For Platform Engineering
Make agents first-class citizens on your platform
Last verified: June 2026· Head of Platform engagement
Your engineers want to use agents against company infra — Datadog, Linear, databases, deploy systems. We build the safe, audited paths that let them, plus the eval and observability layers underneath.
This is highly engineer-coded work, and very few people do it well. Our open-source ormai and openclawOS are the same kind of infrastructure we deploy for your platform.
What’s on your desk
No safe way to expose internal systems
Agents are boxed into the text of the repo because reaching the database or deploy pipeline isn’t audited or access-controlled. MCP servers fix that — done right.
No golden path for AI tooling
Every team wires up agents differently. You need shared slash commands, agent definitions, and templates that make the right thing the easy thing.
No observability for agents in production
When an agent misbehaves you can’t trace why. You need tracing, prompt versioning, and regression detection as platform primitives.
Readiness is a feeling, not a number
You can’t prioritize platform investment in agent-readiness without a score. We give you one, with a roadmap.
How we’d help
The engagements that fit a Head of Platform best — each ships working software and a measurable result.
MCP Server Builds for Internal Tooling
We expose your internal systems — Datadog, Linear, databases, deploy tooling — to AI agents over MCP, so your devs can work against company infra safely.
Internal MCP servers with auth, access control, audit logs
Internal AI Coding Workflow Build-Out
We standardize how your team uses Claude Code / Cursor / Copilot — custom slash commands, agent definitions, MCP servers for your internal tools, golden-path templates, and code-review hooks.
A working internal AI dev platform your team adopts
Production AI Eval Infrastructure
Most teams shipped AI features with zero evals. We build eval harnesses, regression suites, online quality monitoring, and A/B infra for prompts and models.
An eval platform wired into your CI/CD
Agentic Codebase Readiness Audit
We map your codebase against what actually makes it AI-coding-ready — module boundaries, test coverage, type strictness, docs, CLAUDE.md / rules files, and MCP potential — and quantify how far off you are.
A scored report + prioritized remediation roadmap
The proof is open source
We deploy the same infrastructure we build in the open.
Build the agent-ready platform layer
Tell us which internal systems your engineers most want agents to reach. We’ll scope the MCP and tooling work to get there safely.
What lands on platform, specifically
Agentic development moves a set of problems onto platform teams that did not previously exist there, and they arrive without a budget line. The clearest is identity: when an agent calls an internal system, whose credentials is it using, and does your existing authorisation model have any way to express 'this agent, acting for this engineer, may read this and not write that'? Most internal systems were built assuming a human or a service account, and agents fit neither well. Answering it properly is platform work, and answering it badly is how an agent ends up with a token that does far more than anyone intended.
The second is that agents are an unusually effective way to exhaust a quota. A human misusing an API does so at human speed; an agent in a retry loop does not. Rate limits, cost guards and circuit breakers belong on the server side rather than being trusted to the caller, and the blast radius of getting this wrong is measured in an afternoon's spend rather than a support ticket. This is the argument for internal MCP servers being real infrastructure with an owner, not a weekend integration that spread.
The third is the audit trail. When something goes wrong in a system an agent touched, the questions are immediate and specific: what did it do, on whose behalf, with what authority, and would we have caught it. That is answerable only if the tool layer recorded it at the time, which means logging is a design requirement rather than an addition. Teams that skip it discover the gap during an incident, which is the worst possible moment to find out.
Underneath all three sits the platform team's usual constraint: you are being asked to support a set of tools chosen by other people, at a pace set by their enthusiasm. The way through that we see work is to make the supported path genuinely the easiest one — a small number of well-built MCP servers, clear defaults, good documentation — rather than to police the unsupported ones. Governance that is more convenient than the workaround is the only kind that holds.