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How Instruction Driven Development uses layered markdown documentation as executable specifications for AI coding assistants, transforming how teams work with GitHub Copilot and Claude Code.
A practical guide to evaluating agent quality, implementing Unity Catalog governance, and scaling agentic AI workloads on Databricks from prototype to production.
How to build multi-agent pipelines on Databricks using LangGraph for orchestration, Unity Catalog for tool governance, and Delta Lake for persistent state management.
How Databricks Mosaic AI Agent Framework enables compound AI systems with Unity Catalog governance, Vector Search RAG, and MLflow-managed agent lifecycles.
How to build custom autonomous AI agents on GCP using Gemini models, LangChain/LangGraph framework, and Cloud Run — a production architecture pattern with monitoring, state management, and scaling.
Deep dive into Google’s Agent2Agent (A2A) protocol — how it enables multi-agent interoperability across platforms, its relationship to Anthropic’s MCP, and what it means for enterprise agent architecture.
A comprehensive look at building agentic AI on Google Cloud — Vertex AI Agent Builder, Gemini’s multimodal capabilities, BigQuery integration, and production deployment patterns for enterprise agents.
Microsoft’s enterprise agentic AI vision — from Copilot extensions and Copilot Studio to custom multi-agent ecosystems on Azure, with governance frameworks for managing agents at organizational scale.
How to build multi-agent AI systems using Microsoft’s AutoGen framework on Azure — covering conversation patterns, group chats, nested agents, and enterprise deployment with Azure AI Foundry.
Exploring Azure’s enterprise agentic AI stack — AI Agent Service, Semantic Kernel orchestration, Azure AI Foundry, and how Microsoft’s ecosystem provides unique advantages for building production AI agents.
NIHAR MALALI 