TextMine Help Center

Agents are reusable AI instructions that can run against documents, Vault context, Workbench context, workflow inputs, or other configured TextMine data. Use them when a repeated analysis needs consistent instructions but does not need the full branching, approval, and side-effect model of a workflow.

When to use agents

Use case	Use Agents when	Use something else when
Repeated analysis	The same prompt or reasoning task should run many times.	Use Workbench for one-off exploration.
Document review support	The agent should summarize, classify, compare, or extract reasoning from documents.	Use Playbooks for formal review policies and clause checklists.
Workflow step	The agent should produce an output inside a repeatable process.	Use a Workflow when the process itself has triggers, routing, approvals, emails, records, or integrations.
Product assistant	The agent should help a team follow a recurring review method.	Use Records when the output must become durable structured data.

Where agents run

Agents can be used from several surfaces:

Agents page: create, edit, list, and run reusable agents where permitted.
Vault: run an agent against selected documents or Vault context.
Workbench: list agents, create a new agent when approved, run agents, and wait for completion.
Workflows: use run_agent as a step in a repeatable automation.
API/MCP: discover and run exposed agent operations where enabled.

Agent inputs

An agent should state which context it expects. Common inputs include:

Selected Vault documents.
Uploaded Workbench files.
Extracted tags, tables, entities, or records.
A workflow run payload.
A user-provided instruction or question.
External context imported from a provider.

If an agent depends on processed Vault data, wait for document processing before running it. If it depends on tags, tables, or entities, confirm those upstream outputs are current.

Agent outputs

Good agents produce predictable output formats. Examples:

Output	Use it for
Narrative summary	Human-readable analysis, review notes, or issue explanation.
Bullet list	Findings, risks, recommendations, or missing evidence.
Table	Comparisons, issue registers, review matrices, or extracted values.
JSON-like structure	Downstream workflow steps, API clients, or repeatable automation.
Decision with rationale	Escalation, approval support, or triage routing.

For durable updates, connect the agent to a workflow, record update, report, or approval process. Do not rely on free-form output alone when the result changes business data.

Run lifecycle

A typical agent run follows this lifecycle:

Select or discover the agent.
Confirm the scope, such as documents, Vault, record set, or workflow input.
Run the agent.
Wait for completion if the run is asynchronous.
Review the output and source evidence.
Use the output in Workbench, a workflow, a report, or a record process.

Agents should return run IDs and relevant document or record IDs when available so users can audit what happened.

Good agent design

A strong agent definition includes:

Purpose and boundaries.
Required inputs.
Output format.
Source and evidence expectations.
What the agent should not do.
When to escalate to a human.
Whether it can be used inside workflows.

Example instruction pattern:

Review the selected supplier agreement for renewal, termination, assignment, liability, and data protection risk. Return a table with issue, severity, source evidence, recommended action, and whether legal approval is required.

Governance

Agents can have broad operational impact when run over many documents or embedded in workflows. Treat the following as higher-risk actions:

Creating or editing shared agents.
Running agents over large Vault scopes.
Using agent output to update Records.
Triggering workflows from agent output.
Sending agent-generated reports externally.
Scheduling agents to run without human review.

Agents should ask for approval before creating shared agents, changing reusable agent instructions, or running an agent across a large scope.

Start here

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Manage documents and data

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Admin and updates

Agents overview

When to use agents

Where agents run

Agent inputs

Agent outputs

Run lifecycle

Good agent design

Governance

​When to use agents

​Where agents run

​Agent inputs

​Agent outputs

​Run lifecycle

​Good agent design

​Governance

When to use agents

Where agents run

Agent inputs

Agent outputs

Run lifecycle

Good agent design

Governance