3 Why now

Now is the time to build Praxis because models can do useful work, businesses need to turn demonstrations into services, and AI has also lowered the cost of building the platform.

What used to make it costly

Building a cloud platform once required a large engineering team and substantial capital. An agent platform must also bring together execution, memory, sandboxes, identity, and metering, with each part requiring continuing maintenance.

That made a complete platform difficult for a startup to build from scratch. Customers could buy components but still paid the cost of assembling them into a business service.

Development has changed

We use AI as both product and production tool: people set direction and make decisions, while AI handles much of development, testing, operations, and documentation. Building the initial platform and launching the model service are our experience of using this method.

Our view is that this substantially reduces development costs, enabling small teams to build infrastructure that once needed large teams. Our estimate is an order-of-magnitude reduction, or roughly one tenth of the cost; a comparable measurement basis remains to be supplied.

Customer acquisition, sales, and competition for personal users still require substantial money and labor. We therefore turn development efficiency into services for businesses, choosing a supply-side platform rather than competing with model and operating-system companies for the personal entry point.

Business needs have changed too

Models can understand responsibilities, use tools, and carry out sequences of tasks, making conversation a viable way to build agents. Easier prototypes bring businesses to the problem of ongoing operation sooner, making operational reliability more important.

More model choices and price competition at comparable capability levels give supplier selection and management a purpose. Our view is that attention is still concentrated on individual general-purpose agents, leaving room for specialists and their collaboration.

Start with smaller businesses

AWS AgentCore and Anthropic Managed Agents show that running agents has become a platform product. We intend to help smaller businesses through the full process from building to operating, reducing the work of integrating and managing modules themselves.

We start with customers who do not need complex enterprise solutions: independent developers, creator businesses, and small teams. This follows Christensen’s low-end entry approach: lower-cost, simpler products for customers poorly served by existing offerings.

The difference between established platform prices and our purchasing and production costs gives us room to offer savings and operate a business. We believe large providers have commercial reasons to keep serving high-value customers, which gives a small team time; but once developers build deeply into one runtime, migration becomes harder.

Technology can move in a similar direction while serving different customers first. The next chapter explains who those customers are, what they pay for, and where we start.