AI Agents Overview

Waafir runs a workforce of specialist AI agents inside the data room. One summarises documents, one redacts sensitive information, one produces review reports, one proposes a folder structure, and one translates. Together they handle the repetitive reading and tidying that would otherwise consume a deal team's time.

This page is the model for the entire AI surface. Read it first; the individual feature pages cover each agent in detail.

The agents

Every organisation starts with the same five agents configured. No setup is required before they operate; they are ready as soon as your data room exists.

You can adjust how each agent behaves — for example, refining its instructions or the model it uses — but the five core agents are always present and cannot be removed. They define the baseline of platform capability: you tune them, you do not lose them.

Admins can also create their own agents on top of the same framework — with their own instructions, model, and trigger — and those agents inherit the same review and audit rules described below. See Custom AI Agents for how to build one.

Automatic versus on-demand

The split in the table above is deliberate and is the most important distinction to understand.

• Read-only work runs automatically. Summarising a document does not change it and carries no risk, so it runs as soon as a file is uploaded, with no action required. Summaries appear on your documents shortly after processing completes.
• Work that changes a document, or what an investor sees, is on-demand and reviewed. Redaction, organisation, and translation never apply silently. You trigger the agent, it produces a proposal, and you confirm it before it takes effect.

This is the human-in-the-loop principle, and it is not optional. The platform is built so that the AI cannot make a destructive or investor-visible change without human approval.

A typical agent run

When you trigger an on-demand agent — redacting a document, for example — the flow is always the same:

1. You start the action from the document.
2. The agent performs its reasoning and produces a proposed result.
3. The platform presents that proposal in a review screen — a redaction diff, a folder preview, a language toggle.
4. You confirm or reject it.
5. The change is applied only on your confirmation.

If you reject the proposal, nothing changes. Each feature page documents the exact review screen for that agent.

Beyond manual triggers

Most agent work starts from a click, but agents can also run on a schedule or be triggered automatically by an event such as an upload. The review step is unchanged regardless of the trigger: an agent that fires on a schedule still produces a proposal a human approves, for any change that is destructive or investor-visible.

Audit trail

Every AI action is recorded: which agent ran, on which document, what it proposed, and who approved or rejected it. This audit trail is central to why the platform is safe to use with sensitive deal documents — there is always an answer to "what did the AI do, and who signed off on it?" When you need to demonstrate control over how a data room was prepared, this record is the source.

Next steps

• Custom AI Agents — build your own agents on top of this framework, with your own prompts, models, and triggers.
• Redaction — the Redactor agent in detail.
• Translation — the Translator agent in detail.
• Organise — the Organiser agent in detail.
• Readiness scoring — how the platform assesses whether a data room is ready for investors.
• AI Assistant — the conversational surface, which is not one of these agents.