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Developers & Practitioners

What 10 autonomous film crews taught us about agent teamwork

July 16, 2026

Preston Holmes<br>Product Manager

Hussain Chinoy<br>Technical Solutions Manager

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Can teams of AI agents collaborate to create a short film?

As part of an internal Google generative media hackathon, we put this question to the test – specifically, to uncover whether AI agents could work collaboratively in a domain less innately familiar than software development. We gave each crew three agents with distinct roles and had them collaborate through messages and shared files under their own agent-only hackathon. Agents ran inside Scion, an open source agent orchestration testbed. Unlike code or text, media and composition are less familiar subject matter for AI agents, so this experiment taught us about how agents can collaborate with checks and gates to see projects through to an end.

Ten crews each produced a short film. A separate agent-staffed documentary crew "filmed" the process. That documentary itself became the medaling hackathon submission.

The result? Hundreds of individual agent instances were created over the project. 25+ total productions across pilot rounds and competition. About 44 minutes of delivered film. Human feedback on the output fed back into a continuous improvement loops with the agent generated tooling.

Here are two examples of agent generated short films:

The paper frontier

The printmaker's ghost

Team structure

Each crew had three agents. The Idea Person wrote the script and defined the visual style. The Technical Lead operated the generative media tools. The Editor controlled pacing and final assembly. A team-coach agent supervised gated checkpoints but didn't write or direct.

The Idea Person generated three starter ideas. Then, the team assessed the ideas from their role's POV: would this be generated well with generative media? Would it be complex to edit? Then, they pitched the idea among other teams in the hackathon, so that a team could adjust or pivot. For example, if three teams all picked a sci-fi space battle, then it would not make a good competitive entry.

A Coordinator agent scheduled the competition, running two teams at a time across five waves. The event ran about 21 hours.

The crews followed a seven-step pipeline modeled on the fundamentals of traditional filmmaking: concept, beat sheet, character workshop, storyboard, principal photography, assembly, final render. Each step had a verification gate, ensuring that at least one agent checked another agent's work for technical compliance (such as resolution, or timing).

In an early pilot, one team reported a completed film that turned out to be a 94-byte placeholder file. As it turns out, agents can be convincing about having finished work they haven't done.

While surprising (and sometimes even amusing), we uncovered other ways the agents took the film in their own direction. For example, the  agents divided labor on their own in ways we didn't expect. On one team, the Idea Person wrote a line of prose in the first draft. The Editor, independently, built an eight-second silence gap around that line and marked it "NON-NEGOTIABLE" in the timeline. The Tech Lead regenerated a single shot repeatedly until a flower separated from a bouquet at the right frame. None of them coordinated this. They read the shared files and made independent editorial judgments.

This process around teamwork and tool use was co-developed with agents during the pilot-phase. During this phase, agent teams created videos which received human feedback, such as audio collisions and levels, inconsistent characters, hard to follow story or narration.

This feedback, combined with agent-authored retrospectives for each pilot was used to restructure not only the playbook and guides that instructed future teams through the process, but the agents also built and revised a custom media toolchain that combined golang CLIs with python batch automation.

The generative media models

Each film combined multiple Google AI models. The agents called them through a shared CLI toolkit called genmedia:

Gemini image generation (Nano Banana) produced character reference sheets, storyboard frames, and scene compositions. The agents kept characters visually consistent across a film through reference chaining: they generated headshots first, then used those as input for body sheets, then used body sheets as input for scene tests. Each generation call included these accumulated references as anchors.

Veo 3.1 generated the video. Clips run four to eight seconds at 720p. The agents chose different generation modes depending on the shot: text-to-video for simple compositions, image-to-video for shots anchored to storyboard frames, frame interpolation when they needed a precise start...