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Collections is built for agents. Once a collection is linked to an agent, the agent can query it, compute exact aggregations, and write records back — all during a normal conversation. This page explains what agents can do, how access works, and how to keep control.

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What an Agent Can Do with a Collection

When an agent has access to a collection, it gains a set of data tools: Because these are structured operations on real data, answers are exact — a count is a count, not an estimate from retrieved text.

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Linking a Collection to an Agent

1

Open your agent in Agent Creator

Edit the agent that should use the data.
2

Add the data analysis capability

Add the collection/data tool to the agent’s capabilities.
3

Select the collection

Search and select the collection to link. The linked collection appears as a card showing its rows, columns, and linked agents.
4

Test in the Playground

Ask a question the data can answer (“How many active customers do we have?”) and check the agent uses the collection tools.
Back in Collections, the collection card shows which agents are linked to it.

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Access and Isolation

  • Private by default. A collection is visible only to its owner and the agents explicitly granted access. Different agents’ working data is isolated — one agent never sees another agent’s records unless you share the collection.
  • Shared when you decide. A collection can be linked to several agents when they collaborate on the same dataset — for example, a capture agent that inserts leads and an analysis agent that reports on them.
  • System fields are protected. Agents cannot alter record identifiers, timestamps, or ownership metadata.
  • Deletes are guarded. Bulk deletion by agents is disabled unless explicitly allowed by your platform configuration — an agent cannot wipe a collection by accident.

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Auditability

Every write an agent performs is recorded as an audit event — insertions, updates, upserts, and deletions, with the actor and the affected collection. Denied access attempts are logged too. Your platform administrators can monitor these events from the governance tools.

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Example: A Lead-Capture Agent

1

Create the collection

Create a leads collection — either empty, or from a CSV of existing leads with columns like name, email, company, status.
2

Link it to your agent

Give your sales assistant agent access to the collection, with instructions such as: “When a visitor shares contact details, upsert them into the leads collection using the email as the key.”
3

Let it run

During conversations, the agent records each lead. Upserting by email means a returning visitor updates their existing record instead of creating a duplicate.
4

Analyze

Ask the same agent — or another one linked to the collection — “How many new leads this week, by company?” and get exact numbers. Track the trend with a chart in the Visualize tab.

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Best Practices

Column names alone are ambiguous. In the agent’s instructions, describe the collection: what one row represents, what units amounts are in, what the status values mean.
Contacts have emails, products have SKUs, tickets have references. Instructing the agent to upsert on that key prevents duplicate records over time.
If only one agent writes and others read, data quality issues have a single source — much easier to debug than five agents all inserting into the same collection.
Open the Data tab and sort by newest records to spot formatting drift early (wrong date formats, inconsistent categories), then tighten the agent’s instructions.

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Next Steps

API Reference

The underlying REST and MCP interfaces

Agent Creator

Build and configure your agents