Pipi Engines to build, deploy and manage in the cloud

Mike's Notes

Finally got onto this job. Should be fun. 😊

Resources

• https://www.blog.ajabbi.com/p/pipi.html
• https://www.blog.ajabbi.com/2025/10/industry-workspace.html
• https://www.blog.ajabbi.com/2025/10/using-free-tiers-on-cloud.html
• https://www.blog.ajabbi.com/2024/12/comparison-of-open-source-configuration.html
• https://www.blog.ajabbi.com/2025/11/plugins-revisited.html
• https://wiki.ajabbi.com/eng/9/engines.html
• https://www.blog.ajabbi.com/2025/11/using-github-actions-to-cli-jfrog-aws.html
• https://www.adventuresincre.com/data-center-development-model/

References

• Reference

Repository

• Home > Ajabbi Research > Library >
• Home > Handbook > 

Last Updated

11/12/2025

Pipi Engines to build, deploy and manage in the cloud

By: Mike Peters

On a Sandy Beach: 30/11/2025

Mike is the inventor and architect of Pipi and the founder of Ajabbi.

The problem

The open-source workspaces under development are designed to be shared on GitHub/GitLab and hosted in production on various Cloud Platforms. Eventually, private cloud and on-premises will be included, provided Pipi 9 can obtain secure access. In that case, hybrid clouds should also be fine.

Pipi 9 needs to be able to automatically build, deploy, and manage this process, either directly or via third-party tools such as GitHub Actions.

Agent Engines

An early Pipi 6 module from 2016 that catalogued cloud services was converted to a Pipi 7 microservice in 2018. Yesterday, this was imported into Pipi 9 and is being used to create these agents, which act as autonomous engines.

• Platform Engine (plt) - this one is the commander on the battlefield. I will get this finished first.

A dedicated agent engine has been created for each cloud platform. They have yet to be differentiated.

• Apple Engine (ale)
• AWS Engine (aws)
• AZURE Engine (azu)
• Digital Ocean Engine (dgo)
• Google Cloud Engine (ggc)
• IBM Engine (ibm)
• Meta Engine (met)
• Oracle Engine (ora)
• (More will be added later; all are welcome)

Then I remembered: Pipi 9 also has some self-deployment capacity, so generalising the existing capacity for building, deploying, and managing to share with the other engines makes sense.

• Pipi Engine (pip) - for deploying to the closed data centre in a Boxlang/JRE host environment.

How

Most agents start like a Stem Cell. They are then modified to perform a specific job and can evolve over time. That's what I'm doing now.

I started last night in the GCP console, looking into how to reverse-engineer the APIs and build a data model to drive the API calls. Looks straightforward. The Gemini 3 chat is a big help and saves significant time.

But wait, there's more.

Each agent engine is complex and can incorporate other agents like LEGO bricks. Example: API Engine (api) and YAML Engine (yml). Just as in a living biological cell, everything is structured, in flux and self-regulating in response to its environment and internal processes. Other agent types, like primitives, are not complex.

Free-tier experiments

The Engines will initially play in the free tier of the different cloud providers. The first experiments could use GitHub Actions, leveraging the sample code identified and building on it.

Known available free tiers (more to come)

• Alibaba Cloud
• AWS
• Azure
• Cloudflare
• Container Hosting Service
• Couchbase
• DigitalOcean
• Google Cloud
• Hetzner Cloud
• IBM Cloud
• Linode
• Netlify
• OpenShift
• Oracle Cloud
• OVHcloud
• Render
• Salesforce
• Tencent Cloud
• Vercel
• Wasabi
• Zeabur

Cost $$$$$$$ 😎😎

The free-tier usage limits need to be locked to prevent Pipi from burning through lots of cash.

Cloud credits

If Ajabbi can obtain cloud credits, using the more expensive resources would be possible to ensure everything works for future customers. It would enable customers to choose their preferred cloud provider without barriers.

No Series B

Ajabbi is a bootstrap start-up for public good (with a future foundation) and will have no investors, so there will be no Series B. Unfortunately, these cloud providers are obsessed with giving more credits only to Series B start-ups. Go figure.

Stock numbers

Use more agent engines as the workload increases. So if, for example, IBM Engine (ibm) can handle 1,000 enterprise customers, and 10.000 enterprise customers want IBM cloud setups, then Platform Engine (plt) can get the Factory Engine (fac) to breed more IBM Engine (ibm) to nibble on the work. I won't know the actual stocking ratio until field testing under load. However, it won't be a problem. And some huge customers need a dedicated agent engine or two each. All of this is possible.

Developer Accounts

The Workspaces for Developers, currently under development, will enable developers to help configure these agent engines and keep them up to date. This will also allow any platform to add itself by submitting a request for a dedicated agent engine and its developers, who will help with configuration and user documentation.