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AI can help legal teams review contracts by analyzing clauses, comparing drafts, checking terms against a playbook, extracting information, and summarizing documents. It should support—not replace—a lawyer’s judgment. The safest choice depends less on whether a vendor calls its product an “AI agent” and more on the agreement, the task, the tool’s data protections, and how reliably your team can verify its work.

What AI can—and cannot—do in contract review

For bounded tasks, AI can help lawyers work through contract language more efficiently. A team might use it to identify clauses that differ from a standard position, compare two versions, extract key terms, or summarize a document. A tool can also check language against a defined playbook, provided the playbook and the tool’s output are reviewed by people.

These capabilities do not make an output legal advice that can be accepted without verification. The New York City Bar Association’s August 2026 policy paper says AI tools may assist legal work but cannot substitute for professional legal judgment. It connects AI use to existing professional duties, including competence, confidentiality, supervision, candor, client communication, billing, and anti-discrimination.

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Those duties and related court requirements vary by jurisdiction. U.S. teams should check the ethics guidance and court orders that apply to their work rather than assume one uniform rule. The same caution applies internationally: guidance from one jurisdiction is useful for evaluation, but it is not automatically the governing rule elsewhere.

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Match the tool to the agreement and task

Whether AI is suitable depends on the document’s standardization, complexity, degree of tailoring and negotiation, and the team’s experience with that document type—not just the tool’s label. The New York City Bar paper offers illustrative examples, not a categorical rule for every matter:

Agreement or document type Illustrative fit for AI assistance Important qualification
Basic formation documents, board minutes, and NDAs More suitable candidates in the paper’s framework Suitability still depends on the specific document, negotiation, and team experience.
Shareholder and financing agreements Less suitable than the paper’s simpler examples Complexity and tailoring can increase the need for human judgment.
M&A agreements The paper identifies these as demanding the greatest human judgment Do not treat automated issue-spotting or summaries as a substitute for lawyer review.

A standardized agreement familiar to your team may be a reasonable place to test a narrowly defined review task. A highly negotiated or consequential document calls for greater human scrutiny. The table is a guide to scoping work, not permission to accept an unverified result.

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What “AI agent” means—and why autonomy matters

Generative AI typically produces content in response to a person’s prompt. Agentic AI is described by the Law Society of England and Wales’ April 2026 report as setting goals, planning actions, and executing strategies with less direct instruction. In practice, terms such as “agentic AI,” “AI agent,” and “generative AI” are sometimes confused, and vendor language can create expectations that exceed a product’s actual capabilities.

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For contract review, a more autonomous system may do more than analyze text: it could interact with other systems or internal data. That raises questions beyond whether a clause summary is accurate. Before enabling actions across systems, establish what the agent can access, what identity it uses, which actions it may take, what requires approval, and how its actions are recorded and attributed.

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NIST launched its AI Agent Standards Initiative on February 17, 2026, with work covering standards, open protocols, security, and identity. The initiative is ongoing, not a completed legal-sector standard. In a May 18, 2026 summary of responses to an AI-agent-security request for information, NIST reported stakeholder concern about novel security threats and calls to adapt cybersecurity practices. That summary reflects comments received; it is not a controlled measure of how widespread any threat is.

How to evaluate legal AI vendors

Assess the product against the work your team actually needs to do. Singapore’s Ministry of Law’s March 6, 2026 guide recommends due diligence on vendor data practices, security, compatibility, performance evidence, explainability, auditability, implementation, and support. The Law Society of Ireland’s 2026 assessment guide adds a practical warning: consumer-facing terms may not provide the confidentiality, data-residency, or training-use restrictions required for legal work, and enterprise terms may differ.

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Evaluation area Questions to put to the vendor
Accuracy and reliability What task-specific performance evidence is available? How was it measured, on which documents, and against what benchmark?
Task and agreement fit Does the product support the agreement types, clause checks, comparisons, extraction, or summaries your team needs?
Playbook and internal knowledge Can the system use your approved playbooks or institutional knowledge? How are those materials governed and updated?
Data use and confidentiality Can inputs be used for model training? How long are prompts and documents retained? What confidentiality commitments apply?
Location, access, and security Where is data stored and processed? Which jurisdiction applies? What access controls and security measures are available?
Explainability and auditability Can reviewers see the basis for an output, citations to source text, relevant logs, and an audit trail?
Integration and operations Does the tool work with your document and technology systems? What maintenance, onboarding, training, and support are provided?
Accountability and exit Who is responsible for reviewing outputs and agent actions? What happens to your data and materials if you leave the service?
Total cost and impact What is the full cost of adoption, and which measurable outcomes will determine whether the tool is worth maintaining?

Ask for written answers and applicable contract terms, not only demonstrations. The Law Society of Ireland flags confidentiality, GDPR obligations, and possible privilege waiver as concerns in its jurisdictional context; those points should not be treated as individualized legal advice or as a substitute for checking the rules that apply to your organization.

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Run a representative pilot before wider use

A vendor demo can show a workflow, but it cannot establish how the system performs on your team’s documents. Singapore’s Ministry of Law guide recommends scoped pilots, success metrics, user acceptance testing, feedback, training, monitoring, and periodic assessment. A practical pilot can follow these steps:

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  1. Choose a bounded use case. Specify the agreement type and task, such as identifying departures from a particular playbook provision. Avoid starting with an open-ended mandate to “review everything.”
  2. Set the review boundary. Decide which documents and information may be used, who can access the tool, what the system may do, and which actions require lawyer approval. For an agent that interacts with other systems, define its permissions and identity before enabling those connections.
  3. Set success criteria in advance. Define an acceptable performance threshold for the task, including how your team will handle missed issues, unsupported conclusions, and differences from the playbook. Have reviewers apply the same rubric to the AI-assisted work and the team’s benchmark.
  4. Use representative documents. Include examples that reflect the team’s real agreements and review conditions. Do not infer suitability for a different agreement type or workflow from a narrow pilot.
  5. Review vendor evidence and test the output. Ask how any vendor-reported testing was conducted, then compare the product’s results with legal-team benchmarks. Have qualified reviewers verify substantive outputs and record failures as well as useful results.
  6. Conduct user acceptance testing and gather feedback. Check whether the workflow fits actual users’ needs, systems, and review practices. Train users on the tool’s boundaries and how to escalate uncertain or consequential issues.
  7. Monitor after deployment. Track performance, user feedback, access, and changes to the product or its terms. Reassess periodically rather than treating initial approval as permanent.

For large-scale review, the Law Society of England and Wales report notes that some legal teams are experimenting with statistical sampling instead of manually checking every output. That is an approach described in the report, not evidence that sampling is sufficient for every matter. A team should determine whether its review method is appropriate to the document, risk, and applicable professional obligations.

Examples of current product positioning

Product pages describe capabilities; they do not establish independent accuracy or prove that a tool is right for a particular team. The following examples are vendor claims, not results of a comparative test.

Product What the vendor describes What that does not establish
Spellbook Its law-firm page presents it as contract-review software for law firms and says pricing is customized based on team size. Independent accuracy, performance on your agreements, or suitability for your workflow.
Thomson Reuters CoCounsel Legal The vendor describes legal analysis and drafting workflows, including contract modification and document-discovery review, alongside Westlaw and Practical Law content. Independent outcomes, comparative performance, or fit for your organization.
CoCounsel plans The vendor’s plan page lists document review, clause drafting and modification, and contract playbooks; pricing is offered by request on the reviewed page. Independent evidence that the listed capabilities meet your team’s requirements.

Compare products using the same representative agreements, playbooks, reviewers, and evaluation rubric. No comparative benchmark of these named products is established here, so the descriptions above should not be read as a ranking or endorsement. The 2026 Texas Bar Knowledge Center’s Legal AI Planning Guide advertises a framework covering accuracy and reliability, firm-knowledge integration, platform fit, advanced capabilities, vendor partnership, pilot testing, and ROI methods; the full guide is behind an access form, so its underlying scoring rubric is not assessed here.

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Make the decision at the level of a defined workflow

A defensible decision is not simply “use AI” or “avoid AI.” Decide whether a particular tool is suitable for a particular task, document class, and operating boundary. If the pilot shows that reviewers cannot reliably verify outputs, data terms do not meet your requirements, or the workflow lacks adequate auditability or support, do not expand its use on the strength of a polished demonstration. If it meets predefined criteria, introduce it within the tested scope, preserve lawyer responsibility for material judgments, and continue monitoring the system and its terms.

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