Atlas Agents: Parallel Document Review and Knowledge Graph Curation

The Two Sides of Atlas Agents

Atlas Agents ship as a dual-layer execution system. On one side, they work inside Workspaces to review documents at scale and draft new client work from the findings. On the other, they continuously curate your firm's knowledge graph as new content arrives in iManage, SharePoint, OneDrive, and email—so every Atlas response downstream is grounded in your own precedents, playbooks, and work product, not generic LLM knowledge.

This design means the same agent runtime that reviews your discovery docs is also the system that reads new contracts into your knowledge graph, extracts and links indemnification patterns across your M&A matters, and keeps your clause library current. Both jobs happen simultaneously and feed each other: as agents refine the graph, Workspaces get smarter; as Workspaces execute reviews, they surface new patterns back into the graph for future matters.

What Shipped This Week

We just deployed workflow planner dispatch from chat. Now when your team writes a multi-step instruction in Atlas chat—"privilege-screen these discovery docs, extract key admissions, and draft a timeline summary"—the planner parses that into discrete agent tasks, assigns them to the right agents, and executes them end-to-end inside the matter Workspace. Each task has its own folder, status board, and output artifacts. The agent reads the plan, executes it, and writes results back into the Workspace for review.

This also ships with improved upload staging: documents now route to atlas-attachment-staging on first upload, which means agents can begin classifying and pre-processing while the full file library is still being uploaded. Pre-review work (privilege, conflicts, scope-screening) can start in parallel with ingest rather than waiting for all files to land.

In Practice: Three Matter Types

M&A Data Rooms. A team uploads 200 target company docs to a Workspace. Agents classify by document type (financial, legal, contracts, operations), then run a pre-review privilege check. Substantive agents extract key financial metrics, R&Ws, indemnification caps, and sandbagging clauses—writing findings into a tabular review editor. Post-review agents then generate a first-draft diligence memo, pulling language from similar prior deals in the firm's knowledge graph, and write it back into the Workspace for markup. Days instead of weeks.

Litigation Discovery. A litigation team loads 500 emails and documents from opposing counsel. Pre-review agents screen for privilege and work-product protection. Substantive agents extract admissions, timeline-critical facts, and document relationships. Post-review agents generate a facts summary and produce a privilege log from rejected documents. All four steps run in parallel across the corpus; the agents leverage the firm's litigation playbook (curated in Lists) to know which facts matter.

Contracts Review. A corporate team needs to review 50 NDAs for a customer-onboarding program. Agents classify by counterparty and compare each NDA against the firm's standard template (curated in the knowledge graph). Substantive review extracts liability caps, IP assignment terms, and indemnification scope. Post-review agents generate a redline memo for each outlier agreement and draft a summary legal opinion—all from the analysis, all grounded in prior NDAs and the firm's risk appetite.

The Knowledge Graph Flywheel

While Workspace agents review and draft, background agents are continuously reading new iManage and SharePoint content—classifying it, linking it to prior matters and precedents, extracting key terms and clause patterns, and refining the graph. This is invisible work, but it's what makes your firm's Atlas smarter over time.

When an M&A agent in a new deal references indemnification clauses, it's not guessing at generic patterns—it's pulling from every indemnification clause the firm has tagged and linked in the graph. When a litigation agent extracts timeline facts, it's using extraction rules learned from similar prior cases. The graph compounds.

Getting Started

Agents are live inside every Atlas Workspace. Create a Workspace for a matter, upload documents, then either:

• Use the Agent Task Board to manually design a pre-review, substantive, and post-review workflow. Assign each task to an agent and run it.
• Use Chat Planner to write a multi-step instruction ("privilege-screen, then extract key facts, then draft a summary") and have the planner break it into agent tasks automatically.

Your agents will execute in parallel and write results back into the Workspace—tabular reviews for interactive findings, new documents drafted from analysis, and updated folders organized by agent-assigned tags.

Visit atlas-ai.io to see a live Workspace and create your first matter.