API & AI Gateway Management

Every agent you ship reads and writes through an API. So if your API layer has no governance, neither do your agents. Cravings designs, secures, and runs the API layer for the AI era: an API program on the gateway you already own, an AI gateway in front of every model call, MCP servers that let agents use your private APIs safely, and a developer portal your engineers can get answers from.

Most API programs were designed for human developers building apps, with a few partners, predictable traffic, and one key per integration. Agents don’t use APIs that way. They call more often and less predictably, they chain calls across systems, and they sometimes act on instructions that came from somewhere they shouldn’t have. The API layer is where you enforce who can do what, how often, at what cost, and with what record. We build it on that basis.

What we build

• Managed AI gateways. One controlled path between your applications and every model provider. It filters prompt injection and jailbreak attempts, redacts PII before prompts leave your perimeter, enforces per-team and per-agent quotas and cost caps, routes to fallback models, and logs every model call to your SIEM. You get one place to change policy instead of forty SDK configurations.
• API programs on your gateway. Stand-up, migration, or rescue on Apigee X, Azure API Management, Kong, or AWS API Gateway. This covers proxy design, OAuth and mTLS, rate-limit and spike-arrest policies, versioning and deprecation, and CI/CD, so changes go out as code rather than as console clicks.
• MCP servers over your private APIs. Model Context Protocol servers that let Claude, ChatGPT, Cursor, and your internal agents use your systems through scoped, authenticated tools. That means OAuth per user, a distinct identity per agent, rate limits per tool, and read and write actions kept apart, so a reporting agent can’t issue refunds.
• AI-enabled developer portals. Portals generated from your OpenAPI specs, with plain-English summaries of every endpoint, search that ranks by relevance, and an assistant grounded only in your documentation. It writes working client code, explains error responses, and says “I don’t know” rather than inventing an endpoint.
• Security, monitoring, and audit. API inventory (including the shadow APIs nobody registered), OWASP API Top 10 review, anomaly alerting, and audit logs that show which agent called which endpoint on whose behalf. These are the answers your security team and your auditor will ask for.

Three ways to start

API management foundations. A working session on governance, lifecycle, and rollout for teams that are standing up an API program or inheriting one that grew without a plan. You leave with a target operating model and a gateway policy baseline.

API security and audit review. We inventory what is actually exposed, test it against the OWASP API Top 10, and check whether your logs could reconstruct an incident. You get a ranked remediation list, not a 90-page PDF.

API readiness for AI. Part of our AI readiness assessment, focused on the API layer. We look at which of your APIs an agent could safely use today, which need scopes, idempotency, or better errors first, and where an AI gateway or MCP server gives the fastest return.

Two ways to engage

Build it, you run it. We design and ship the gateway, portal, and MCP layer in your cloud accounts and your repos. Policies are written as code and runbooks are written for your on-call rotation, and we train your platform team to own all of it. This is the standard Cravings shape: built to hand back.

Build it, we run it. For teams that want the capability without hiring a platform squad, we operate the gateway and portal against an SLA. That includes monitoring, policy updates as new model and threat patterns appear, developer onboarding, and a monthly report on traffic, cost, and incidents. You can take ownership whenever you are ready.

Platform-neutral by design

We don’t resell a gateway. If you already run Apigee or Azure API Management, we build on it. If you’re choosing one, we’ll give you a straight comparison on your traffic, your cloud, and your team’s skills. The AI gateway and MCP layers are designed to be portable, so switching model providers is a config change, not a rewrite.

Typical engagement

A one- or two-week review or workshop sets the honest scope. From there, an AI gateway in front of your model traffic usually takes four to six weeks. A first MCP server over a production API takes three to five. A full API program with an AI-enabled developer portal takes ten to sixteen, shipped in slices behind feature flags so existing consumers never notice the cutover. It pairs naturally with agent builds and AI integration. The API layer is what lets you run twenty agents safely instead of three.