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Building the platformbehind the agents.

OpenServ needed an AI agent platform and the team to build it. We brought technical leadership, architecture, and engineering, connecting reasoning research with a product customers could use.

Client
OpenServ
Sector
AI infrastructure
Our role
Fractional CTO, AI architecture, and platform engineering
Today
An agent API and marketplace used by customers

A new company without a platform or an engineering team.

OpenServ started with the need to support agentic AI use cases. Building the company meant establishing both the technology and the people who would develop it.

The product also had to address questions that matter once agents perform real work: how they collaborate, how their execution can be reviewed, and how different models fit into the system.

A working platform with a team behind it.

OpenServ developed an agent API and marketplace used by customers, supported by an engineering team and a research-based approach to auditable reasoning.

The engagement brought together the platform, the organization building it, and the methods used to examine AI execution.

Research behind the work: BRAID, Bounded Reasoning for Autonomous Inference and Decisions, by Armağan Amcalar and Eyup Cinar. Research conducted at OpenServ Labs; published on arXiv in December 2025.

How it went

Build the team and the technology together.

  1. Leadership

    Establish engineering leadership

    We provided fractional CTO leadership and helped build the engineering team from scratch. Architecture and implementation were part of the same engagement, keeping technical direction connected to delivery.

  2. Platform

    Develop the agent platform

    The work included an API for distributed agent collaboration, an agent platform, and a marketplace. These capabilities gave customers a way to use and coordinate agents through a shared infrastructure.

  3. Research

    Bring research into the product

    OpenServ’s reasoning work draws on BRAID: Bounded Reasoning for Autonomous Inference and Decisions. The research uses structured instruction graphs to guide reasoning, and its product application creates a traceable record of execution steps.

  4. Models

    Support different models

    The platform works with upstream models from OpenAI, Anthropic, and Google, as well as local models. The engineering work examines which models fit a workflow and the quality and cost tradeoffs involved.

The result

A working platform with a team behind it.

An agent API and marketplace used by customers.
Live
An engineering team behind the platform, and a research-based approach to auditable reasoning.
Own team

More work

More work.

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