Import and export AI workers and AI workflows: reuse what works
When a team builds an AI worker or AI workflow that performs well, the next question is simple. How do you use it again somewhere else without rebuilding everything? This month we introduced import and export for AI workers and AI workflows so teams can move proven setups across environments, projects and teams with far less effort. The goal is straightforward. Once a useful AI solution exists, you should be able to deploy it again quickly.
Before this capability, teams often had to recreate their AI flows step by step when they wanted to replicate a successful setup. That meant reconnecting integrations, reloading knowledge sources and rebuilding the logic that connects workers together. Import and export changes that process. You can now package your workers and workflows and move them wherever they are needed without repeating all of that configuration work.
This helps teams move faster and it helps organizations maintain consistency as more AI systems are deployed internally. When a useful worker or workflow is built, it becomes a repeatable asset instead of a one-off project.
What actually moves when you export your AI solutions?
When you export an AI worker or workflow, you are exporting the configuration that allows it to operate the way it does. That includes worker logic, associated memories, collections and any nested workers or flows that exist inside the broader process. In practice, the structure and intelligence of your AI solution travel together as a bundle that can be imported elsewhere.
For example, a team might build an AI workflow that reviews incoming documents, calls a specialized worker to analyze them and then sends structured output into another system. If another team needs the same capability, they no longer have to recreate the entire flow from scratch. They can import the bundle and adapt it to their own process.
This approach turns successful workers and workflows into reusable building blocks. Teams can create, refine and reuse them across different areas of the business without having to rebuild the underlying logic every time.
How does this help teams roll out AI faster?
As more AI workers and workflows are deployed across an organization, speed and consistency begin to matter more. Teams want the ability to move a working configuration from a development environment into production. They may also want to replicate the same AI flow across multiple departments that follow similar processes.
Importing a worker or workflow bundle helps make that possible. Instead of spending time recreating configuration details, teams can start with something that already works and adjust it where necessary. This shortens the time between an idea and a working AI solution that others can use.
It also helps organizations standardize how certain types of work are handled. When one team builds a strong AI flow, other teams can benefit from it quickly. Over time this creates a library of workers and workflows that reflect real operational knowledge inside the company.
Secure deployments without sharing sensitive credentials
Portability is important, but security remains essential. For that reason, exports never include sensitive credentials such as secrets or API keys. These details stay within the environment where they belong.
During the import process, the platform guides users through reconnecting any required integrations. This ensures that each deployment is properly configured for its destination environment while still maintaining strong security practices. Teams can move their AI flows between environments without exposing sensitive data.
The result is a system that allows flexibility without introducing unnecessary risk. Your AI solutions can travel across environments while security controls remain intact.
A practical step toward scaling your AI workforce
Many organizations are moving from isolated experiments with AI toward broader adoption inside everyday business processes. As that shift happens, the ability to reuse and adapt working solutions becomes more important. Import and export helps support that transition by making successful workers and workflows portable.
Instead of rebuilding solutions repeatedly, teams can focus their time on improving and expanding what already works. Workers can evolve through iteration, then be shared and deployed wherever they create value. Over time this approach helps organizations build a more capable AI workforce that reflects the way their teams actually operate.
See it in action
If you are already using the platform, you can explore this capability today. You will find export functionality within the workflow builder and across the platform, where you can package your workers and AI flows and move them where they are needed.
If you are not yet an active user, we would be glad to walk through how an AI workforce could support your team. Schedule time with us to explore your processes, see how workers and workflows are created and understand how capabilities like import and export help scale what works across your organization.
Ameya Deshmukh