massgen-develops-massgen

To run automated AI agent experiments for self-improvement, execute MassGen in automation mode with a configuration file to coordinate backend workflows, collect logs, and iterate on agent capabilities.

Visual evaluation in multi-agent coordination workflows is a testing process that visually assesses agent interactions and UI/UX to refine backend coordination and tool integration across multiple agents.

You collect logs and monitor performance during automated agent experimentation by using MassGen's modular monitors and observability integrations to track status, cost, and coordination data throughout the experiment lifecycle.

Yes, this workflow orchestration supports safe, repeatable multi-agent testing by coordinating automation and visual-evaluation workflows to systematically test backend coordination and agent interactions.

MassGen experimentation requires no external dependencies to orchestrate experiments, relying solely on its internal components to set up, run, monitor, and analyze self-improvement workflows.

A limitation of using automation mode for AI agent coordination is that it focuses strictly on backend execution, requiring a switch to the visual evaluation workflow to properly assess UI/UX and complex tool integration.