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Contract review consumes billable hours that could be spent on strategic work. Missing a critical clause or deadline creates liability. This playbook shows how to build an AI system that reviews contracts, extracts key terms, flags risks, and manages the entire contract lifecycle.
Who This Is For
If Your Team Is...
The Problem
Most legal teams still handle contract review manually:
Meanwhile:
Target Outcomes
An orchestrated AI agent system should:
End-to-End Workflow
The key: AI handles the heavy lifting — attorneys focus on judgment calls.
We typically design 4 coordinated agents for this workflow:
Document Intake Agent
Goal: Capture contracts and classify them correctly.
✔️ Channels: Email (monitored folder), document management system, DocuSign webhook.
✔️ Responsibilities: Extract contract from email/system, classify by type (NDA, MSA, SOW, employment agreement, etc.), route to appropriate review queue.
Extraction Agent
Goal: Pull out every key term and data point.
✔️ Responsibilities: Extract parties, effective/expiration dates, payment terms, auto-renewal clauses, liability caps, indemnification language, IP provisions, termination rights, governing law/venue.
✔️ Output structured JSON with all key terms.
Risk Assessment Agent
Goal: Identify clauses that need attorney attention.
✔️ Responsibilities: Compare terms against your playbook (acceptable ranges, red-flag language), flag unusual or missing provisions, score overall contract risk (low/medium/high), explain why specific clauses were flagged.
Coordination & Repository Agent
Goal: Package everything for attorney review and manage lifecycle.
✔️ Responsibilities: Create Contract Brief (original + extracted terms + risk flags), notify assigned attorney, store in searchable repository, set calendar reminders for renewals/key dates, track obligations (e.g., "client must deliver X by Y date").
Extraction Agent — System Instructions
You are a Contract Extraction Agent for a legal team. You receive contracts and must extract all key terms with precision.
Your responsibilities:
✔️ Extract: Party names (full legal entities), effective date, expiration date, payment terms (amount, schedule, invoicing), renewal terms (auto-renew? notice period?), liability caps, indemnification scope, IP ownership/licensing, termination rights, notice requirements, governing law, venue for disputes.
✔️ Use exact language from the contract. Do not paraphrase. Include section references (e.g., "Section 8.3").
✔️ If a term is ambiguous or missing, flag it as "UNCLEAR" or "NOT FOUND" rather than guessing.
✔️ Output structured JSON with all extracted terms.
✔️ Never make legal judgments—only extract what the document says.
Risk Assessment Agent — System Instructions
You are a Risk Assessment Agent for a legal team. You receive extracted contract terms and compare them against your firm's contract playbook.
Your responsibilities:
✔️ Compare extracted terms against acceptable ranges defined in the playbook (e.g., liability cap should be >= contract value, IP ownership should remain with client unless specifically negotiated otherwise).
✔️ Flag clauses that deviate from standards or contain red-flag language (unlimited liability, broad indemnification, IP assignment without compensation, etc.).
✔️ Assign risk score: LOW (standard terms, no unusual provisions), MEDIUM (some non-standard terms that may need negotiation), HIGH (material deviations or missing critical protections).
✔️ Provide clear explanations for each flagged item: what the clause says, why it's concerning, what the playbook recommends instead.
✔️ Never approve or reject a contract—provide analysis for attorney decision-making.
You can implement this with different vendors; the pattern stays similar:
Core LLM / Agent Platform
✔️ OpenAI GPT-4 (with function calling), Claude, Azure AI, or custom LLM stack.
Legal Systems
✔️ Document management (NetDocuments, iManage, SharePoint, Box).
✔️ Contract management (Ironclad, ContractWorks, Airtable, custom database).
✔️ Matter management (Clio, Litify, custom).
Integration Layer
✔️ Zapier, Make, n8n, or custom APIs.
OCR & Document Processing
✔️ Adobe PDF Services, AWS Textract, Azure Document Intelligence (for scanned contracts).
Communication Channels
✔️ Email monitoring (monitored inbox), DocuSign webhooks, Slack/Teams notifications.
The key: seamless data flow from intake to repository without manual re-entry.
Because this playbook involves legal advice and client commitments, governance is critical.
Clear Boundaries
AI agents:
✔️ Can extract terms and flag risks based on predefined rules.
✔️ Cannot make final legal decisions or approve contracts.
✔️ Cannot negotiate on behalf of the firm or client.
Mandatory Human Review Points
✔️ All HIGH-risk contracts → senior attorney review before approval.
✔️ All contracts above certain dollar thresholds → partner approval.
✔️ Any contract with non-standard IP provisions → IP specialist review.
✔️ First use of new contract type → full attorney review to train the system.
Audit Trails
Log:
✔️ Which contracts were processed and when.
✔️ What terms were extracted (with confidence scores).
✔️ What risks were flagged and why.
✔️ Who reviewed and approved each contract.
✔️ Any attorney overrides (when agent flagged something but attorney approved anyway).This supports:
✔️ Professional liability defense.
✔️ Quality control and continuous improvement.
✔️ Training new attorneys on contract standards.
When you implement this system, track:
Time Savings
✔️ Before: 3-8 hours per contract for manual review
✔️ After: 45 minutes - 2 hours (AI extracts, attorney reviews findings)
✔️ Target: 60-75% time reduction
Accuracy
✔️ Extraction accuracy: Target 95%+ for key terms
✔️ Risk flag precision: What % of flagged issues were actually important?
✔️ Risk flag recall: Are we missing important issues?
Throughput
✔️ Contracts processed per week/month
✔️ Backlog reduction
✔️ Time from contract receipt to attorney review (target: < 4 hours)Repository Value
✔️ % of contracts searchable in repository
✔️ Time to find specific contract or clause type (before: 20-30 min, after: < 2 min)
✔️ Renewal reminders triggered on time: target 100%Business Impact
✔️ Contract cycle time (from negotiation start to signature)
✔️ Missed renewal notices (should approach zero)
✔️ Contract-related disputes (should decrease as risks are caught earlier)
Who This Is For
If Your Team Is...
Treating AI as a black box.
Attorneys need to understand what the AI is checking and why. Involve them in defining the playbook and risk rules from the start.
Over-relying on AI for novel contract types.
AI is best for contracts you see frequently. New or highly complex agreements still need full attorney review—use AI as a research assistant, not decision-maker.
Not maintaining the playbook.
Contract standards evolve (new laws, new business models). Schedule quarterly reviews of your playbook and update risk rules accordingly.
Poor change management.
Attorneys may resist if they don't trust the AI. Start with low-risk contract types, build confidence through accuracy, then expand to more complex agreements.
Ignoring extraction errors.
Track when the AI misses or misinterprets terms. Use these as training examples to improve the system. Every error is a learning opportunity.
You can use this playbook as a roadmap to build your own system. But if you:
✔️ Don't have LLM engineering expertise in-house
✔️ Need to integrate with complex document management systems
✔️ Want to go from pilot to production in 8-12 weeks with proven accuracy...then bringing in a team with legal AI deployment experience will accelerate your timeline and reduce risk.
This playbook is based on patterns we use when deploying contract review systems for legal teams.
If you'd like:
✔️ A tailored system for your specific contract types and playbook
✔️ Integration with your document management and matter systems
✔️ Production-ready deployment in 8-10 weeks
