📚 Clojure + Google ADK + Vertex AI Laboratory

Version: 1.2.0 Last Updated: 2025-10-27 Objective: Complete knowledge base for developing production AI agents using Clojure, Google ADK, and Vertex AI

🎯 Laboratory Vision

This laboratory explores creating AI agent solutions using Clojure as the primary language, integrating:

•Google ADK (Agent Development Kit) via Java SDK (native JVM)
•Python libraries via libpython-clj (NumPy, HuggingFace, etc.)
•Vertex AI Agent Engine for deployment
•Functional programming for agent orchestration

🏗️ Technology Stack

Technology	Version	Purpose
Clojure	1.11+	Primary language
Java	17+	Runtime (JVM) and ADK SDK
Python	3.10+	Interop for ML/AI libraries
Google ADK	Latest	Agent framework
libpython-clj	2.x	Python interop
Vertex AI	-	Deployment platform

📖 Key Concepts

Agent Types (A/B/C/D Taxonomy)

This lab uses a validated taxonomy of agent types based on capabilities:

•Type A: Pure AI (input → LLM → output) - ~$0.02, ~3s
•Type B: AI + CAG (Context-Aware Generation with database) - ~$0.08, ~5s
•Type C: AI + Web (Grounding with external APIs) - ~$0.18, ~12s
•Type D: AI + CAG + Web (maximum context) - ~$0.42, ~17s

📘 Learn more: See cva-concepts-agent-types skill for detailed explanation and decision tree.

Multi-Model Strategy

Optimize costs by routing tasks to appropriate models:

•Gemini Flash (70%): Simple tasks, extraction, classification
•Claude Haiku (20%): Medium complexity, personalization
•Claude Sonnet (10%): Complex reasoning, consolidation

Result: 41% cost reduction vs Claude-only approach

🏥 Healthcare Pipeline (Production-Ready)

Complete 5-system pipeline for regulated medical content generation:

•S.1.1 (Type B): LGPD-compliant data extraction
•S.1.2 (Type A): Medical claims identification
•S.2-1.2 (Type C): Scientific reference search (PubMed, Scholar)
•S.3-2 (Type B): SEO optimization with professional profile
•S.4 (Type D): Final consolidation with compliance

Validated ROI

Real case: Clínica Mente Saudável (20 posts/month)

•⏱️ Time: 4h 15min → 1.5min (-99.4%)
•💰 Cost: R$ 192.50 → R$ 14.70 (-92.4%)
•📈 ROI: -R$ 3,850 → +R$ 3,094 (+180%)

📘 Learn more: See cva-healthcare-pipeline skill for complete implementation.

🚀 Quick Start Path

For Beginners (Clojure + ADK)

•Setup → See cva-setup-vertex (⭐ START HERE)
•Concepts → See cva-concepts-adk
•First Agent → Use /cva:new-agent command
•Deploy → Use /cva:deploy command

For Experienced Clojure Developers

•ADK Overview → See cva-concepts-adk
•Agent Types → See cva-concepts-agent-types
•Quick Reference → See cva-quickref-adk
•Advanced Patterns → See cva-patterns-workflows

For Production Healthcare Systems

•GCP Context → See cva-setup-vertex (credentials, costs)
•Agent Types → See cva-concepts-agent-types (understand A/B/C/D)
•Compliance → See cva-healthcare-compliance (LGPD, CFM, CRP)
•Pipeline → See cva-healthcare-pipeline (5-system workflow)
•Cost Optimization → See cva-patterns-cost (multi-model routing)

📋 Initial Setup Checklist

• Clojure installed (1.11+)
• Java 17+ installed
• Python 3.10+ installed
• Google Cloud SDK configured
• Vertex AI API enabled
• Clojure project created with deps.edn
• libpython-clj configured and tested
• Google ADK Java SDK added to project
• Google Cloud credentials configured

📘 Detailed instructions: See cva-setup-clojure, cva-setup-interop, and cva-setup-vertex skills.

🎯 Lab Objectives

•Explore Clojure capabilities for AI agent development
•Integrate Google ADK via Java SDK idiomatically
•Leverage Python libraries (HuggingFace, NumPy) via libpython-clj
•Develop architecture patterns for agents in Clojure
•Deploy agents to Vertex AI Agent Engine
•Document learnings and best practices

📊 Lab Status

•✅ Initial setup: Complete
•✅ GCP/Vertex context: Aggregated (project saas3-476116)
•✅ Validated credentials: Complete
•✅ Base documentation: Complete
•✅ Python ADK lessons: Documented
•✅ Healthcare pipeline knowledge: Aggregated (validated ROI)
•✅ Domain knowledge: Healthcare, multi-model strategies
•✅ Advanced patterns: Workflows, contexts, optimization
•📋 Production deployment: Planned

🔗 Related Skills

Setup & Configuration

•cva-setup-clojure - Clojure project setup
•cva-setup-interop - libpython-clj configuration
•cva-setup-vertex - Vertex AI & GCP setup ⭐

Core Concepts

•cva-concepts-adk - Google ADK architecture
•cva-concepts-agent-types - A/B/C/D taxonomy ⭐

Quick References

•cva-quickref-adk - ADK API cheatsheet
•cva-quickref-libpython - libpython-clj patterns

Patterns & Best Practices

•cva-patterns-workflows - Multi-agent workflows
•cva-patterns-context - Context management (CAG)
•cva-patterns-cost - Cost optimization ⭐

Healthcare Specialization

•cva-healthcare-pipeline - Complete 5-system pipeline ⭐
•cva-healthcare-compliance - Brazilian regulations (LGPD, CFM, CRP)
•cva-healthcare-seo - Medical SEO strategies

Case Studies

•cva-case-study-roi - Validated ROI analysis ⭐

🛠️ Available Commands

Use these slash commands for productive workflows:

•/cva:new-agent [type] - Create new agent scaffold (A/B/C/D)
•/cva:healthcare-workflow - Generate complete healthcare pipeline
•/cva:deploy [target] - Deploy to Vertex AI or Cloud Run
•/cva:cost-analysis - Analyze workflow costs and suggest optimizations

🎓 Learning Resources

Official Documentation

Community

💡 Key Insights

★ Functional Programming + AI Agents: Clojure's immutability and REPL-driven development are excellent for agent orchestration and testing.

★ JVM Native Advantage: Using Google ADK Java SDK directly (no Python wrapper) provides better performance and type safety.

★ Cost Optimization Matters: Multi-model strategy (Gemini Flash 70%, Claude 20%, Sonnet 10%) reduces costs by 41% vs single-model approach.

★ Type System for Agents: The A/B/C/D taxonomy based on capabilities (not implementation) enables systematic architecture decisions and cost optimization.

★ Healthcare ROI Validated: -99.4% time and -92.4% cost reduction proven in production with Clínica Mente Saudável case study.

This skill provides high-level context. Activate related skills for detailed implementation guidance.