MakeYourAgent
The AI agent layer for your SaaS. Plug in your OpenAPI spec and your knowledge base, and ship a production-ready agent your customers can chat with, complete with a managed MCP server, tool calling, a hosted chat UI, and an embeddable SDK. We built the whole stack and run it ourselves.
What it is
MakeYourAgent is infrastructure that lets a SaaS company add a real AI agent to its product without building the agent platform from scratch. You point it at your API through an OpenAPI spec, connect a knowledge base of your docs and content, and you get an agent that can answer questions with grounded, cited responses and take real actions in your product, not just talk about them.
It is built for two kinds of user. SaaS, docs, and support teams use it to give their customers an agent that knows their product and can act on their behalf. Creators, coaches, and experts use the same engine to train an agent on their own writing and conversations so it answers in their voice. Either way, the product ships with the parts that usually take months of plumbing: a managed MCP server, tool calling, a hosted chat UI, an embeddable SDK, and per-customer agents with strict knowledge boundaries so one customer's agent never sees another's data.
See it live at makeyouragent.aiWhy we built it
Every SaaS team wants an agent in their product. Almost none of them want to build agent infrastructure, and they are right not to. Getting from "our users could chat with an AI" to something you can safely put in front of paying customers means solving a long list of unglamorous problems: connecting the agent to your real API, deciding which tools it may call and with what permissions, grounding its answers so it does not invent facts, keeping each customer's data walled off from every other customer, and exposing all of it through an interface a front-end developer can drop in.
That is months of work that has nothing to do with the actual product a team is trying to build. We kept seeing companies either postpone their agent indefinitely or ship a thin wrapper around a chatbot that could answer questions but could not do anything. We built MakeYourAgent so that the agent layer is a component you configure rather than a platform you have to invent.
The bar we set was that a team should be able to go from an OpenAPI spec to a working, grounded, action-taking agent in an afternoon, and that the result should be safe enough to expose to real customers on day one.
How we built it
The heart of the product is the agent runtime and the MCP server that sits in front of it. When you connect an OpenAPI spec, MakeYourAgent turns your endpoints into tools the agent can call, complete with the guardrails you need to trust it against your production systems. The knowledge base is indexed for retrieval so answers are grounded in your own content and come back with citations rather than confident guesses.
Managed MCP server
We run the Model Context Protocol server so the agent has a standard, well-defined surface for discovering and invoking tools, and you do not have to operate that layer yourself.
Tool calling from your API
Your OpenAPI spec becomes a set of callable tools, so the agent can take real actions in your product instead of only answering questions about it.
Grounded, cited answers
Responses are generated against your indexed knowledge base and return citations, which keeps the agent honest and gives users a way to verify what it says.
Per-customer isolation
Each customer's agent operates inside strict knowledge boundaries, so one tenant's data and context never leak into another's answers.
Hosted chat UI and SDK
A hosted chat interface works out of the box, and an embeddable SDK, published to npm, lets your own developers drop the agent into your product with native UX.
Safety controls
Scoped API keys, rate limits and quotas, HMAC-signed webhooks, a read-only mode, and audit events give operators the controls a real production integration requires.
The hard part of an agent platform is not making a model reply. It is the trust boundary around it: letting an agent take real actions while making sure it can only take the ones you allow, against the data it is allowed to see, in a way you can audit afterwards. That boundary is why the product includes scoped keys, read-only mode, signed webhooks, and audit events as first-class features rather than afterthoughts. Under the hood it is a TypeScript stack with the SDK shipped on npm, designed so the same engine can serve a single creator's personal agent or a SaaS company's fleet of per-customer agents.
What this proves for your project
MakeYourAgent is our own product, so the agent infrastructure inside it is something we designed, shipped, and operate. If you are thinking about AI agents in your own product, this is the experience you would be hiring.
AI agent infrastructure, end to end
We have built the whole path from an OpenAPI spec to a running, tool-calling agent: the runtime, the tool layer, the retrieval, and the hosting. That is a full agent platform, not a proof of concept, and we run it in production.
MCP servers and tool calling done right
We operate a managed MCP server and turn real APIs into safe, callable tools. If your project needs an agent that does things rather than just chats, this is exactly the muscle we have already built.
Multi-tenant safety and trust boundaries
Per-customer isolation, scoped keys, read-only mode, and audit events are the difference between an internal toy and something you can put in front of paying customers. We treat those as core, because our own users depend on them.
To apply this to your product, look at our AI workflow automation work and the internal tools we build, then check the pricing so there are no surprises.
Want something like this built for your team?
The engineers who built MakeYourAgent would build your agent too. Start with a fixed-price audit, or book a call and tell us what your customers need to be able to do.