How to Build Vision Data Agents with Tools, Skills, and MCP

Virtual

Americas

Workshops

How to Build Vision Data Agents with Tools, Skills, and MCP - June 17, 2026

Jun 17, 2026

9 AM PST

Online. Register for the Zoom!

About this event

AI agents are rapidly changing how teams build and scale machine learning workflows—but most implementations still rely on fragmented tools, manual processes, and brittle integrations.

In this session, you’ll learn how to build production-ready AI agents that can reason over your data, automate complex tasks, and integrate seamlessly into your existing stack using tools, skills, and the Model Context Protocol (MCP).

We’ll walk through how modern agentic systems move beyond simple prompts—leveraging structured tools like dataset operations, embeddings, evaluation pipelines, and model execution to take real action. You’ll see how these agents can tag data, run inference, evaluate performance, and surface insights automatically, all within a unified workflow.

By combining natural language interfaces with programmable building blocks, teams can dramatically reduce manual effort, accelerate experimentation, and unlock faster decision-making across the ML lifecycle.

Whether you're building data-centric AI systems, managing large-scale vision datasets, or exploring agentic workflows for the first time, this session will give you a practical blueprint for getting started.

Host

What you’ll learn:

How AI agents actually work in production: Move beyond hype—understand how agents use tools, memory, and structured workflows to execute real tasks
Using tools to take action on your data: Program agents to run operations like filtering labels, computing embeddings, evaluating detections, and more
What “skills” are and how they enable multi-step workflows: Learn how to package complex logic into reusable capabilities your agents can call on demand
How MCP connects your models, tools, and agents: See how MCP standardizes communication between LLMs and external systems for scalable, flexible architectures
Automating the ML lifecycle with agents: From data curation to model evaluation, discover how to eliminate repetitive workflows and accelerate iteration
Best practices for building reliable agentic systems: Design patterns, guardrails, and practical tips for deploying agents in real-world environments