Google Agent Development Kit (ADK): Collaborative AI Agent Architecture

கூகிள் ஏஜென்ட் டெவலப்மென்ட் கிட் (ADK): கூட்டுச் செயற்கை நுண்ணறிவு முகவர் கட்டமைப்பு Provide a comprehensive overview of ADK Architecture for Building Collaborative AI Agents - Analyze the architectural trade-offs between Sequential, Loop, and Parallel agent types. - Compare sequential workflows with graph-based or parallel agent architectures. - Describe the structure and benefits of using SequentialAgent for deterministic workflows. - Discuss how developers can chain agents using a SequentialAgent workflow. - Walk through building a multi-agent system for a code development pipeline. - Discuss using Output Key to pass data between agents in a pipeline. - Explain how to share session state between agents during multi-step processes. - Discuss when to choose custom agents over standard workflow agent patterns. - Describe how to integrate external tools using the Model Context Protocol. - Outline how to use FastMCP servers for building and exposing custom tools. - Compare when to use local versus remote agents for microservices architectures. - Outline essential steps for developers choosing between local sub-agents and remote A2A agents. - Contrast the usage of A2A versus local sub-agents with concrete examples. - Explain when to use A2A for integrating standalone services. - Explain the process of connecting specialized agents via the A2A protocol. - Summarize how developers can use the Gemini Live API Toolkit for streaming. - Detail how to implement safety guardrails for agent inputs and outputs. - Explain best practices for sandboxing model code execution to prevent security risks. - Compare plugins versus callbacks for enforcing uniform security policies across agents. - Explain how to use callbacks and plugins to implement security guardrails. - Focus on identity, authorization, and advanced plugins like Gemini as a Judge. - Explain how to implement a PII Redaction Plugin for data protection. - Discuss using Model Armor to prevent content safety violations. - Highlight tips for implementing effective user authentication with OAuth scopes. - Explain common risk scenarios like reward hacking and data exfiltration in production. - Detail how to implement VPC security controls to protect sensitive agent data. - Explain how to use the Code Executor tool for secure data analysis tasks. - Review how to configure content filters to block harmful model output automatically. - Illustrate how to deploy ADK agents on Google Cloud Run for production. - Show how to use observability tools like logging and traces to debug agent workflows. - Explain how to setup cross-language support between Python and Java agents. - Explain how to build a layered defense strategy against indirect prompt injection. - Analyze advanced strategies for context compression in long-running agent workflows. - Discuss best practices for managing state and memory in multi-agent systems. - Analyze trade-offs between agent-auth and user-auth for securing external tool access. - Discuss techniques for handling multi-language agent communication patterns effectively. - Describe about Ambient Agent Build Approaches