How Atlas Agents Execute Multi-Step Legal Workflows on Your Firm's Data

What Atlas Agents Do

Atlas Agents are autonomous systems that execute multi-step legal workflows. They don't generate text on command; they read documents, extract findings, design review processes, and draft new client documents from those findings, all happening inside a matter-scoped Workspace.

Agents work in two places on the Atlas platform:

1. Inside Workspaces: They run document review at scale. Upload hundreds of contracts, and agents classify them, route them to the right folders, run pre-review processes (privilege, conflicts, scope), execute substantive review (R&Ws, indemnification clauses, diligence findings), and finally draft new client documents like redline markups, schedules, or closing memos.

2. On the Knowledge Graph: They continuously curate the firm's data layer. As new documents land in iManage, SharePoint, OneDrive, or email, agents read, classify, and link them into the firm's knowledge graph, so every agent response in a Workspace is grounded in precedent and matter history, not generic training data.

The Curated Data Layer

The critical difference between Atlas Agents and a generic AI agent is the foundation. Every agent execution runs against Singularity (v6), the Atlas platform that ingests your firm's existing document stores, iManage, SharePoint, OneDrive, NetDocuments, email, and continuously curates them into a per-tenant knowledge graph.

This curation is agentic. As your firm's files and emails land, Atlas Agents read them, extract entities and relationships, classify them by matter and practice area, and link them to existing precedent. That means when you run a deal review agent on a new NDA, the agent doesn't just see the document in isolation; it sees every NDA your firm has drafted, negotiated, and closed over the past five years, and it runs its review against that practice-area history.

What Just Shipped

This release focused on making agent work visible and editable in real time.

Agent Task Boards: When an agent runs inside a Workspace, its work now appears as a structured task board. You can see which documents it's processing, what step it's on (privilege screening, clause extraction, diligence findings), and which tasks have completed or are waiting for human input. Each task is a real execution unit; agents don't just hallucinate steps, they run them sequentially against the curated data.

Inline Tabular Review Editor: Agents produce findings as structured tabular reviews (clause flags, R&W gaps, indemnification issues). You can now edit those findings inline without leaving the Workspace. When you correct a finding or add context, the agent execution layer receives that feedback and rewrites the updated findings back into the workspace, so the work stays synchronized.

Auto-Generated Agent Folders: Agents organize documents as they review them, creating folders for high-priority items, resolved issues, and further review needed. Those folders now persist in the Workspace and stay accessible to the whole deal team, so agents' organizational work becomes the team's workflow.

Concrete Example: M&A Due Diligence

A partner uploads 150 contracts for a target company acquisition. She points an agent at the document set and specifies "run standard M&A review."

The agent:

• Classifies each contract by type (supply, customer, IP, employment, lease)
• Runs privilege and conflicts screening, flagging documents that need human legal review before the review can proceed
• Extracts key contract clauses (change of control, termination rights, indemnification) and compares them to the firm's deal precedent
• Produces a structured diligence memo with gaps, risks, and recommended redlines
• Drafts a marked-up version of the highest-risk contracts

All of this happens in parallel against 150 documents. The agent reads the firm's internal M&A playbook (stored in the knowledge graph), applies it, and produces auditable findings that the team can edit inline.

The agent can run the same workflow on the next deal because it's built as a reusable action list (Lists in Atlas), and it'll execute differently each time because it's reading different firm precedent and different deal facts.

Why This Matters

Legal work at scale today means hiring contract review firms, staffing up associates for due diligence, or running manual spreadsheets. Agent-driven review at scale means your team can process hundreds of documents in days, with agents applying your firm's playbook and precedent instead of generic frameworks.

Because agents are grounded in your firm's curated knowledge graph, they don't drift into generic outputs. A supply contract review agent learns from your firm's supply contracts. A privilege review agent applies your firm's privilege protocols. The more work the firm does, the sharper the agents become.

How to Try It

Visit https://atlas-ai.io to see Atlas Agents in action. Create a Workspace, upload documents, and run an agent. You'll see the task board in real time, watch the agent process documents in parallel, and edit the findings inline. Share the Workspace with your team to see how agent work becomes the team's workflow.

Agents are live in Atlas v6 across all Workspaces.