AI can help legal teams analyze clauses, compare documents, apply a playbook, extract terms, and summarize contracts—but it should not make material legal judgments without lawyer review. Choose a tool by testing it on representative agreements and checking its data protections, auditability, workflow fit, and limits. Treat “agent” claims separately: a system that can plan and take actions across other systems needs clear permissions and accountability, not just a promising contract-review demo.
What an AI agent does—and why the label matters
Generative AI typically produces content in response to a human request. Agentic AI is described as setting goals, planning actions, and executing strategies with less direct instruction. That difference matters in legal work: an assistant that drafts a clause for a lawyer to review presents a different control problem from an agent that can retrieve files, update a contract system, or send an action to another service.
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A report from the Law Society of England and Wales in April 2026 says solicitors often confuse agentic AI, AI agents, and generative AI, and that vendor language can raise expectations beyond demonstrated capabilities. It also describes legal teams experimenting with statistical sampling to evaluate large-scale review systems; that is an example reported by the Society, not evidence of universal adoption or a substitute for a team’s own validation.
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Which contracts are plausible candidates for AI assistance?
Suitability depends on the agreement, the task, and the team’s experience—not just the product category. The New York City Bar Association’s August 2026 policy paper considers standardization, complexity, tailoring, organizational familiarity, and the degree of negotiation. Its illustrative assessment places basic formation documents, board minutes, and NDAs among more suitable examples than shareholder, financing, or M&A agreements, with M&A requiring the greatest human judgment. These are examples from the paper, not categorical rules for every document or matter.
For a standardized agreement, AI may help flag deviations from approved terms or find clauses that need attention. A heavily negotiated document can involve linked provisions, business context, or bespoke risk allocation that a clause-by-clause check may miss. Use the tool to support review, not to treat an unverified summary or recommendation as legal advice.
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Tasks to consider for a bounded pilot
- Compare a draft against a known form or prior version and identify changes.
- Check clauses against a defined playbook, with the source text and rationale available for lawyer verification.
- Extract specified terms into a structured review or summarize a document for a lawyer.
- Identify missing, unusual, or inconsistent provisions for human assessment.
The New York City Bar paper connects AI use to existing professional duties, including competence, confidentiality, supervision, candor, client communication, billing, and anti-discrimination. In the United States, check the ethics guidance and court orders applicable to the jurisdiction and matter. Requirements and guidance vary; there is no single global framework established by the cited material.
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Do not judge a tool solely from a vendor demonstration or a few hand-picked documents. Singapore’s Ministry of Law guide, published March 6, 2026, recommends scoped pilots, performance benchmarks, review of vendor testing, user acceptance testing, feedback mechanisms, training, monitoring, and periodic assessment. Its recommendations are official Singapore guidance; teams elsewhere should also apply their own legal, privacy, security, and procurement requirements.
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- Define the task and boundary. Choose a specific use, such as comparing one agreement type with a playbook. State which documents are in scope, what the system may access, what it may produce, and which actions require a lawyer’s approval.
- Set a benchmark before testing. Have experienced reviewers establish expected findings and acceptable performance for the task. Include material risks, missed issues, unsupported assertions, and consistency—not just speed or user preference.
- Use representative documents. Include ordinary examples and meaningful variations in drafting, negotiation, and complexity. Protect confidential information and follow applicable client, privacy, and security requirements when selecting or preparing test material.
- Review outputs against the benchmark. Record whether the tool found the expected issues, introduced errors, explained its conclusions, and exposed the text or evidence a reviewer needs to verify them. Keep human review in place during the pilot.
- Test workflow and acceptance. Confirm that lawyers and legal operations staff can use the system in the intended document workflow. Gather structured user feedback, provide training, and assess whether the tool’s results are reviewable and useful in practice.
- Set monitoring and stop conditions. Decide who checks performance after deployment, how errors and changes are escalated, and what results would pause or end use. Reassess when the vendor changes the product, terms, or underlying workflow.
How to evaluate legal AI vendors
Singapore’s Ministry of Law guide recommends diligence on data practices, security, compatibility, performance evidence, explainability, auditability, vendor credentials, and implementation. The Law Society of Ireland’s 2026 assessment guide warns that consumer-facing AI terms may not be designed for legal practice: consumer and enterprise versions can differ in confidentiality, data residency, training use, and other protections. It links these concerns to client confidentiality, GDPR obligations, and possible privilege waiver in its jurisdictional context; teams should obtain advice on their own legal obligations rather than assume those consequences are identical everywhere.
- Data and confidentiality: Ask whether submitted prompts, files, and outputs are retained or used for model training; where processing and storage occur; who can access the data; and what contractual confidentiality protections apply. Review the actual terms for the edition being procured, not assumptions based on another version.
- Security and access: Check security measures, access controls, user administration, and how permissions work when the tool connects to internal repositories or other services. For agentic functions, identify the system identity, permitted actions, approval gates, and logs.
- Performance evidence: Request information about how the vendor tested the relevant task and documents, then compare it with results from your own benchmark. Vendor-reported testing is evidence to evaluate, not independent proof that the tool will perform adequately for your team.
- Explainability and auditability: Determine whether reviewers can inspect the relevant contract text, understand why a clause was flagged, and retain a record of inputs, outputs, edits, and approvals appropriate to the workflow.
- Workflow and knowledge fit: Test support for the agreement types and review tasks you actually handle, integration with your playbooks and institutional knowledge, compatibility with document systems, and the effort needed to maintain those connections.
- Implementation and continuity: Assess onboarding, user training, ongoing support, maintenance, scalability, vendor credentials and track record, and the practical ability to retrieve or remove data and leave the service.
- Cost and impact: Compare total cost with a defined, measurable benefit for the scoped task. Do not treat faster draft output alone as proof of improved legal review.
The 2026 Texas Bar Knowledge Center’s Legal AI Planning Guide describes a framework covering accuracy and reliability, firm-knowledge integration, platform fit, advanced capabilities, vendor partnership, pilot testing, and ROI methods. Its public page exposes those categories, but not the full scoring rubric; it should not be presented as an independently examined scoring system.
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What the named product pages establish—and what they do not
Product descriptions can help identify candidates for a pilot, but they do not establish comparative accuracy or a recommendation. The following information comes from vendor pages, not independent product testing:
| Product | What the vendor page describes | Pricing information on the reviewed page | What remains unestablished here |
|---|---|---|---|
| Spellbook | Presented as contract-review software for law firms. | Customized based on team size. | Independently measured accuracy, performance against a particular playbook, and comparative results. |
| Thomson Reuters CoCounsel Legal | Described for legal analysis and drafting workflows, including contract modification and document discovery review, alongside Westlaw and Practical Law content. | Not stated on the cited product description. | Independent results for a team’s agreements, workflow, or review benchmark. |
| CoCounsel plans | The plan page lists document review, clause drafting and modification, and contract playbooks. | Available by request on the reviewed page. | Independent comparative performance or results for a specific legal team. |
For any candidate—including tools not named here—run the same representative agreements and playbooks through the same evaluation rubric. The evidence summarized here does not benchmark these products against one another, so it cannot support a claim that one is more accurate or safer than another.
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What to require before allowing an agent to act
A review system that only returns text still needs validation and confidentiality controls. If it can take actions across systems, the team also needs to govern what it is authorized to do and how people can reconstruct what happened. NIST’s agent work highlights security and identity as active areas of concern, not settled implementation rules for legal teams.
- Give the agent only the permissions needed for its defined task; do not assume its access should match a user’s full access.
- Require human approval before consequential actions such as changing a record, sending a document, or initiating an external workflow.
- Keep usable records of the agent’s identity, instructions, retrieved material, actions, and approvals, proportionate to the risk and applicable requirements.
- Plan for errors and interrupted workflows: identify who can stop access, correct records, investigate an action, and notify the appropriate people.
These are governance questions to resolve before deployment, not claims that a particular vendor offers every control. Verify each capability and contractual commitment with the vendor for the product version and configuration under consideration.
Quick Recap
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