Connect AI Agents to Your Product
Give agents real work to do
Your product team is being asked about its agent strategy. Expose the flows you already run as tools, and your assistant can look up a record, file a ticket, or pull a document from the systems your customers have connected to – all from inside your product.
Your customers are building and buying their own agents. Any MCP-compatible agent can connect to the same tools, scoped to that customer, and see only the flows deployed to them with their credentials. Your product becomes part of how they work.
Agents interpret intent and pick a tool. Your flow enforces what has to be right every time: authentication, sequencing, validation, and error handling. The invocation schema shapes the inputs, but the flow's logic holds the guardrails, not the prompt.
Every tool call runs in the customer's deployed instance with their connections, so an agent working for one customer never sees another's data. Agents authenticate with your embedded JWT or an OAuth consent flow, and every execution is logged, with alerting that you configure.
Tools for the work your customers hand to agents
Look up records
Answer questions from the customer's CRM. An agent asks for open opportunities or a contact's history, and the flow queries Salesforce, HubSpot, or MS Dynamics 365.
Act in connected apps
Create the ticket, post the update, or log the event. The agent supplies the inputs, and the flow writes to Zendesk, Jira, or Slack using the customer's credentials.
Fetch and process documents
Pull files from Google Drive, Dropbox, or Microsoft SharePoint, extract what the agent needs, and return structured results instead of raw API responses.
Enrich data before an agent acts
Enrich, classify, or summarize data in the flow before it reaches the agent, with connectors for OpenAI, Anthropic, and Google Gemini that use each customer's credentials.
The agent decides. The flow does the work.
What the agent handles
Reads the tools available to that customer, interprets the request, chooses a flow, and fills in the inputs from the conversation.
WHY
That is what models do best: understand intent and pick the right capability from a short, well-described list.
HOW
Point your assistant, or your customer's agent, at the MCP flow server. Tool names, descriptions, and schemas are what the model reads, so write them like prompt copy.
The platform that runs it
Authenticates as the customer and calls the systems in the order you defined, with input validation, error handling, and result shaping built in by your team.
WHY
Chaining raw API calls from a prompt breaks in production. A flow your team builds runs the same way every time, and you can test, monitor, and fix it.
HOW
Build it in TypeScript or low-code on the same platform as the rest of your catalog. Mark it as agent-ready, add an invocation schema, and agents can call it.
Don't take our word for it
Common questions
The agent, whether it is the assistant you ship or one your customer brings, can call your product's integrations and workflows as tools. It looks up records, takes actions, and fetches documents in the systems your customers have connected, from inside your product, using the customer's credentials.
A flow you designate as callable by AI agents. It carries an invocation schema that describes what it does, what inputs it accepts, and what it returns. Build it in TypeScript or low-code, mark it tool-enabled, and it appears to agents as a tool. A flow you already run can become one the same way: add the schema and enable it as a tool.
Prismatic's hosted server exposes agentic flows as tools through MCP, the open protocol AI agents use to discover and call tools. An agent connects, receives the flows available to that customer along with their schemas, and invokes them. Endpoints are available per region and can be scoped to a single integration or a single instance.
Any MCP-compatible client that supports streamable HTTP: Claude Desktop, Claude Code, Cursor, or an assistant you build with an AI SDK on Claude, OpenAI, Gemini, or another provider. If you need to filter flows or use your own authentication, build a custom MCP server on Prismatic's API.
An agent embedded in your product authenticates with the same embedded JWT you use for embedding Prismatic, scoped to that customer. Standalone clients use an OAuth 2.0 consent flow. Either way, the agent sees only the agentic flows on instances deployed to that customer and acts with that customer's connections.
No. Add an invocation schema to a flow you already run, enable it for the MCP flow server, and it becomes a tool. You don't need separate execution infrastructure or an auth system; agentic flows deploy through the same platform as your other integrations.
Your team builds agentic flows today in code-first or low-code and deploys them to each customer. Exposing the workflows your customers build in the embedded workflow builder as agent tools is on Prismatic's roadmap.
Yes. Connectors for OpenAI, Anthropic, Google Gemini, Azure OpenAI Service, and xAI Grok let a flow summarize, classify, extract, or enrich data as it moves between systems. Customers can use their AI credentials or shared credentials you configure, and you can build a custom connector for any other model with an API.