This topic covers how creators and teams use generative AI to produce images, video, designs and code while managing licensing, content protection, and end-to-end workflows. By 2025 creators must balance fast iteration with provenance, permissioning and reproducibility: license metadata and tamper-evident provenance are now central to distribution and platform compliance, and secure execution sandboxes and credentialed integrations reduce legal and security risk. Key components include secure sandboxing (Daytona; pydantic/mcp-run-python) for running AI-generated code and transformations without exposing secrets or host systems; Model Context Protocol (MCP) integrations (GitHub, Atlassian Confluence/Jira, Figma/TalkToFigma, Playwright) that let agents read and modify source files, issues, and designs; and integration platforms (Pipedream) to wire APIs and automate publishing pipelines. Image and video generators feed asset pipelines that must attach licensing and provenance metadata; document management integrations keep source contracts, release notes and attributions centralized. Credential management and A2A (agent-to-agent) controls enforce least-privilege access to repositories, design files and build systems. Practical workflows stitch these elements together: agents read Figma designs, generate variations, run unit or visual tests in sandboxes, commit to GitHub via the GitHub MCP, and create tickets in Jira/Confluence for approvals — all while embedding license tags and cryptographic provenance. Playwright MCP can automate web-based approvals or publishing, and Pipedream handles cross‑service orchestration. The result is a reproducible, auditable pipeline that reduces downstream disputes over ownership, simplifies compliance, and preserves creators’ control over how generated assets are licensed and distributed.