Agentic Multi-Step Execution: Reasoning Over Your Firm's Knowledge Graph

The Problem: Generic LLM Reasoning on Generic Data

Frontier language models are good at reasoning, but they reason on whatever data they have access to. For law firms, that usually means the generic internet, the model's training data, or whatever snippet gets pulled out of a vector search and stuffed into a prompt. The model has no context about your firm's precedent, your deal playbooks, your risk appetite, or your matter-specific facts. It makes better arguments when it knows your firm.

AtlasAI Singularity changes this. The platform ingests your firm's data from iManage, SharePoint, OneDrive, NetDocuments, and email into a per-tenant curated knowledge graph. That graph is shaped continuously by Atlas Agents themselves - reading, classifying, linking, and refining new content as it lands. When Atlas Agents execute tasks inside a Workspace or outside curating the graph, they reason against YOUR firm's work, not the public internet.

What Shipped: Multi-Step Agent Execution

Atlas Agents now execute full multi-step workflows. Inside Workspaces, agents can chain multiple reasoning steps: screen documents for privilege, run conflicts, extract key clauses, compare them to firm precedent, flag issues, and draft new contract language or diligence memos - all in one coordinated task. The planner component breaks down complex workflows into discrete steps, handles token budgets, and recovers cleanly when boundaries shift. Tasks reference prior reviews, so an agent that runs redlines can see what the prior privilege agent flagged and build on those findings.

Outside Workspaces, the same agent execution layer powers knowledge graph curation. As new documents land in iManage or SharePoint, Atlas Agents read them, classify them into matter buckets, extract relevant entities and relationships, and link them into the graph. This happens continuously and agentic - agents don't just ingest data, they understand it and shape it.

How It Works: A Real Example

Consider an M&A team running deal review. They upload 300 PDFs from diligence into an Atlas Workspace. An agent task board triggers a multi-step workflow:

1. Privilege agent reads all 300 documents, flags attorney communications, generates a privilege summary and flags for outside counsel. 2. Conflicts agent checks the flagged subset against the firm's conflict database (curated into the knowledge graph from iManage lookups). 3. Clause-extraction agent pulls key M&A terms (reps and warranties, indemnification caps, post-close adjustments) from each deal document. 4. Comparison agent reads those clauses against the firm's last 10 successful deals (linked in the graph), flags deviations from firm precedent. 5. Redline agent generates marked-up contract language for key sections, drafts an indemnification schedule, and writes a due-diligence memo.

All 5 steps run in parallel where possible, reference each other's output, and every agent decision is grounded in the firm's curated graph - not a generic model. A human attorney reviews the drafts and the summary, approves them, and sends them to the client.

Workspace Agents vs. Graph Curation Agents

Atlas Agents do two jobs. In-Workspace agents review documents, draft client deliverables, and collaborate with human attorneys on specific matters. You design these agents once in a Workspace and run them on every new deal or case in that matter type. You can see agent task boards, inline editors for review findings, and auto-generated folders keeping workspace structure in sync with agent discoveries.

Graph-curation agents run continuously in the background, reading new iManage or SharePoint content and refining the firm's knowledge graph. These agents classify documents into matters, extract entities (client names, deal types, clause templates), and link relationships so the graph stays sharp. Both types of agent share the same execution layer, the same token budgets, and the same access controls.

What AmLaw Teams Use It For

M&A teams build deal-review agents that extract and flag deal terms, compare them to precedent, and draft closing documents from the comparison.

Litigation teams build privilege agents to screen documents before substantive review, conflict agents to catch adverse parties, and summary agents to distill findings into litigation memos.

IP teams build patent-diligence agents that extract prior art, flag invalidity risks, and draft validity opinions.

Corporate teams build contract-review agents that check terms against policy, flag indemnification exposure, and draft redlines or termination notices.

Every agent workflow reasons over the firm's own work. When the privilege agent flags attorney-client communication, it's checking against the firm's real iManage privilege matrix and case history. When the M&A comparison agent flags a deal term, it's comparing against the firm's actual precedent deals stored in the graph. The reasoning is specific, grounded, and auditable.

Grounded in the Curated Knowledge Graph

Every agent decision traces back to the curated graph. If a clause-extraction agent finds a "post-close adjustment" clause, it can link that finding to similar clauses in prior deals, prior risk assessments, and firm policies - all because the graph already connected those relationships. If the privilege agent flags a document, the flag lives in the graph and flows to every downstream review step. The graph is the shared source of truth.

How to Try It

Atlas Agents are live in Singularity v6. Visit https://atlas-ai.io to see agents in action on real Workspaces, set up a demo matter with your team, and build your first practice-area agent workflow.