Exploring Modern AI in Tamil

Sivakumar Viyalan
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This show explores practical, real-world applications of modern AI tools in Tamil for better understanding. Gen AI (Generative AI ) is AI that can create original content such as text, images, video, audio or software code in response to a user’s prompt or request. Agentic AI - Autonomous systems that make decisions and execute tasks independently to achieve goals. Agentic AI acts as a partner rather than just a tool, transforming industries through intelligent planning and multi-agent collaboration. Audio is AI generated by Google's NotebookLM. Images by Google's Gemini.

  1. Google Antigravity 2.0: From Now On, We Are No Longer Coders—We Are Agent Managers

    May 25

    Google Antigravity 2.0: From Now On, We Are No Longer Coders—We Are Agent Managers

    கூகிள் ஆன்டிகிராவிட்டி 2.0: இனிமேல், நாம் குறியீட்டாளர்கள் அல்ல—நாம் ஏஜென்ட் மேலாளர்கள் This episode of Exploring Modern AI in Tamil podcast provides a simple guide for someone setting up their first project in Antigravity. - Includes steps to initialize a new folder and associate your local repositories. - Explains the difference between Local Mode and New Worktree Mode. - Describes how to use Planning Mode and verify Artifacts before final execution. - Explains how to invoke subagents for parallel tasks and manage their lifecycles. - Explains the Request Review policy versus the Always Proceed policy for artifacts. - Details the built-in subagent types like research and browser for better task automation. - Describes how to enable the multi-agent teamwork framework for complex tasks. - Explains how to enable Build with Google bundles for Firebase or Android projects. - Explains how to use the teamwork-preview command for collaborative multi-agent orchestration. - Details the nesting depth limits for hierarchical subagent delegation structures. - Details how to use system instructions for customizing agent persona and behavior. - Explains file-based customization using AGENTS.md and SKILL.md directory structures. - Details the iterative workflow for testing and persisting custom managed agents. - Explains how to use the teamwork-preview command for advanced multi-agent orchestration. - Details how to resolve common communication issues between parent agents and subagents. - Details the network configuration options for locking down agent outbound access. - Summarizes how parent agents effectively manage state and context across multiple subagents. - Details how subagents inherit safety boundaries and permission scopes from their parent agent. - Explains how to configure network allowlists to restrict agent outbound access. - Outlines steps to stabilize environments and transition prototypes into managed agents. - Shares tips for providing effective inline feedback during the Artifact review process.

    19 min
  2. 2025 AI Agents Course with Google: Day 5 - Prototype to Production

    May 25

    2025 AI Agents Course with Google: Day 5 - Prototype to Production

    கூகிளுடன் 2025 AI ஏஜென்ட்கள் பயிற்சி வகுப்பு: நாள் 5 - முன்மாதிரியிலிருந்து உற்பத்திக்கு This episode of Exploring Modern AI in Tamil podcast provides a step-by-step guide for moving an agent from a notebook to production. - Includes cost management tips - Details the CI/CD pipeline steps - Explains how to integrate long-term memory using Memory Bank. - Outlines key quality checks needed before final deployment. - Focuses on operational best practices for monitoring and cleaning up production agent resources. - Explains how to use Memory Bank to preserve user preferences across different sessions. - Suggests methods for scaling from one to many concurrent user instances. - Outlines strategies for managing multi-region deployment availability. - Discusses A2A protocol patterns for cross-framework and cross-organization agent communication. - Defines roles and process workflows for cross-functional AI development teams. - Defines evaluation metrics to serve as a formal quality gate before deployment. - Contrasts the performance of local sub-agents against remote agents using A2A. - Highlights techniques for using scaling policies to manage traffic spikes effectively. - Describes how to implement robust health checks for identifying failing agent instances. - Explains how to choose between containerized, serverless, or Kubernetes deployment platforms. - Outlines communication processes for teams working on different parts of an agent pipeline. - Suggests documentation standards to ensure consistency across collaborative AI development workflows.

    22 min
  3. 2025 AI Agents Course with Google: Day 4 - Agent Quality

    May 25

    2025 AI Agents Course with Google: Day 4 - Agent Quality

    கூகிளுடன் 2025 AI ஏஜென்ட்கள் பயிற்சி வகுப்பு: நாள் 4 - ஏஜென்ட் தரம் This episode of Exploring Modern AI in Tamil podcast focuses on the three core messages regarding trajectory, observability, and evaluation loops. - Explains these concepts simply for someone new to agent systems. - Provides real world examples of the kitchen analogy for better understanding. - Adds tips for starting the quality flywheel process. - Explains how this framework builds enterprise trust in autonomous agents. - Connects agent quality improvements to measurable business outcomes. - Outlines a phased approach for teams starting their first agent evaluation project. - Compares logging, tracing, and metrics for diagnostic clarity. - Discusses methods to ensure agent safety and prevent failure modes. - Describes how human feedback loops specifically improve long term agent reliability. - Roleplays as an experienced engineering manager coaching a junior team on agent quality. - Lists common agent failure modes and how to detect them early. - Explains how teams should plan for scaling agent quality over time. - Highlights how to integrate responsible artificial intelligence into the agent development lifecycle. - Contrasts the black box end to end view with glass box trajectory analysis. - Explains how to implement the Outside-In evaluation hierarchy - Discusses future trends in agent reliability. - Predicts how autonomous systems will evolve. - Advises executives on prioritizing quality as a core architectural investment. - Analyzes the benefits of using AI as a judge for automated evaluation.

    21 min
  4. 2025 AI Agents Course with Google: Day 3 - Context Engineering: Sessions & Memory

    May 25

    2025 AI Agents Course with Google: Day 3 - Context Engineering: Sessions & Memory

    கூகிளுடன் 2025 AI ஏஜென்ட்கள் பாடநெறி: நாள் 3 - சூழல் பொறியியல்: அமர்வுகள் மற்றும் நினைவகம்

    20 min
  5. 2025 AI Agents Course with Google: Day 2 - Agent Tools & Interoperability with Model Context Protocol (MCP)

    May 25

    2025 AI Agents Course with Google: Day 2 - Agent Tools & Interoperability with Model Context Protocol (MCP)

    கூகிளுடன் 2025 AI ஏஜென்ட்கள் பாடநெறி: நாள் 2 - ஏஜென்ட் கருவிகள் மற்றும் மாடல் கான்டெக்ஸ்ட் புரோட்டோகால் (MCP) உடனான இடைசெயல்தன்மை

    23 min
  6. 2025 AI Agents Course with Google Day 1 - Introduction to Agents

    May 25

    2025 AI Agents Course with Google Day 1 - Introduction to Agents

    கூகிளுடன் 2025 AI ஏஜென்ட்கள் பாடநெறி நாள் 1 - ஏஜென்ட்களுக்கான அறிமுகம்

    17 min
  7. Google Gemini Enterprise Agent Platform: The Enterprise Agentic Lifecycle

    May 19

    Google Gemini Enterprise Agent Platform: The Enterprise Agentic Lifecycle

    கூகிள் ஜெமினி எண்டர்பிரைஸ் ஏஜென்ட் பிளாட்ஃபார்ம்: நிறுவன ஏஜென்ட் செயல்முறைச் சுழற்சி Explains the core architecture and key benefits of the Agent Platform for developers. - Describes how to use Agent Studio for rapid prototyping. - Outlines steps for optimizing agent performance with ADK. - Explains how to use Sessions for conversation history and memory. - Describes how Memory Bank persists personalized user information across multiple interaction sessions. - Details how Agent Registry centralizes governance for agents and tools. - Highlights how to use Agent Studio features like slash commands and comparison views. - Distinguishs between Administrator and Builder roles within the Agent Studio collaborative workspace. - Explains core session concepts like events, state, and memory for interaction persistence. - Defines how event schemas and state management enable custom agent data handling. - Explains the Quality Flywheel concept for continuous evaluation and optimization of agent performance. - Explains how to implement custom optimization strategies using the ADK framework. - Explains how developers use the interactive canvas and minimap in Agent Studio. - Details how Agent Registry helps manage and discover Model Context Protocol servers. - Outlines how to register custom endpoints and MCP servers within the central registry. - Describes the step-by-step process of the Quality Flywheel for fixing agent failures. - Explains how the Agent Registry resolves issues like fragmented tool access and isolation.

    16 min
  8. Google Agent Development Kit (ADK): Collaborative AI Agent Architecture

    May 19

    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

    20 min
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About

This show explores practical, real-world applications of modern AI tools in Tamil for better understanding. Gen AI (Generative AI ) is AI that can create original content such as text, images, video, audio or software code in response to a user’s prompt or request. Agentic AI - Autonomous systems that make decisions and execute tasks independently to achieve goals. Agentic AI acts as a partner rather than just a tool, transforming industries through intelligent planning and multi-agent collaboration. Audio is AI generated by Google's NotebookLM. Images by Google's Gemini.

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