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An Agent node on its own can only reason about what you type into it. Tools are what let it answer from your own material, call an outside system, check itself before it speaks, and pause for a person.

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The tool and logic nodes

Only Agent and Classify spend credits. Every node on this page is free to run.

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Answer from your own material

1

Add a File search node

Drag File search onto the canvas and connect it before the Agent node that will use its results.
2

Create a knowledge base

In the node’s panel, give it a Knowledge base name and paste your content — the box prompts you to “Paste FAQs, policies, protocols…”. Opening hours, preparation instructions, a returns policy and a referral protocol are all good candidates.
3

Point the Agent node at the results

The search writes its matches into the run. Reference them from the Agent node’s instructions so the model answers from your material rather than from general knowledge.
Keep one knowledge base per subject rather than one enormous one — a narrow search returns better matches, and you can retire a policy without rewriting everything else.

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Constrain what the agent may say

Guardrails checks a value and sends the run down a pass or a fail path. Put one between the Agent node and anything that sends a message, and route fail to an End node or to a User approval step so a doubtful answer is seen by a person instead of a customer.

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Put a person in the loop

User approval is the node to reach for whenever the agent is about to do something you would want to sign off yourself — sending a message on your workspace’s behalf, changing a record, or committing money. The run halts at that node until someone approves it, so an agent that would otherwise act at 3 a.m. simply waits.

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Connect an outside tool server

An MCP node lets an agent call a system you already use.
1

Add the server

From the MCP node’s panel, or from under Connected tools (MCP), select Add MCP server. Enter a Name (e.g. Weather API) and the server’s address — the field shows the shape it expects, https://server.example.com/mcp.
2

Choose how it authenticates

Pick No auth, Bearer token, or OAuth. OAuth asks for the authorization endpoint, the token endpoint, a client ID, a client secret and a scope, then opens the provider’s own sign-in window.
3

Check it responds

Use the test action before saving. It reports how many tools the server offers and names the first few, so you know you have the right address before you build against it.
4

Pick the tools the agent may call

Select the server on the MCP node, then choose which of its tools this agent is allowed to use. Give an agent the one tool it needs, not the whole server.
Identifying details are stripped out of the arguments before anything is sent to a third-party server, so a connected tool sees the question but not the person it is about. That also means an outside tool cannot look someone up by name — design around it rather than trying to defeat it. See AI safety and patient data.

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Reusable instructions and regression checks

Two sheets in the canvas top bar are worth knowing about early:
  • Playbooks hold instructions you want more than one agent to follow, so a wording change lands everywhere at once instead of in six separate nodes.
  • Evals record the answers you expect and re-check them after you edit. Build a handful for anything you deploy — otherwise “I improved the prompt” is a guess.

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Let other systems call your agents

Your deployed agents can themselves be offered to another system as tools, from Expose as MCP (“let other agents call your agents”) under . Each endpoint is rate-limited, capped per month and revocable, and you choose which published agents it exposes. This is a Team feature.

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Check it worked

  • The File search node names your knowledge base, and a Preview run quotes something only your own material could have said.
  • The MCP node lists the connected server’s tools in its picker.
  • A run that hits User approval appears on the Runs page waiting rather than finished.

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If something goes wrong

Check the File search node actually runs before the Agent node — a node that is on the canvas but not connected never executes. Then check the Agent node’s instructions refer to the search results; the model does not read them automatically.
Test the address before saving. A trailing path matters — most servers expect the address to end in /mcp. If it authenticates with a token, confirm the token is current; if with OAuth, look for a Reconnect badge on the server and reconnect it.
That is the de-identification working. Names and identifying details never leave your workspace, so an outside tool cannot match on them. Do the lookup inside ClinikEHR and send the outside tool only what it genuinely needs.
User approval blocks the run until someone acts. If nobody is watching, move the approval to only the branch that needs it, or replace it with Guardrails for the cases that can be decided automatically.

Build an AI agent

The canvas, triggers, and why Deploy is the step that matters.

AI safety and patient data

What leaves your workspace, and what never does.