Agent-Driven Vision AI Workflows Using Synthetic Data

Описание: In this demo, we show how developers can build, validate, and deploy vision AI pipelines using an end-to-end workflow, starting with synthetic data and finishing with a confirmed, running system.

Using Plainsight’s Computer Vision Platform, we walk through how builders can generate synthetic video data, create tests and assertions, validate pipeline behavior, and deploy with confidence before any real-world data or customer input is required. This approach dramatically shortens feedback loops and eliminates many of the common blockers in vision AI development.

A key part of this workflow is fast iteration with clear system state. Developers can push changes, rerun pipelines, and always know exactly where things stand, without manual cleanup, log diving, or fragile infrastructure.

We also include a demo of how this workflow can be driven through an MCP server integration. This demo shows how agents can interact with the platform from the IDE, calling structured tools to manage data, pipelines, tests, and deployment lifecycle, reducing context switching and operational overhead.

What you’ll see in this video:
Synthetic video data generation for vision AI
Automated tests and assertions for pipeline validation
A clear, end-to-end pipeline lifecycle
Faster iteration and earlier feedback before production
An MCP-powered demo enabling agentic workflows from the IDE

The result is a developer-first workflow that lets teams move faster, catch issues earlier, and ship vision AI applications with far more confidence.

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