=min(4, len(optimization_areas))
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),
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"performance_score": random.randint(65, 95),
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"implementation_complexity": random.choice(["Low", "Medium", "High"]),
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"estimated_improvement": f"{random.randint(15, 45)}%",
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"recommendations": [
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"Implement distributed caching for frequently accessed data",
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"Optimize database queries and add strategic indexes",
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"Configure auto-scaling based on traffic patterns",
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"Implement asynchronous processing for heavy operations",
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],
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}
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def analyze_security_vulnerabilities(
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system_components: List[str],
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security_scope: str = "comprehensive",
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compliance_frameworks: Optional[List[str]] = None,
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threat_model: str = "enterprise",
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) -> Dict[str, Any]:
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"""Perform comprehensive security vulnerability analysis and risk assessment.
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This tool conducts detailed security analysis including vulnerability identification,
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threat modeling, compliance gap analysis, and provides prioritized remediation
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strategies based on risk levels and business impact.
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Args:
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system_components: List of system components to analyze:
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- "web_frontend": User interfaces, SPAs, mobile apps
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- "api_endpoints": REST/GraphQL APIs, microservices
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- "database_layer": Data storage and access systems
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- "authentication": User auth, SSO, identity management
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- "data_processing": ETL, analytics, ML pipelines
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- "infrastructure": Servers, containers, cloud services
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- "network_layer": Load balancers, firewalls, CDNs
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security_scope: Analysis depth:
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- "basic": Standard vulnerability scanning
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- "comprehensive": Full security assessment
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- "compliance_focused": Regulatory compliance analysis
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- "threat_modeling": Advanced threat analysis
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compliance_frameworks: Required compliance standards:
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["SOC2", "GDPR", "HIPAA", "PCI-DSS", "ISO27001"]
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threat_model: Threat landscape consideration:
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- "startup": Basic threat model for early-stage companies
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- "enterprise": Corporate threat landscape
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- "high_security": Government/financial sector threats
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- "public_facing": Internet-exposed systems
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Returns:
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Security analysis results:
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{
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"vulnerability_assessment": {
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"critical_vulnerabilities": ["High-priority security issues"],
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"moderate_risks": ["Medium-priority concerns"],
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"informational": ["Low-priority observations"],
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"risk_score": "Overall security risk rating (1-10)"
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},
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"threat_analysis": {
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"attack_vectors": ["Potential attack methods"],
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"threat_actors": ["Relevant threat actor profiles"],
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"attack_likelihood": "Probability assessment",
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"potential_impact": "Business impact analysis"
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},
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"compliance_status": {
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"framework_compliance": "Compliance percentage per framework",
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"gaps_identified": ["Non-compliant areas"],
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"certification_readiness": "Readiness for compliance audits"
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},
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"remediation_plan": {
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"immediate_actions": ["Critical fixes (0-2 weeks)"],
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"short_term_improvements": ["Important fixes (1-2 months)"],
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"strategic_initiatives": ["Long-term security enhancements"],
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"resource_requirements": "Personnel and budget needs"
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}
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}
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"""
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# Simulate security vulnerability analysis
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vulnerability_types = [
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"SQL Injection",
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"Cross-Site Scripting (XSS)",
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"Authentication Bypass",
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"Insecure Direct Object References",
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"Security Misconfiguration",
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"Sensitive Data Exposure",
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"Insufficient Logging",
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"CSRF",
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]
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return {
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"components_analyzed": len(system_components),
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"critical_vulnerabilities": random.randint(0, 3),
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"moderate_risks": random.randint(2, 8),
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"overall_security_score": random.randint(6, 9),
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"compliance_percentage": random.randint(75, 95),
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"top_recommendations": [
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"Implement input validation and parameterized queries",
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"Enable comprehensive security logging and monitoring",
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"Review and update authentication and authorization controls",
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"Conduct regular security training for development team",
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],
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}
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def design_scalability_architecture(
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current_architecture: str,
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expected_growth: Dict[str, Any],
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scalability_requirements: Dict[str, Any],
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technology_preferences: Optional[List[str]] = None,
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) -> Dict[str, Any]:
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"""Design comprehensive scalability architecture for anticipated growth.
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This tool analyzes current system architecture and designs scalable solutions
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to handle projected growth in users, data, traffic, and complexity while
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maintaining performance, reliability, and cost-effectiveness.
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Args:
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current_architecture: Current system architecture type:
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- "monolith": Single-tier monolithic application
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- "service_oriented": SOA with multiple services
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- "microservices": Containerized microservice architecture
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- "serverless": Function-as-a-Service architecture
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- "hybrid": Mixed architecture patterns
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expected_growth: Projected growth metrics:
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{
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"user_growth_multiplier": "Expected increase in users",
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"data_volume_growth": "Projected data storage needs",
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"traffic_increase": "Expected traffic growth percentage",
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"geographic_expansion": "New regions/markets",
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"feature_complexity": "Additional functionality scope"
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}
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scalability_requirements: Scalability constraints and targets:
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{
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"performance_sla": "Response time requirements",
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"availability_target": "Uptime requirements",
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"consistency_model": "Data consistency needs",
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"budget_constraints": "Cost limitations",
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"deployment_model": "On-premise/cloud preferences"
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}
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technology_preferences: Preferred or required technologies:
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["kubernetes", "aws", "microservices", "nosql", etc.]
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Returns:
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Scalability architecture design:
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{
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"architecture_recommendation": {
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"target_architecture": "Recommended architecture pattern",
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"migration_strategy": "Path from current to target architecture",
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"technology_stack": "Recommended technologies and frameworks"
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},
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"scalability_patterns": {
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"horizontal_scaling": "Auto-scaling and load distribution strategies",
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"data_partitioning": "Database sharding and data distribution",
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"caching_strategy": "Multi-level caching implementation",
|
|
"async_processing": "Background job and queue systems"
|
|
},
|
|
"infrastructure_design": {
|
|
"compute_resources": "Server/container resource planning",
|
|
"data_storage": "Database and storage architecture",
|
|
"network_topology": "CDN, load balancing, and routing",
|
|
"monitoring_observability": "Logging, metrics, and alerting"
|
|
},
|
|
"implementation_phases": {
|
|
"foundation_setup": "Core infrastructure preparation",
|
|
"service_decomposition": "Breaking down monolithic components",
|
|
"data_migration": "Database and storage transitions",
|
|
"traffic_migration": "Gradual user traffic transition"
|
|
}
|
|
}
|
|
"""
|
|
# Simulate scalability architecture design
|
|
architecture_patterns = [
|
|
"Event-driven microservices",
|
|
"CQRS with Event Sourcing",
|
|
"Federated GraphQL architecture",
|
|
"Serverless-first design",
|
|
"Hybrid cloud architecture",
|
|
"Edge-computing integration",
|
|
]
|
|
|
|
return {
|
|
"recommended_pattern": random.choice(architecture_patterns),
|
|
"scalability_factor": f"{random.randint(5, 50)}x current capacity",
|
|
"implementation_timeline": f"{random.randint(6, 18)} months",
|
|
"estimated_cost_increase": f"{random.randint(20, 80)}%",
|
|
"key_technologies": random.sample(
|
|
[
|
|
"Kubernetes",
|
|
"Docker",
|
|
"Redis",
|
|
"PostgreSQL",
|
|
"MongoDB",
|
|
"Apache Kafka",
|
|
"Elasticsearch",
|
|
"AWS Lambda",
|
|
"CloudFront",
|
|
],
|
|
k=4,
|
|
),
|
|
"success_metrics": [
|
|
"Response time under load",
|
|
"Auto-scaling effectiveness",
|
|
"Cost per transaction",
|
|
"System availability",
|
|
],
|
|
}
|
|
|
|
|
|
def benchmark_performance(
|
|
system_name: str,
|
|
metrics: Optional[List[str]] = None,
|
|
duration: str = "standard",
|
|
load_profile: str = "realistic",
|
|
) -> Dict[str, Any]:
|
|
"""Perform comprehensive performance benchmarking and analysis.
|
|
|
|
This tool conducts detailed performance benchmarking across multiple dimensions
|
|
including response time, throughput, resource utilization, scalability limits,
|
|
and system stability under various load conditions. It supports both synthetic
|
|
and realistic workload testing with configurable parameters and monitoring.
|
|
|
|
The benchmarking process includes baseline establishment, performance profiling,
|
|
bottleneck identification, capacity planning, and optimization recommendations.
|
|
It can simulate various user patterns, network conditions, and system configurations
|
|
to provide comprehensive performance insights.
|
|
|
|
Args:
|
|
system_name: Name or identifier of the system to benchmark. Should be
|
|
specific enough to identify the exact system configuration
|
|
being tested.
|
|
metrics: List of performance metrics to measure:
|
|
- "latency": Response time and request processing delays
|
|
- "throughput": Requests per second and data processing rates
|
|
- "cpu": CPU utilization and processing efficiency
|
|
- "memory": Memory usage and allocation patterns
|
|
- "disk": Disk I/O performance and storage operations
|
|
- "network": Network bandwidth and communication overhead
|
|
- "scalability": System behavior under increasing load
|
|
- "stability": Long-term performance and reliability
|
|
duration: Benchmarking duration:
|
|
- "quick": 5-10 minutes for rapid assessment
|
|
- "standard": 30-60 minutes for comprehensive testing
|
|
- "extended": 2-4 hours for stability and endurance testing
|
|
- "continuous": Ongoing monitoring and measurement
|
|
load_profile: Type of load pattern to simulate:
|
|
- "constant": Steady, consistent load throughout test
|
|
- "realistic": Variable load mimicking real usage patterns
|
|
- "peak": High-intensity load testing for capacity limits
|
|
- "stress": Beyond-capacity testing for failure analysis
|
|
- "spike": Sudden load increases to test elasticity
|
|
|
|
Returns:
|
|
Dictionary containing comprehensive benchmark results:
|
|
{
|
|
"summary": "Performance benchmark executive summary",
|
|
"baseline": {...}, # Baseline performance measurements
|
|
"results": {...}, # Detailed performance metrics
|
|
"bottlenecks": [...], # Identified performance bottlenecks
|
|
"scalability": {...}, # Scalability analysis results
|
|
"recommendations": [...], # Performance optimization suggestions
|
|
"capacity": {...}, # Capacity planning insights
|
|
"monitoring": {...} # Ongoing monitoring recommendations
|
|
}
|
|
"""
|
|
if metrics is None:
|
|
metrics = ["latency", "throughput", "cpu", "memory"]
|
|
|
|
# Simulate benchmarking
|
|
time.sleep(0.3)
|
|
|
|
return {
|
|
"summary": f"Completed {duration} performance benchmark of {system_name}",
|
|
"baseline": {
|
|
"avg_latency": f"{random.uniform(50, 200):.2f}ms",
|
|
"throughput": f"{random.randint(100, 1000)} requests/sec",
|
|
"cpu_usage": f"{random.uniform(20, 80):.1f}%",
|
|
},
|
|
"results": {
|
|
metric: f"Measured {metric} performance within expected ranges"
|
|
for metric in metrics
|
|
},
|
|
"recommendations": [
|
|
f"Optimize {system_name} for better {metrics[0]} performance",
|
|
f"Consider scaling {system_name} for higher throughput",
|
|
"Monitor performance trends over time",
|
|
],
|
|
}
|
|
|
|
|
|
# Create the cache analysis research assistant agent
|
|
cache_analysis_agent = Agent(
|
|
name="cache_analysis_assistant",
|
|
model="gemini-2.0-flash-001",
|
|
description="""
|
|
Advanced Research and Analysis Assistant specializing in comprehensive system analysis,
|
|
performance benchmarking, literature research, and test scenario generation for
|
|
technical systems and AI applications.
|
|
""",
|
|
instruction="""
|
|
|
|
You are an expert Research and Analysis Assistant with deep expertise across multiple
|
|
technical domains, specializing in comprehensive system analysis, performance optimization,
|
|
security assessment, and architectural design. Your role encompasses both strategic planning
|
|
and tactical implementation guidance for complex technical systems.
|
|
|
|
**Core Competencies and Expertise Areas:**
|
|
|
|
**Data Analysis & Pattern Recognition:**
|
|
- Advanced statistical analysis including multivariate analysis, time series forecasting,
|
|
regression modeling, and machine learning applications for pattern discovery
|
|
- Trend identification across large datasets using statistical process control, anomaly
|
|
detection algorithms, and predictive modeling techniques
|
|
- Root cause analysis methodologies for complex system behaviors and performance issues
|
|
- Data quality assessment and validation frameworks for ensuring analytical integrity
|
|
- Visualization design principles for effective communication of analytical findings
|
|
- Business intelligence and reporting strategies for different stakeholder audiences
|
|
|
|
**Academic & Professional Research:**
|
|
- Systematic literature reviews following PRISMA guidelines and meta-analysis techniques
|
|
- Citation network analysis and research impact assessment using bibliometric methods
|
|
- Research gap identification through comprehensive domain mapping and trend analysis
|
|
- Synthesis methodologies for integrating findings from diverse research sources
|
|
- Research methodology design including experimental design, survey methods, and case studies
|
|
- Peer review processes and academic publication strategies for research dissemination
|
|
- Industry research integration including white papers, technical reports, and conference proceedings
|
|
- Patent landscape analysis and intellectual property research for innovation assessment
|
|
|
|
**Test Design & Validation:**
|
|
- Comprehensive test strategy development following industry frameworks (ISTQB, TMMI, TPI)
|
|
- Test automation architecture design including framework selection and implementation strategies
|
|
- Quality assurance methodologies encompassing functional, non-functional, and security testing
|
|
- Risk-based testing approaches for optimizing test coverage within resource constraints
|
|
- Continuous integration and deployment testing strategies for DevOps environments
|
|
- Performance testing including load, stress, volume, and endurance testing protocols
|
|
- Usability testing methodologies and user experience validation frameworks
|
|
- Compliance testing for regulatory requirements across different industries
|
|
|
|
**Performance Engineering & Optimization:**
|
|
- System performance analysis using APM tools, profiling techniques, and monitoring strategies
|
|
- Capacity planning methodologies for both current needs and future growth projections
|
|
- Scalability assessment including horizontal and vertical scaling strategies
|
|
- Resource optimization techniques for compute, memory, storage, and network resources
|
|
- Database performance tuning including query optimization, indexing strategies, and partitioning
|
|
- Caching strategies implementation across multiple layers (application, database, CDN)
|
|
- Load balancing and traffic distribution optimization for high-availability systems
|
|
- Performance budgeting and SLA definition for service-level agreements
|
|
|
|
**Security & Compliance Analysis:**
|
|
- Comprehensive security risk assessment including threat modeling and vulnerability analysis
|
|
- Security architecture review and design for both defensive and offensive security perspectives
|
|
- Compliance framework analysis for standards including SOC2, GDPR, HIPAA, PCI-DSS, ISO27001
|
|
- Incident response planning and security monitoring strategy development
|
|
- Security testing methodologies including penetration testing and security code review
|
|
- Privacy impact assessment and data protection strategy development
|
|
- Security training program design for technical and non-technical audiences
|
|
- Cybersecurity governance and policy development for organizational security posture
|
|
|
|
**System Architecture & Design:**
|
|
- Distributed systems design including microservices, service mesh, and event-driven architectures
|
|
- Cloud architecture design for AWS, Azure, GCP with multi-cloud and hybrid strategies
|
|
- Scalability patterns implementation including CQRS, Event Sourcing, and saga patterns
|
|
- Database design and data modeling for both relational and NoSQL systems
|
|
- API design following REST, GraphQL, and event-driven communication patterns
|
|
- Infrastructure as Code (IaC) implementation using Terraform, CloudFormation, and Ansible
|
|
- Container orchestration with Kubernetes including service mesh and observability
|
|
- DevOps pipeline design encompassing CI/CD, monitoring, logging, and alerting strategies
|
|
|
|
**Research Methodology Framework:**
|
|
|
|
**Systematic Approach:**
|
|
- Begin every analysis with clear problem definition, success criteria, and scope boundaries
|
|
- Establish baseline measurements and define key performance indicators before analysis
|
|
- Use structured analytical frameworks appropriate to the domain and problem type
|
|
- Apply scientific methods including hypothesis formation, controlled experimentation, and validation
|
|
- Implement peer review processes and cross-validation techniques when possible
|
|
- Document methodology transparently to enable reproducibility and peer verification
|
|
|
|
**Information Synthesis:**
|
|
- Integrate quantitative data with qualitative insights for comprehensive understanding
|
|
- Cross-reference multiple authoritative sources to validate findings and reduce bias
|
|
- Identify conflicting information and analyze reasons for discrepancies
|
|
- Synthesize complex technical concepts into actionable business recommendations
|
|
- Maintain awareness of information currency and source reliability
|
|
- Apply critical thinking to distinguish correlation from causation in analytical findings
|
|
|
|
**Quality Assurance Standards:**
|
|
- Implement multi-stage review processes for all analytical outputs
|
|
- Use statistical significance testing and confidence intervals where appropriate
|
|
- Clearly distinguish between established facts, supported inferences, and speculative conclusions
|
|
- Provide uncertainty estimates and risk assessments for all recommendations
|
|
- Include limitations analysis and recommendations for additional research or data collection
|
|
- Ensure all analysis follows industry best practices and professional standards
|
|
|
|
**Communication and Reporting Excellence:**
|
|
|
|
**Audience Adaptation:**
|
|
- Tailor communication style to technical level and role of the intended audience
|
|
- Provide executive summaries for strategic decision-makers alongside detailed technical analysis
|
|
- Use progressive disclosure to present information at appropriate levels of detail
|
|
- Include visual elements and structured formats to enhance comprehension
|
|
- Anticipate questions and provide preemptive clarification on complex topics
|
|
|
|
**Documentation Standards:**
|
|
- Follow structured reporting templates appropriate to the analysis type
|
|
- Include methodology sections that enable reproduction of analytical work
|
|
- Provide clear action items with priority levels and implementation timelines
|
|
- Include risk assessments and mitigation strategies for all recommendations
|
|
- Maintain version control and change tracking for iterative analytical processes
|
|
|
|
**Tool Utilization Guidelines:**
|
|
|
|
When users request analysis or research, strategically leverage the available tools:
|
|
|
|
**For Data Analysis Requests:**
|
|
- Use analyze_data_patterns for statistical analysis, trend identification, and pattern discovery
|
|
- Apply appropriate statistical methods based on data type, sample size, and research questions
|
|
- Provide confidence intervals and statistical significance testing where applicable
|
|
- Include data visualization recommendations and interpretation guidance
|
|
|
|
**For Literature Research:**
|
|
- Use research_literature for comprehensive academic and professional literature reviews
|
|
- Focus on peer-reviewed sources while including relevant industry reports and white papers
|
|
- Provide synthesis of findings with identification of research gaps and conflicting viewpoints
|
|
- Include citation analysis and research impact assessment when relevant
|
|
|
|
**For Testing Strategy:**
|
|
- Use generate_test_scenarios for comprehensive test planning and validation protocol design
|
|
- Balance test coverage with practical constraints including time, budget, and resource limitations
|
|
- Include both functional and non-functional testing considerations
|
|
- Provide automation recommendations and implementation guidance
|
|
|
|
**For Performance Analysis:**
|
|
- Use benchmark_performance for detailed performance assessment and optimization analysis
|
|
- Include both current performance evaluation and future scalability considerations
|
|
- Provide specific, measurable recommendations with expected impact quantification
|
|
- Consider cost implications and return on investment for optimization recommendations
|
|
|
|
**For System Optimization:**
|
|
- Use optimize_system_performance for comprehensive system improvement strategies
|
|
- Include both technical optimizations and operational process improvements
|
|
- Provide phased implementation approaches with quick wins and long-term strategic initiatives
|
|
- Consider interdependencies between system components and potential unintended consequences
|
|
|
|
**For Security Assessment:**
|
|
- Use analyze_security_vulnerabilities for comprehensive security risk evaluation
|
|
- Include both technical vulnerabilities and procedural/operational security gaps
|
|
- Provide risk-prioritized remediation plans with business impact consideration
|
|
- Include compliance requirements and regulatory considerations
|
|
|
|
**For Architecture Design:**
|
|
- Use design_scalability_architecture for strategic technical architecture planning
|
|
- Consider both current requirements and future growth projections
|
|
- Include technology stack recommendations with rationale and trade-off analysis
|
|
- Provide migration strategies and implementation roadmaps for architecture transitions
|
|
|
|
**Professional Standards and Ethics:**
|
|
|
|
**Analytical Integrity:**
|
|
- Maintain objectivity and avoid confirmation bias in all analytical work
|
|
- Acknowledge limitations in data, methodology, or analytical scope
|
|
- Provide balanced perspectives that consider alternative explanations and interpretations
|
|
- Use peer review and validation processes to ensure analytical quality
|
|
- Stay current with best practices and methodological advances in relevant domains
|
|
|
|
**Stakeholder Communication:**
|
|
- Provide clear, actionable recommendations that align with organizational capabilities
|
|
- Include risk assessments and uncertainty estimates for all strategic recommendations
|
|
- Consider implementation feasibility including technical, financial, and organizational constraints
|
|
- Offer both immediate tactical improvements and long-term strategic initiatives
|
|
- Maintain transparency about analytical processes and potential sources of error
|
|
|
|
Your ultimate goal is to provide insights that are technically rigorous, strategically sound,
|
|
and practically implementable. Every analysis should contribute to improved decision-making
|
|
and measurable business outcomes while maintaining the highest standards of professional
|
|
excellence and analytical integrity.
|
|
""",
|
|
tools=[
|
|
analyze_data_patterns,
|
|
research_literature,
|
|
generate_test_scenarios,
|
|
benchmark_performance,
|
|
optimize_system_performance,
|
|
analyze_security_vulnerabilities,
|
|
design_scalability_architecture,
|
|
],
|
|
)
|
|
|
|
# Create the app with context caching configuration
|
|
# Note: Context cache config is set at the App level
|
|
cache_analysis_app = App(
|
|
name="cache_analysis",
|
|
root_agent=cache_analysis_agent,
|
|
context_cache_config=ContextCacheConfig(
|
|
min_tokens=4096,
|
|
ttl_seconds=600, # 10 mins for research sessions
|
|
cache_intervals=3, # Maximum invocations before cache refresh
|
|
),
|
|
)
|
|
|
|
# Export as app since it's an App, not an Agent
|
|
app = cache_analysis_app
|
|
|
|
# Backward compatibility export - ADK still expects root_agent in some contexts
|
|
root_agent = cache_analysis_agent
|