Cva Overview

📚 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

📖 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:

1. S.1.1 (Type B): LGPD-compliant data extraction
2. S.1.2 (Type A): Medical claims identification
3. S.2-1.2 (Type C): Scientific reference search (PubMed, Scholar)
4. S.3-2 (Type B): SEO optimization with professional profile
5. 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)

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

For Experienced Clojure Developers

1. ADK Overview → See cva-concepts-adk
2. Agent Types → See cva-concepts-agent-types
3. Quick Reference → See cva-quickref-adk
4. Advanced Patterns → See cva-patterns-workflows

For Production Healthcare Systems

1. GCP Context → See cva-setup-vertex (credentials, costs)
2. Agent Types → See cva-concepts-agent-types (understand A/B/C/D)
3. Compliance → See cva-healthcare-compliance (LGPD, CFM, CRP)
4. Pipeline → See cva-healthcare-pipeline (5-system workflow)
5. 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

1. Explore Clojure capabilities for AI agent development
2. Integrate Google ADK via Java SDK idiomatically
3. Leverage Python libraries (HuggingFace, NumPy) via libpython-clj
4. Develop architecture patterns for agents in Clojure
5. Deploy agents to Vertex AI Agent Engine
6. 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

• Clojure
• Google ADK
• Vertex AI
• libpython-clj

Community

• ClojureVerse
• Clojurians Slack
• Clojurians Slack #data-science

💡 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.