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cva-overview

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Overview of Clojure + Google ADK + Vertex AI development environment. Comprehensive lab for building production AI agents using Clojure as primary language, integrating Google ADK via Java SDK and Python libraries via libpython-clj. Includes healthcare pipeline with validated ROI (-99.4% time, -92.4% cost). Use when starting new projects, understanding architecture, or needing general context about the stack.

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SKILL.md

name cva-overview
description Overview of Clojure + Google ADK + Vertex AI development environment. Comprehensive lab for building production AI agents using Clojure as primary language, integrating Google ADK via Java SDK and Python libraries via libpython-clj. Includes healthcare pipeline with validated ROI (-99.4% time, -92.4% cost). Use when starting new projects, understanding architecture, or needing general context about the stack.
allowed-tools Read,Bash,Edit,Write,Glob,Grep

📚 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:

  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

Core Concepts

Quick References

Patterns & Best Practices

Healthcare Specialization

Case Studies

🛠️ 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.