Guide
Updated April 20, 2026
How to use Veo 3.1 in an AI agent
Veo 3.1 is Google DeepMind's latest video generation model. It produces high-fidelity, physics-consistent video clips from text prompts or image references, with strong temporal coherence across scenes. Adding it directly to an agent means integrating a Google Cloud credential path, managing async job polling, and parsing a separate response schema. AnyCap solves this by exposing Veo 3.1 through the same capability runtime an agent already uses for image generation, one command, one auth path, one response format.
Three things that matter when adding video generation to an agent
Async job handling
Video generation takes 30–120 seconds. The agent needs a job ID, a stable polling surface, and clear completion semantics. A missing polling loop causes the agent to either block or lose the result.
Consistent response schema
The agent doesn't watch the video. It parses the response. An API that returns the video URL in a predictable field survives prompt drift. One that changes response shape across API versions breaks the loop.
Single auth surface
Every additional provider credential is another secret to rotate, another error vocabulary, and another rate-limit surface. Routing Veo 3.1 through AnyCap means the agent authenticates once and routes multiple video models without separate SDK integrations.
Why Veo 3.1 matters for agent video workflows
Veo 3.1 is currently the strongest model for cinematic quality, temporal coherence, and text-to-video prompting with physical plausibility. In agent workflows, automated content pipelines, product demo generation, code-driven video creation, these properties translate into more reliable outputs with fewer retry loops. An agent that generates a 5-second product clip from a structured prompt needs the output to land well consistently, not occasionally.
The integration challenge is the real constraint. Veo 3.1 runs through Google's Vertex AI infrastructure, which requires separate credential management, a different job-polling pattern, and a different response envelope than the image generation APIs most agents already use. AnyCap normalizes all of this: the agent calls anycap video generate with a model flag, and the runtime handles credential resolution, job submission, polling, and the final URL return. The workflow pattern for Veo 3.1 is identical to Kling or Seedance, the agent doesn't need to know which provider is running.
Decision pattern for video in an agent
Need text only? → stay in the prompt
Need a new video clip? → anycap video generate
Specify the model? → anycap video generate --model veo-3-1
Need to analyze a video? → anycap video read
How to add Veo 3.1 to your agent
Install or verify AnyCap
If you don't have AnyCap installed, install it with the one-line install script. If you have it, verify it's up to date.
curl -fsSL https://anycap.ai/install.sh | sh && anycap login && anycap status
Add AnyCap as a skill to your agent
For Claude Code, Cursor, or Codex, add the AnyCap skill so the agent can discover and call video generation capabilities from its context.
npx -y skills add anycap-ai/anycap -a claude-code -y
Generate a video with Veo 3.1
Run a video generation job targeting Veo 3.1. The runtime submits the job to Google DeepMind infrastructure, polls until complete, and returns the video URL.
anycap video generate --model veo-3-1 --prompt "a timelapse of a cityscape at dusk"
Use the result in the agent loop
The response is JSON with a predictable video URL field. The agent can store it, forward it, or chain it with the next task, no provider-specific parsing needed.