Cost Control
Contextual cost auditing and budgeting that keeps AI expenses in check while letting your agents run at full tilt.
Pillar 02 — Rails
The plumbing for serious AI work.
Frontier models are extraordinary. The systems they live inside are usually duct-taped together. We build the durable, well-documented infrastructure that makes agentic software act like real software.
Boring is good. Boring stays up at 3 a.m.
The Model Context Protocol, command-line tooling, integration adapters, and gateway layers are the boring, load-bearing parts of an AI stack. They are not glamorous, and they are not where most of the attention is going. They are also where most production AI systems fall over.
The next wave of useful AI software will be defined less by the model at the center and more by the quality of the rails around it. The rails are currently underbuilt. We are filling that gap.
What we work on
Eight focus areas in the rails layer.
01
Project memory.
Persistent context that survives sessions, models, and team turnover. The rails remember the why behind decisions, not just the what.
02
MCP servers and registries.
Robust, well-documented MCP servers and the discovery layer around them. Built to be operated, not just demoed.
03
CLI tools for AI workflows.
Command-line interfaces that let developers compose agentic operations the way they compose any other software.
04
Integration adapters.
Connecting agents to the rest of an organization's software without the usual brittleness. Adapters that survive deploys.
05
Observability and evaluation.
Production-grade visibility into what agents are actually doing. Evaluation that runs in CI, not just on a deck.
06
Tool registries and discovery.
The catalog layer for the growing universe of agent tools. Discoverable, versioned, and trustworthy by default.
07
Agent operations.
Observability, debugging, and replay for agentic systems. The boring tooling that makes production-grade work possible.
08
Multi-agent coordination.
The traffic control that keeps multiple agents from stepping on each other. Less collision, more flow.
Featured incubations
A glimpse at the workbench.
MCP Gateway
A secure and scalable MCP gateway with built-in observability and governance.
Agent Ops
Observability and replay for production agentic systems.
agentctl
A command-line interface for composing agent operations.
Eval Harness
Evaluation that runs in CI and surfaces regressions before they ship.
Building infrastructure?
If you are building the rails for AI — or running into the limits of someone else's — we would like to compare notes.
Contact the studio