A comprehensive collection of whitepapers covering AI agents from introduction to production deployment. This repository serves as a one-stop resource for learning about agent architecture, tools, context engineering, quality assurance, and production deployment.

This repository contains five whitepapers from Google's AI Agents workshop series, providing in-depth coverage of building and deploying AI agents. Each whitepaper focuses on a critical aspect of agent development, from fundamental concepts to production-ready systems.

White Papers/
├── 1 Introduction to Agents.pdf
├── 2 Agent Tools & Interoperability with Model Context Protocol (MCP).pdf
├── 3 Context Engineering_ Sessions & Memory.pdf
├── 4 Agent Quality.pdf
└── 5 Prototype to Production.pdf

Whitepaper: 1 Introduction to Agents.pdf

Coverage:

• Taxonomy of agent capabilities
• Introduction to AI agents and their architecture
• Agent Ops discipline for reliability and governance
• Agent interoperability and security through identity and constrained policies

Key Topics:

• Multi-agent systems
• Architectural patterns for agents
• Agent capabilities and classifications
• Security and governance frameworks

Additional Resources:

• Build your first agent using Gemini and ADK
• Build your first multi-agent systems using ADK

Whitepaper: 2 Agent Tools & Interoperability with Model Context Protocol (MCP).pdf

Coverage:

• External tools functions that allow agents to perform actions beyond their training set
• Best practices for designing effective tools
• Model Context Protocol (MCP) architecture and components
• MCP communication layer, risks, and enterprise readiness gaps

Key Topics:

• Tool integration and design patterns
• Long-running operations
• Human-in-the-loop workflows
• MCP protocol specifications

Additional Resources:

• Explore new ways to add tools to extend agent capabilities
• Implement MCP and long-running operations

Whitepaper: 3 Context Engineering_ Sessions & Memory.pdf

Coverage:

• Context engineering as the practice of dynamically assembling and managing information within an agent's context window
• Creating stateful and personalized AI experiences
• Sessions: containers for single, immediate conversation history
• Memory: long-term persistence mechanisms

Key Topics:

• Context window management
• Session state management
• Working memory vs. long-term memory
• Stateful agent design patterns

Additional Resources:

• Build stateful agents and perform context engineering
• Implement memory systems for agents

Whitepaper: 4 Agent Quality.pdf

Coverage:

• Holistic evaluation framework for AI agents
• Observability foundation built on three pillars:
  • Logs: The diary (detailed execution records)
  • Traces: The narrative (end-to-end request flow)
  • Metrics: The health report (performance indicators)
• Continuous feedback loops using:
  • LLM-as-a-Judge evaluation
  • Human-in-the-Loop (HITL) evaluation

Key Topics:

• Agent observability and monitoring
• Debugging agent failures
• Evaluation methodologies
• Quality assurance frameworks

Additional Resources:

• Implement observability for agent debugging
• Evaluate agent response quality and tool usage

Whitepaper: 5 Prototype to Production.pdf

Coverage:

• Technical guide to the operational lifecycle of AI agents
• Deployment, scaling, and productionization strategies
• Challenges of transitioning agentic systems from prototypes to enterprise-grade solutions
• Agent2Agent (A2A) Protocol for inter-agent communication

Key Topics:

• Production deployment patterns
• Scaling agent systems
• Agent-to-agent communication
• Enterprise-grade agent architectures

Additional Resources:

• Explore A2A Protocol for agent interactions
• Deploy agents to production environments

• Agent Development Kit (ADK): Google's framework for building agents
• Gemini: Google's AI model powering the agents
• Model Context Protocol (MCP): Protocol for agent interoperability
• Agent2Agent (A2A) Protocol: Protocol for agent-to-agent communication
• Vertex AI Agent Engine: Google Cloud service for deploying agents

• Agent architecture and taxonomy
• Multi-agent systems and coordination
• Tool integration and design patterns
• Context engineering and optimization
• Session and memory management
• Observability (Logs, Traces, Metrics)
• Agent evaluation and quality assurance
• Production deployment and scaling
• Agent interoperability protocols

1. Start with the Fundamentals: Begin with "Introduction to Agents" to understand the core concepts
2. Progress Sequentially: Each whitepaper builds upon previous concepts
3. Hands-on Practice: Use the Kaggle notebooks for practical implementation
4. Reference Guide: Use these whitepapers as a reference when building your own agents

• Agent Development Kit (ADK) Documentation
• Gemini API Documentation
• Model Context Protocol (MCP)
• Vertex AI Agent Engine

This is an educational resource repository. If you find errors, have suggestions, or want to add complementary materials, contributions are welcome!

Disclaimer: This repository is a curated collection of educational resources. The whitepapers contained herein are the property of Google and are provided for educational and reference purposes only.

• The whitepapers (PDF files) in this repository are copyrighted by Google
• This repository serves as a centralized resource for accessing these publicly available educational materials
• The organization, curation, and README documentation are maintained for educational purposes
• Please respect Google's copyright and usage terms for the original workshop materials
• This repository does not claim ownership of the whitepapers or any content created by Google

• Google: For providing the comprehensive whitepapers on AI agents
• Kaggle: For hosting practical codelabs and notebooks

A curated resource collection for AI agent development 🚀