Pulse for AI agents
How Pulse gives external AI agents authorized, deterministic access to your processes, with big-data work done inside the workflow.
Connecting an AI agent straight to your systems is easy to demo and hard to run. The moment it matters, three questions show up: who is the agent allowed to act for, will it improvise a critical step, and how do you move real volumes of data without melting the source. Pulse answers all three by putting Orchesty in front as the security and execution layer.
A dynamic MCP server, scoped per group #
Pulse exposes a curated set of your processes as tools over MCP. An agent authenticates with a key, fetches the list of tools it is allowed to use, and calls one with arguments. The list is built from the same authorization model you use for people: the agent belongs to a group, and the group grants specific processes. Anything outside that scope is invisible and cannot be run, even by guessing its name.
This is the difference between exposing "the database" and exposing a small, well-described catalog of business operations. The agent chooses from a short list of real actions, which keeps it accurate.
Deterministic by design #
The agent decides which operation to invoke based on the conversation. The workflow decides how that operation actually runs. Critical steps stay under engineering control, validated and tested, instead of being assembled on the fly.
Built for big data #
Many agent use cases need data that is moved, joined, and shaped, not just a single live call. Pulse does that heavy lifting inside the workflow: pull from sources, align, aggregate, and hand back a clean result. The agent stays conversational while the platform does the integration work, and raw data never has to stream back through the model.
One authorization, full audit #
Connectors call your systems with credentials that live in one controlled place. The agent never sees a key. The real caller is carried through the run and recorded, so every action is traceable to who asked for it.