Agentic Workflows

Agentic workflows get defined the same way everywhere: an autonomous agent plans a task, calls tools, checks its own work, and adapts, instead of following a fixed script. That definition quietly assumes the hard part is already solved. The tools the agent calls have to exist first, and for anyone generating images, video, or audio, the tool that never exists is a production pipeline the agent can actually run.

Wireflow is where that pipeline lives. You wire it once on a node canvas: a brief input, an image model, a video model, an iterator for variants, and a publish step. It runs on hosted compute in the browser, no GPU and no install, and the moment you publish it, the same graph becomes a REST endpoint and an MCP tool. That is AI pipeline automation built to be handed to an agent, not just run by a person.

An agentic workflow is a division of labor, not a hand-over of judgment.

• You design. You choose the models, lock the inputs, and decide what a run is allowed to change. That happens once, on the canvas.
• The pipeline produces. Brief in, the graph renders images, clips, and variants on hosted compute, the same way every run.
• The agent operates. Over the hosted MCP server the agent lists your published workflows, fills the typed inputs from the task, runs one, and returns asset URLs for review.

Delegation only works because runs are reproducible: workflows are versioned server-side, so the difference between two runs is the inputs, never the pipeline. That property is what separates AI creative workflows you can hand to an agent from prompt sessions you cannot, and it is why the same graph serves a headless AI workflow platform and a person on the canvas equally well.

Wireflow is the generation and publishing layer, not the reasoning brain. Planning, decisions, and strategy come from the agent you bring, Claude, GPT, or your own; an LLM node can rewrite prompts and captions inside a graph, but it will not decide what to make or why. Bring the agent; Wireflow gives it something reliable to run.

It is a media platform, not general-purpose automation: it generates images, video, and audio, it does not run databases, CRM writes, or arbitrary SaaS actions beyond its integration and research nodes. There are no offline or local runs and no custom Python nodes. And every generation costs credits, so an agent looping over hundreds of rows is a spend decision to cap deliberately. If your work is one-off assets with no repeatable shape, a pipeline has nothing to automate. If the same shape ships over and over with new inputs, this is exactly the practice for it.