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Decision Frameworks

Purpose: Master strategic decision-making for ADK implementation - when to use which patterns, tools, and deployment strategies.

Source of Truth: google/adk-python/src/google/adk/ (ADK 1.15) + production case studies

Table of Contents​

  1. Pattern Selection Framework
    • Choose the right agent type
  2. Tool Selection Matrix
    • Function vs OpenAPI vs MCP tools
  3. Performance Optimization
    • Cost vs speed trade-offs
  4. Deployment Strategy
    • Choose local, cloud, or managed deployment
  5. Security & Compliance
    • Protect agents and data
  6. Monitoring & Observability
    • Track performance in production
  7. Implementation Checklist
    • Step-by-step deployment guide

Pattern Selection Framework​

Agent Type Decision Tree​

When to Use Each Agent Type​

Agent TypeWhen to UseExample Use Cases
LLM AgentSingle-step tasks, pure reasoningQ&A, analysis, simple classification
SequentialAgentOrdered dependencies, pipeline workflowsData processing → analysis → reporting
ParallelAgentIndependent tasks, speed optimizationMulti-source data collection, parallel analysis
LoopAgentIterative refinement, quality improvementCode review, content editing, optimization

🔧 Tool Selection Matrix​

Function Tools vs OpenAPI vs MCP​

Tool Decision Criteria​

CriteriaFunction ToolsOpenAPI ToolsMCP Tools
Development SpeedFastestMediumSlowest
MaintenanceHighestMediumLowest
FlexibilityMaximumLimitedMedium
InteroperabilityNoneLimitedMaximum
SecurityCustomAPI KeysBuilt-in

⚡ Performance Optimization​

Cost vs Speed Trade-offs​

Model Selection Guide​

Use CaseRecommended ModelReasoning
Fast Responsesgemini-2.0-flashSpeed optimized, cost effective
Complex Reasoninggemini-2.0-flash-thinkingBuilt-in chain-of-thought
Code Generationgemini-2.0-flashStrong coding capabilities
Multimodalgemini-2.0-flashVision, audio, video support
Live Interactiongemini-2.0-flash-liveReal-time streaming

🚀 Deployment Strategy Matrix​

Environment Selection​

Deployment Decision Factors​

FactorLocalCloud RunAgent EngineGKE
Setup TimeFastestFastMediumSlowest
ScalingManualAutomaticAutomaticAutomatic
CostFreePay-per-usePay-per-useInfrastructure
CustomizationMaximumLimitedLimitedMaximum
MonitoringBasicBasicAdvancedAdvanced

🔒 Security & Compliance​

Data Handling Strategy​

State Scope Guidelines​

Data TypeRecommended ScopeRetentionEncryption
User Preferencesuser:PermanentAlways
Session Contextsession:SessionOptional
Temporary Datatemp:RequestOptional
Application Configapp:PermanentAlways
Sensitive PIIuser:PermanentRequired

📊 Monitoring & Observability​

Alert Thresholds​

Key Metrics to Monitor​

  • Performance: Latency, throughput, error rates
  • Cost: Token usage, API costs, infrastructure costs
  • Quality: Task completion rates, user satisfaction
  • Reliability: Uptime, error recovery, fallback success

🎯 Implementation Checklist​

Pre-Production Validation​

  • Agent configuration tested with realistic data
  • Tool integrations verified end-to-end
  • Error handling covers all failure modes
  • Performance benchmarks meet requirements
  • Security review completed
  • Cost estimates validated
  • Monitoring and alerting configured
  • Rollback plan documented

Production Readiness​

  • Load testing completed
  • Disaster recovery tested
  • Documentation updated
  • Team training completed
  • Support processes established
  • Compliance requirements met

🎯 Key Takeaways​

  1. Pattern Selection: Match agent types to task complexity and dependencies
  2. Tool Choice: Balance development speed vs long-term maintenance
  3. Performance: Optimize for cost, speed, or quality based on priorities
  4. Deployment: Choose environment based on scale and customization needs
  5. Security: Use appropriate state scopes and encryption for data sensitivity
  6. Monitoring: Establish clear thresholds and comprehensive observability

🔗 Next: Follow structured Learning Paths to master ADK development.