End to End Production Legal AI Agents (MCP, Google ADK, Docling)

Описание: Most developers build "happy path" agents. Watch me build a 5-step production-ready legal AI agent system using Google ADK, MCP standardization, and Docling and Gemini Pro 3 as LLM.

Perfect for developers, AI engineers, MLOps engineers, and AI practitioners building real-world AI Agents.

𝗙𝗿𝗲𝗲 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴: https://www.maryammiradi.com/go/free-...
𝟱-𝗶𝗻-𝟭 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 (𝟱𝟲% 𝗢𝗙𝗙): https://maryammiradi.com/go/yt-desc

I Build in This Video:
Understands Business Constraints (PydanticAI)
Maps Data Reliability (Uncertainty Profiling)
Parses Complex Legal Docs (Benchmarking Docling vs. LlamaIndex vs. PyPDF)
Engineers the Flow (Google ADK Router & Auditor Agents)
Standardizes for Reusability (MCP Server)

👨‍💻 Tech Stack Used:
Google Agent Development Kit (ADK)
Google Antigravity
Google Gemini Pro
Docling (IBM)
Model Context Protocol (MCP)
PydanticAI
Python

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💡 Key Concepts Covered:
✅ Fail-safe defaults that prevent naive agent behavior
✅ Input validation for regulatory compliance
✅ OCR confidence checking and document quality gates
✅ Human-in-the-loop escalation mechanisms
✅ Data surface mapping for uncertainty profiling
✅ Multi-agent routing with regeneration logic
✅ MCP server architecture for cross-project reuse

⏱️ Chapters & Timestamps:
00:00 - The problem with "Naive" Agent Building
00:30 - The "Happy Path" Setup (And why it fails) - LLM
02:40 - Step 1: Business Understanding (Pydantic State & Fail-Safes)
05:00 - Step 2: Data Understanding (Surface Mapping & Uncertainty)
07:50 - Step 3: Data Preparation (Benchmarking Docling vs. LlamaIndex)
10:29 - Step 4: Flow Engineering with Google ADK (Auditor & Router Agents)
13:17 - Running the Multi-Agent System
14:20 - Step 5: Reusability with MCP (Model Context Protocol)
15:15 - Building the MCP Server & Tools
16:30 - Combining Frameworks (LangGraph, CrewAI, Google ADK)

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