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Managed Deep Agents explained in 20 minutes thumbnail

· 18:17

Managed Deep Agents explained in 20 minutes

Managed Deep Agents provides an integrated framework to simplify the entire lifecycle of building, running, and deploying AI agents into production. It addresses the complexity of agent infrastructure by bundling the open-source Deep Agents harness with necessary components like durable execution, context management (via Context Hub), sandboxes, and scheduling capabilities, allowing developers to move from local business logic directly to a scalable, managed deployment on LangSmith.

Key takeaways

  1. Agent Architecture Components

    An agent requires three layers: 1) Business Logic (provided by the user, e.g., prompts/tools); 2) Harness (orchestrates context and passes data to the model); and 3) Infrastructure (runtime, sandboxes, etc.). Managed Deep Agents bundles these into a seamless package.

  2. Production Readiness 2:05

    The framework handles complex production requirements such as durable execution, fault tolerance, streaming, queueing, run cancellation, and rollbacks, which are necessary when moving agents from local development to cloud serving.

  3. Decoupled Context Management 5:05

    Context (instructions and skills) is stored in the dedicated Context Hub. This allows non-developers to edit and maintain agent context via a UI without requiring code changes or redeployments, significantly improving collaboration.

  4. Deployment Workflow 10:30

    The process involves initializing the project using `MDA innit research assistant`, defining components (e.g., tools in `tools/search.py`), and deploying via `MDA deploy`. This pushes context to Context Hub and creates a deployment on LangSmith.

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Build a Full-Stack GenAI Project in 4 Hours (FastAPI, React, Supabase) thumbnail

· 3:51:50

Build a Full-Stack GenAI Project in 4 Hours (FastAPI, React, Supabase)

This tutorial provides an end-to-end guide for building a production-grade Retrieval Augmented Generation (RAG) application—a Document Copilot. The project uses SEC filings data and demonstrates the complete AI engineering workflow: from initial client brief analysis to setting up the full stack (FastAPI, React/TypeScript, Supabase Postgres with pgvector). Key phases covered include database schema design using SQLAlchemy/Alembic, implementing user authentication via Supabase Auth, building a front-end chat interface, and establishing a robust document ingestion pipeline that converts messy HTM files into structured Markdown chunks for vector embedding.

Key takeaways

  1. Full Stack GenAI Architecture 30:45

    The system is designed as a mono repo using FastAPI (backend) and React/TypeScript (frontend), connected via Supabase Postgres, which utilizes the pgvector extension for efficient vector storage and retrieval.

  2. Data Ingestion Pipeline 3:44:09

    Raw SEC filings (HTM format) are processed using Dockling to convert them into clean Markdown. This structured data is then chunked, embedded via OpenAI, and stored in the database for RAG retrieval.

  3. Database Management

    The project utilizes SQLAlchemy and Alembic for defining models (Users, Documents, Chunks, Messages) and managing schema migrations, ensuring a structured development process.

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