📚 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

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🎯 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

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🏗️ 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

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📖 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

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🏥 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.

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🚀 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)

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📋 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.

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🎯 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

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📊 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

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🔗 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 ⭐

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🛠️ 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

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🎓 Learning Resources

Official Documentation

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

Community

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

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💡 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.

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