AI-Assisted Platform Operations

We bring AI to the infrastructure itself: Skills that read your manifests, your Terraform and your catalogs, then act on them under guardrails. And we connect agents to your enterprise the way we've connected enterprise systems for more than two decades: through open, contract-first protocols.

Agent Protocols Are Enterprise Integration, Again

We've spent our careers connecting enterprise systems: service-oriented architectures and service buses, Open Service Broker marketplaces, SAML and OAuth identity management, Streaming technologies (i.e. Kafka, RabbitMQ, SMS), and API gateways in front of millions of members. Agents need the same things. They have to discover a capability, call it through a contract, prove they're allowed to, get a person's authorization before money moves, and show the result to a human. The new agent protocols restate that problem, and the integration lessons carry over directly.

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Agent = Model + Harness + Skills

The vocabulary behind every guardrail on this site

An agent is three separable things, not one black box. The model is a function from context to next action: it reasons and proposes, and the same input can yield different output from one run to the next. The harness is deterministic code that owns everything the model cannot: state, tool dispatch, permissions, the sandbox, context budget. Skills are loaded into context and the tool set on demand, changing what the agent knows and can call without touching the model or the harness. Claude is the model, Claude Code is the harness, and our accelerators are the skills.

Soft Constraints and Hard Constraints

Soft, in context

Profiles, skills and conventions are followed with high probability, not certainty. Cheap to change, and good for style, structure and idioms.

Hard, in code

Validators, schema checks, guards and gates are followed always, or the action is refused. Costlier to build, but testable.

Every accelerator previews before acting and refuses the wrong target, reports missing dependancies and directs the user on how to resolve issues.

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Claude Code Accelerators

Skills that operate the deployment harness

A Claude Code skill tells Claude when to act and exactly how: which command, in which order, and what each refusal means. Ours adopt applications from legacy pipelines, render them for a new stack, deploy them, and reconcile catalogs against reality. They follow the verification and security practices set out in the community's Agentic Coding guide, so every result is previewed, checked and reviewable. See each skill and the practices it builds in →

In short

  • Adopt: derives an application's manifest from its existing deployment, catching storage that won't survive containers and dependencies nobody uses
  • Provision: Acquires appropriate application runtime form a manifest, the same manifest expresses intent across cloudes such as AWS Fargate, Kubernetes, or Cloud Foundry and checks it against the stack contract
  • Deploy: runs infrastructure, secrets and release in order, refusing the wrong account before Terraform touches state
  • Reconcile: finds drift between what's declared and what's deployed, reconciling Infrastructure as Code (IaC) with the actual state

Why Work With Us on AI

The difference is integration experience and guardrails, not a bigger model.

  • Integration heritage: agents meet the same problems as enterprise systems: contracts, registries, identity and authorization. We've solved those for decades
  • Domain knowledge: skills reason over your actual infrastructure, not generic templates
  • Guardrails: previews by default, target checks before any change, and refusals that name the fix
  • Offline-capable: our tools work on an IaC (Infrastructure as Code) basis on source code, not live services, so an outage can't block deploying a fix
  • Incremental adoption: start with one application, then add deployments and drift checks. No big-bang rewrite

Ready to see what we can do together?

Let's discuss how we can help accelerate your initiatives