A great SaaS app isn’t enough any more.

For most of the last decade, a good SaaS product won by being the place people went to get something done. The dashboard, the report, the export. If the app was fast, clear and reliable, users came back.

That habit is changing. Founders, operators and analysts now keep Claude or ChatGPT open all day. They draft there, plan there and increasingly ask there. When they want to know how last quarter went, they type the question into an assistant before they open your app.

And the assistant can’t answer it. The data is in your product, behind a login, in a format only your screens understand. So your users copy numbers across by hand, paste in a CSV, or settle for a guess.

This isn’t a niche trend. In June 2026, AWS described agentic AI as the third big shift in software, after the move to the web and the move to the cloud, and argued it grows the SaaS market rather than shrinking it. AWS cites Forrester’s estimate that SaaS grows from about $300 billion today to $576 billion by 2029.

The channel is filling up fast. The AI agents and tools category on AWS Marketplace went from 900 listings at launch to more than 3,300 in under a year, most of them existing SaaS products made usable by agents. AWS’s headline for software companies is blunt: “Agentic AI isn’t killing SaaS, it’s transforming it.”

And the results are already public. AWS reports that Workday cut its sales prospecting cycle by 75% with an AI sales companion, that Zendesk customers automate 80% of customer service interactions with Amazon Bedrock and Amazon Connect, and that Druva’s multi-agent system resolves 68% of customer issues without manual intervention. None of them are Assembly clients; they show where the market is going.

The Model Context Protocol (MCP) is an open standard that lets an assistant call tools in other products. An MCP server sits in front of the APIs you already have and describes them as tools: “get a KPI summary”, “list this month’s placements”, “export this dataset”. Claude, ChatGPT and other assistants can then choose the right tool for the question a user asks. AWS names MCP as the standard for agents to find and call the tools in your product.

On AWS, Amazon Bedrock AgentCore can turn existing REST APIs into an MCP server, so you don’t rebuild your product to open this channel. You start from what you have, and improve the tools once you see what users actually ask.

Putting customer data in front of an assistant only works if your security team and your enterprise customers can sign it off. In practice that means four things.

Sign-in through your own accounts. Users connect with the account they already have; the assistant gets a scoped token, never a password or a shared API key.

Read-only tools first. Start with questions, not actions. No tool changes a record until you decide it should.

Each customer sees only their own data. Who the user is, and which customer they belong to, comes from the verified sign-in, never from the prompt, and is enforced again where the data lives.

Every call logged. Who asked, which tool, the outcome. Never a token in the log.

Here’s the part that’s easy to miss. Plugging your data into an assistant is useful. But the lasting value isn’t the data, it’s the context around it.

A general assistant knows a little about everything. It will happily guess what “margin” or “active customer” means in your industry, and the guess will sound plausible. Your product knows exactly what those words mean, how the numbers connect and which questions matter to your customers.

An MCP server lets you package that context: your data model, which pulls data together from many systems into one shape; your domain logic, the definitions and KPIs your customers trust; and your workflow, from a question to a chart, a CSV or a client-ready deck. That is what turns a plausible guess into an answer from your niche.

It also decides whether you get picked at all. AWS calls it agentic discoverability: assistants choose tools on precision, not brand. Clear tool names and precise descriptions are what make an assistant reach for your product instead of guessing.

Anyone can plug in a model. Only you can give it your niche.

We’re building exactly this with a recruitment SaaS platform. An agency director adds the platform as a connector in Claude, signs in with their existing account and asks how placements, fees and margin tracked this quarter. The answer comes back as a chart in the chat, from their own agency’s data only. The same server works in ChatGPT.

It started not with code but with an assessment: which questions customers ask, which tools the server needs, how sign-in works and what it costs to run.

Start with one question your users ask most, and the APIs that already answer it. Agree how users sign in and what each customer is allowed to see. Then build the smallest set of read-only tools that answers that question well, measure what people ask, and grow from there. The same logs that keep the server secure also tell you what it’s worth, which matters once you price for what agents do, not just for seats.

If you’re a startup already in production, AWS may fund an AI Assess to map it out with us: the use case, the architecture on AWS, the running cost and a plan you can build from, yourselves or with us.