2 Solution

Praxis connects building, learning, publishing, running, and operating agents so businesses can provide ongoing services within their own operations.

Businesses describe responsibilities and rules through conversation; the platform manages the execution environment and resources. Personal Agents are employees that help build and operate services, while Service Agents are made available to customers; several specialists can collaborate in Praxis App.

The example below follows a shop’s support agent, Xiao An, through the complete process we intend to build. See Product & traction for delivered capabilities; the whole example is not a list of features already launched.

From building to operating

An agent’s lifecycle is the process from its creation to ongoing service. The five steps are build, grow, release, run, and operate; feedback from operations returns to learning and produces a new version.

In the diagram, an “individual” is an agent with its own responsibilities, rules, and experience. A “public agent service” is a published Service Agent offered to customers, not public access to customer data. Private experience can keep changing, while the customer-facing service changes through version releases.

Each difficulty in the problem chapter has a place: platform execution saves assembly time, learning preserves experience, and resource management addresses costs and supplier quality.

One shop’s support agent

Build. The owner tells Xiao An to answer from the shop’s product information and follow its after-sales rules. The platform saves those responsibilities and behavior requirements, called “character” in the existing agent system.

Grow. Xiao An first assists the owner, records corrections, and turns those events into experience, principles, and procedures: what happened, how to make decisions, and which steps to follow. This improvement during work is called online learning; colleagues use Xiao An with its accumulated experience rather than a blank template.

Release. The owner saves a tested version of Xiao An, evaluates it, and then connects it to the shop for customers. Separating learning from customer-facing versions makes the service testable and allows rollback; the version and rules are fixed, not every generated answer.

Run. Customers can ask questions while the owner is resting. The platform keeps Xiao An working, separates customers’ data, runs tools in a controlled environment, and records each step.

Operate. The owner can inspect usage by customer and task and trace execution when a complaint arrives. Improvements are made and evaluated privately before a new release; existing tasks finish on their old version, and new tasks use the new one.

What ongoing operation requires

A runtime is the environment that executes an agent’s tasks and stores its state. It has more to do than make one model call.

Where businesses use it

Praxis App is a tool for building and operating services, with web, desktop, and mobile coverage planned. Businesses can also embed Service Agents in their own products; Personal Agents help them work rather than compete for a mass-market personal entry point.

Future channels can extend to devices with voice interfaces or screens. Wherever the service is used, the business keeps its brand and customer relationships.

How resources connect

Xiao An uses models to answer questions, sandboxes to run tools, and search to find information. The resource goal is a common address and API key, the access credential; within supported scope, customers can retain familiar OpenAI, Anthropic, Google, E2B, and Tavily interfaces and adjust connection settings.

We select models and manage suppliers for customers. Customers choose a model; the platform selects and replaces the supplier behind it, with quality monitoring expected to detect degraded service and compensate customers as promised before they complain. Actual support is defined by the public documentation, and services not yet open are not migration options already available.

Two ways to use Praxis

Hosted. Businesses build agents within Praxis, with the platform responsible for the full process from construction to operation. Our agent system is our own and runs individuals built through conversation in Praxis; it does not plan to accept third-party runtimes, so migrations bring instructions and memory instead.

Resource access. Businesses keep their agents running elsewhere and use only Praxis resource services, with the same model selection and quality commitments. Both forms of use contribute purchasing volume, so improvements in procurement costs can benefit both.

In the diagram, “agent layer” means the system managing responsibilities and experience, “observability and metering” means records and usage measurement, and “version pinning” means keeping a task on a fixed version. “Buyer of record” means selecting and managing suppliers for customers; the legend’s open runtime capabilities do not mean importing third-party runtimes, and the resource-access boundary is the one described above.

Bringing these steps into one platform requires changes in both model capability and development costs. The next chapter explains why those conditions now exist.