Best AI Agents for Legal Teams in 2026: 11 Tools for In-House Counsel and Law Firms

Updated August 2026. If you run legal for a company or a firm, Harvey and Thomson Reuters CoCounsel lead broad research-and-drafting depth, LexisNexis Protégé is the strongest pick if you already run on Lexis content, Ironclad's Jurist and Legora's Agent lead contract lifecycle work end to end, and Clearbrief is the only tool here built specifically to catch a fabricated citation before it reaches a judge. This guide ranks 11 AI agents for legal teams, evaluated on whether they genuinely plan and execute multi-step work on their own, with real vendor-confirmed pricing where it exists and clearly labeled reported figures where it doesn't.

An agent is not the same purchase as an AI tool, and that distinction matters more in law than almost anywhere else. A tool like the ones in our best AI tools for lawyers and best AI tools for paralegals guides assists a person who stays in the loop on every step: draft this clause, summarize this deposition, find this case. An agent plans a multi-step task, pulls documents and calls other systems on its own, and returns finished or near-finished work for a human to approve (see what is an AI agent for the plan-act-observe loop every product below runs some version of). This guide treats two questions as first-class, not footnotes: whether the agent's output is actually grounded in real authority, and what happens to the confidential material it touches along the way. One more thing up front: this article is informational, not legal advice and not bar guidance. Verify anything below against the vendor's current terms and your own jurisdiction's ethics rules before you rely on it.

Key Facts

• A public database tracking court rulings on AI-generated fabrications, maintained by researcher Damien Charlotin at HEC Paris, had logged well over 1,500 cases worldwide by mid-2026, more than 1,000 of them in US courts, and the count grows most weeks (Damien Charlotin, AI Hallucination Cases Database).
• A 2024 Stanford RegLab study hand-scored 202 real legal queries and found Lexis+ AI hallucinated 17% of the time and Westlaw AI-Assisted Research hallucinated 33% of the time, despite both vendors marketing the tools as effectively hallucination-free (Stanford RegLab).
• 41% of law firms now report active generative AI use, up from 28% in 2025, yet 90% of legal dollars still flow through standard hourly billing, per Thomson Reuters' own Legal Tracker data (Thomson Reuters Institute).
• More than half (52%) of in-house counsel now actively use generative AI, more than double the 23% who did in 2024, and 64% expect to depend less on outside counsel as their own AI capability matures (Association of Corporate Counsel).
• 69% of legal professionals personally use general-purpose AI tools for work, but only 46% of firms have formally rolled AI out firm-wide, 43% have no AI policy and no plan to write one, and just 9% have a written policy anyone actually enforces (8am 2026 Legal Industry Report).

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Quick Comparison Table

One more name before the list: Robin AI isn't ranked below, and that's deliberate. Robin built a real contract-review and negotiation agent and served roughly 13 Fortune 500 customers at its peak, but a planned $50 million funding round collapsed in October 2025, the company cut about a third of its staff, and by early 2026 it had wound down as an independent business. Its managed-services and human-in-the-loop review team was acquired by the law firm Scissero in December 2025, and its technology and engineering team was acqui-hired by Microsoft in January 2026 to strengthen legal AI features inside Word (Sifted). There's nothing left to buy under the Robin AI name. It's worth knowing the story anyway: vendor stability is a real evaluation criterion in a category this young, not just a footnote, which is why it gets its own line in the buying-mistakes table further down.

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1. Harvey - The Broadest Agentic Coverage Across Practice Areas

Harvey pitches itself plainly: "Harvey Agents execute legal work end-to-end, so you can focus on what only lawyers can do." In practice that means purpose-built agents for contract intelligence, bulk document analysis and storage through Vault, and research across legal, regulatory, and tax domains, plus workflows that hand off across a deal team instead of staying inside one lawyer's session. It's the platform most large firms mean when they say they're piloting an AI agent.

Pricing draws the most criticism. Harvey doesn't publish rates, every quote runs through a sales conversation, and reported figures vary enormously: some trackers cite $100 to $200 per seat monthly at AmLaw 100 scale, others put mid-market quotes at $1,000 to $2,000 per seat monthly with 20-to-25-seat minimums, putting a small deployment's entry cost near $300,000 a year (reported, not vendor-confirmed).

Pricing: Custom, quote-based; no public rate card. Reported ranges run from $100-200/seat/mo at AmLaw 100 scale to $1,000-2,000+/seat/mo mid-market (reported, not vendor-confirmed); treat any online figure as directional.

Best for: Large firms and enterprise legal departments running a dedicated AI program across research, contracts, and due diligence.

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2. Thomson Reuters CoCounsel - Research Grounded in Westlaw

CoCounsel's pitch is that it "reasons from authoritative Westlaw primary law, trusted Practical Law guidance, and your organization's own knowledge" instead of the open web. Its Deep Research capability runs what Thomson Reuters describes as a multistep plan, the kind of research workflow a senior associate would run, and shows its reasoning steps so a lawyer can redirect it mid-task rather than only reviewing a finished answer.

The catch in 2026 is that CoCounsel isn't sold standalone. It ships bundled with a Westlaw subscription, so your real monthly cost is Westlaw plus the CoCounsel layer on top, and neither product page lists a dollar figure. Third-party cost trackers report a wide spread, roughly $100 to over $600 per user monthly depending on tier and Westlaw configuration (reported, not vendor-confirmed).

Pricing: Custom; bundled as a Westlaw add-on. Reported figures range roughly $100-$600+/user/month depending on Westlaw tier (reported, not vendor-confirmed).

Best for: Firms and legal departments already running Westlaw who want research and drafting handled by the same authoritative source.

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3. LexisNexis Protégé - Hundreds of Prebuilt Agentic Workflows

Relaunched February 24, 2026 as "Lexis+ with Protégé" (formerly Lexis+ AI), this is LexisNexis's agentic layer: hundreds of prebuilt, configurable workflows for litigation, transactional, and everyday legal tasks, grounded in LexisNexis's own content plus a firm's internal knowledge, and available alongside enhanced general models from Anthropic, Google, and OpenAI rather than one house model. A May 2026 update added agentic skills, shared "workrooms," and customer-held encryption keys, a concrete control worth asking every other vendor on this list whether they match.

Pricing is entirely custom, and third-party trackers describe it so differently (estimates for the same product span roughly $125 to $500 per seat monthly) that it's genuinely opaque even by this category's low standard. LexisNexis's own pricing page states only that cost depends on organization size, capability scope, and content access.

Pricing: Custom, add-on to a Lexis+ subscription; no published rate card. Reported estimates span roughly $125-$500/seat/month depending on source (reported, not vendor-confirmed).

Best for: Firms and legal departments already standardized on Lexis content who want agentic workflows with strong data controls.

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4. Legora - One Agent Across Matter Types, Moving to Usage-Based Pricing

Legora markets its "aOS," an "agentic operating system for legal work," built around an Agent that "plans, executes, reviews and delivers" across M&A, litigation, banking, tax, and insurance work, plus a Monitors feature that continuously scans regulatory change for a client's business. It's the platform most often positioned as Harvey's direct challenger, and it had raised over 500 million euros at a reported $5.5 billion valuation by 2026.

The pricing story here is genuinely newsworthy: in 2026 Legora moved its new Agent Pro tier to consumption-based, credit-metered pricing, tracked per matter with real-time dashboards and spend controls, arguing that seat-based licensing doesn't fit how agentic work actually gets used. The original Legora Agent stays free to existing customers. Historical flat-fee reports put a seat around $3,000 a year with a 10-seat minimum (reported, not vendor-confirmed); Legora publishes no rate card for either model.

Pricing: No public rate card. Agent Pro moved to consumption/credit-based pricing in 2026; the legacy Agent stays free to existing customers. Historical reports cited roughly $3,000/seat/year with a 10-seat minimum (reported, not vendor-confirmed).

Best for: Mid-size to large firms wanting one agent that runs full matters across multiple practice areas.

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5. Ironclad AI (Jurist) - A Contract Review Agent Inside Your CLM

Ironclad calls Jurist "the agentic AI contract partner purpose-built for legal contract review," built into its existing contract lifecycle management platform rather than sold as a separate product. That matters for scope: Jurist's value is tied to running inside Ironclad's workflow engine, Smart Import, and repository, so you're evaluating a CLM with an agent built in, not a standalone review tool you bolt onto whatever system you already run.

Ironclad doesn't publish pricing; its site asks buyers to "design your digital contracting journey" through a sales conversation. Buyer-side trackers report first-year quotes commonly landing between $40,000 and $120,000 for small-to-mid-market deployments and $200,000 or more at enterprise scale, noting that base licensing often covers only 40-50% of true first-year spend once implementation is added (reported, not vendor-confirmed).

Pricing: Custom, quote-based. Reported first-year quotes commonly $40,000-$120,000 (small/mid-market) to $200,000+ (enterprise), with implementation adding significantly more (reported, not vendor-confirmed).

Best for: Legal departments consolidating contract lifecycle management and AI-assisted review into one platform.

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6. Workday CLM, Powered by Evisort - Contract Intelligence Inside Your HR and Finance Data

Workday agreed to acquire Evisort on September 17, 2024, and now sells its technology as Workday Contract Intelligence and Workday Contract Lifecycle Management. The pitch is straightforward for existing Workday customers: contract terms surfaced next to the HR, procurement, and financial data you already run in Workday, natural-language search over your contract set, and automated drafting, redlining, and risk assessment that Workday says can cut approval cycles from months to hours.

The real constraint is ecosystem fit. This isn't a product you evaluate on its own; it's a module inside a Workday deployment, and pricing is quoted alongside, or as part of, your broader Workday contract. Buyer-side estimates for smaller standalone deployments report $30,000 to $60,000 annually, though most enterprise buyers will see it priced inside a larger Workday agreement (reported, not vendor-confirmed).

Pricing: Custom, quote-based, typically bundled into a broader Workday agreement. Reported standalone estimates for smaller deployments run $30,000-$60,000/year (reported, not vendor-confirmed).

Best for: Companies already running Workday HCM or Financials that want contract intelligence in the same data model.

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7. Spellbook - Autonomous Contract Management, Word-Native

Spellbook built its reputation as a Word add-in for clause suggestions and redlining, then moved further into agent territory: it launched Spellbook Associate, an early legal AI agent, in August 2024, and on June 30, 2026 released Autonomous Contract Management (ACM) to early-access customers, an agentic system that runs intake, review, and insight as one pipeline. ACM pulls documents in from systems like Outlook or Slack, reviews and redlines them against your standards, files signed contracts automatically, and flags upcoming renewals, a genuinely end-to-end loop rather than a drafting assistant waiting on the next command.

Spellbook doesn't publish pricing; its site states plainly that cost "is structured around the number of team members on your license," scaling from individuals to global legal departments, with every quote requiring a demo. Third-party trackers report meaningful price increases through late 2025 into 2026, though the specific figures circulating online vary too much to repeat reliably here.

Pricing: Custom, scales by seat count; no public rate card. Third-party figures vary too widely to repeat reliably; get a current quote.

Best for: Mid-market legal teams that want a Word-native agent running the full contract lifecycle, not just drafting help.

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8. Luminance - Enterprise Contract Analysis, Investigation, and Compliance

Luminance, founded by mathematicians in 2015, positions itself as "Legal-Grade™ AI" running a "Mixture of Experts" approach with what it calls "probabilistic consensus" for accuracy. Its six functions span the contract and compliance lifecycle: Draft, Negotiate (AI-assisted review inside Word), Analyze (portfolio-wide contract visibility), Comply (tracking new compliance requirements as they land), Investigate (litigation, arbitration, and investigation support), and Collaborate (end-to-end workflow automation between legal and the rest of the business).

There's no self-serve tier and no public pricing; Luminance sells exclusively through a demo-gated enterprise motion. Third-party analyses put a realistic first-year deployment for a mid-size rollout in the low-to-mid six figures once implementation, typically 20-50% of the license fee, is added (reported, not vendor-confirmed).

Pricing: Custom, enterprise-only. Third-party estimates suggest low-to-mid six figures annually for a mid-size deployment, before implementation (reported, not vendor-confirmed).

Best for: Large legal departments or firms consolidating contract review, investigation, and compliance monitoring into one platform.

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9. Clearbrief - Purpose-Built to Catch a Fabricated Citation

Clearbrief is the one tool on this list built specifically around the failure mode the rest of this guide spends so much time on. Working inside Microsoft Word, it backs every factual claim in a brief with a clickable, verifiable citation, builds tables of authorities and evidence tables automatically, generates chronologies and case summaries from the record, and produces cross-examination outlines with citations attached in real time. For a litigation team, it's less a drafting agent and more a verification layer that sits between a draft and a filing.

Clearbrief is also the only vendor on this entire list that publishes an actual price on its own website, worth noting given how opaque the rest of this category is.

Pricing: Solo $300/user/month; Enterprise Unlimited custom, with volume and multi-year discounts available. Published on Clearbrief's own site, a rarity in this category.

Best for: Litigation teams and solo litigators who want every citation in a brief verified before it reaches a judge.

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10. DeepJudge - Institutional Knowledge as Agent Infrastructure

DeepJudge, founded by former Google search engineers, isn't a drafting or review agent itself; it's search and knowledge infrastructure that other agents can run on. It indexes a firm's institutional knowledge across SharePoint, OneDrive, document management systems, and email, then layers "AI Workflows" on top that DeepJudge says can "build, deploy, orchestrate, and govern AI agents" for multi-step tasks like cross-matter research and negotiation intelligence. In 2026 it partnered with Harvey specifically to ground Harvey's agents in a firm's own institutional knowledge rather than public data alone, a sign of where this category is heading: agent products increasingly plug into a shared knowledge layer instead of each building their own.

No pricing is published anywhere, and unlike most others on this list, third-party trackers don't report reliable figures either; DeepJudge is early enough in its go-to-market that a demo is the only way to get a number.

Pricing: Not published; no reliable third-party estimate exists either. Contact for a demo and quote.

Best for: Larger firms that want their own institutional knowledge grounding the agents they buy, including Harvey.

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11. Eve - Intake Through Discovery for Plaintiff Firms

Eve, built by Butler Labs, is scoped narrowly and deliberately: it's an agent platform for the plaintiff personal injury case lifecycle specifically, covering case intake and evaluation, medical record overviews, drafting, demand letters, and propounding and responding to discovery. That's a genuinely different buyer than the rest of this list; Eve isn't built for in-house legal departments or transactional practice, it's built for plaintiff firms running high case volume who need intake-through-discovery work handled at scale. More than 1,200 firms reportedly use it, and the company raised a $103 million round at a reported $1 billion-plus valuation.

Pricing isn't published, and given how narrow the buyer base is, third-party pricing trackers offer little reliable signal beyond generic legal-AI market ranges rather than Eve-specific figures. Budget for a sales conversation.

Pricing: Not published; no reliable Eve-specific third-party estimate found. Contact for a quote.

Best for: Plaintiff personal injury firms running high case volume who want intake through discovery handled by one agent.

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How to Choose: Decision Framework

Use the shortlist below only after defining the legal task, trusted source base, deployment scale, confidentiality controls, and review gate.

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Citation and Hallucination Risk: What Actually Goes Wrong, and How to Verify It

Every agent on this list will eventually generate a citation, a quote, or a factual claim that isn't real. That's not a hypothetical risk in law; it's a documented, growing body of case law. The AI Hallucination Cases database, maintained by Damien Charlotin at HEC Paris, had tracked well over 1,500 court rulings worldwide addressing AI-generated fabrications by mid-2026, more than 1,000 of them in US courts, with the count growing most weeks (Damien Charlotin).

The landmark case is still the first one. In June 2023, New York attorney Steven Schwartz asked ChatGPT for case law supporting a personal injury claim and filed a brief citing six cases that didn't exist, complete with invented docket numbers. Judge P. Kevin Castel sanctioned Schwartz, his co-counsel, and their firm a combined $5,000, and the case was referred for further review. Penalties since have gotten sharper, not softer. In December 2025, a federal magistrate judge in Oregon fined two attorneys, Stephen Brigandi and Tim Murphy, a combined roughly $110,000 after their briefs in a family dispute over a winery cited 15 nonexistent cases and 8 fabricated quotations; when the defense flagged the errors, the lead attorney refiled amended briefs that still contained misstatements, and the judge dismissed the case with prejudice, calling it an outlier only in scale, not in kind. And in 2026, an Illinois appellate court fined attorney Mason Cole $15,000, a flat $1,500 for every fabricated citation in his brief, and referred him to the state's attorney discipline commission, after he told the court he had used ChatGPT and thought he had cross-checked the results against Lexis but missed the errors anyway (ABA Journal).

Vendor claims about accuracy deserve the same scrutiny as a citation an agent hands you. In 2024, a Stanford RegLab team led by Varun Magesh and Daniel Ho ran the first preregistered, hand-scored test of commercial legal AI research tools; on 202 real legal queries, Lexis+ AI hallucinated 17% of the time and Westlaw AI-Assisted Research hallucinated 33% of the time, despite both vendors marketing retrieval-grounded, citation-linked answers as effectively hallucination-free. The researchers' conclusion was blunt: "providers' claims are overstated" (Stanford RegLab). That study tested an earlier generation of these products, not the current CoCounsel Legal or Lexis+ with Protégé builds, and both vendors have since layered in more grounding and citation-linking. But the underlying finding hasn't expired: retrieval grounding cuts hallucination, it doesn't eliminate it, and no vendor's internal benchmark should be the last check before a filing. The same discipline applies to AI research agents generally, not only legal-specific ones.

What actually holds up in practice: treat every agent-generated citation as a draft citation until a human, or a dedicated verification pass like Clearbrief's, has opened the source and confirmed it says what the agent claims. If your workflow includes heavy discovery or due diligence review, the build-side AI document processing agent blueprint is a useful reference for where a human confidence-check gate belongs in that kind of pipeline, and firms running agents past the pilot stage are increasingly pairing them with dedicated AI agent observability tools built to trace exactly which source an agent's output actually came from.

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Privilege and Confidentiality: Where the Data Goes

An AI agent working on a contract or a discovery set isn't just a security question, it's a professional-responsibility question, and the two get confused constantly. The American Bar Association's Formal Opinion 512, issued July 29, 2024, the first national ethics guidance on generative AI, is explicit that using a GAI tool touches at least six duties at once: competence (Model Rule 1.1), confidentiality (Rule 1.6), communication with the client (Rule 1.4), candor to the tribunal (Rules 3.1 and 3.3), supervision of subordinates and nonlawyers (Rules 5.1 and 5.3), and reasonable fees (ABA Formal Opinion 512 summary, UNC Law Library). Confidentiality is the one that trips up agent deployments specifically, because an agent doesn't just answer a question, it moves data: pulling a contract from a repository, pasting clauses into a comparison, sending a draft out for review.

State guidance has started filling in the specifics. The Texas State Bar's Professional Ethics Committee, in Opinion 705 (February 2025), told attorneys not to input confidential client information into unvetted or public AI tools, to vet a vendor's security and data-handling terms before adopting it, and, depending on the tool, to consider informing the client and getting consent (Texas Bar Blog). The question worth asking every vendor on this list, one a generic security questionnaire won't surface, is whether the tool trains its models on your inputs by default, what you'd have to do to opt out, and how long your firm's or client's data sits in the vendor's systems after a matter closes. LexisNexis added customer-held encryption keys to Protégé in its May 2026 update, a concrete control worth asking every other vendor here whether they offer too.

None of this is a one-time check. The build-side guides on AI agent data privacy and AI agent compliance go deeper on audit trails and data residency if your firm or department is standing up its own agent rather than buying one off this list, and the same questions apply either way.

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Self-Serve vs. Firm-Wide: Two Very Different Purchases

Almost nothing on this list is a credit-card purchase. Of the 11 tools here, Clearbrief is the only one with a published price on its own website; everyone else routes you to a demo or a sales call, and reported figures for the same vendor can vary by a factor of four or five depending on which third-party tracker you trust, itself a signal of how little price transparency this category has right now. That's a real contrast with more mature agent categories: general-purpose AI agent platforms increasingly offer a self-serve tier a single team can start on this week, while legal agents mostly assume a firm-wide or department-wide procurement motion from day one, closer to how enterprise AI agent platforms get bought than how an individual buys software.

That split maps to two different buying motions worth naming honestly. A solo attorney or a two-person in-house team piloting Spellbook or Clearbrief is buying a tool the way anyone evaluates SaaS: trial it, check the output, expand seats if it earns them. A general counsel evaluating Harvey, Legora, or Luminance for firm-wide rollout is running a security review before a single associate touches it: data processing agreements, SSO, audit logging, a defined AI-use policy, and usually IT or InfoSec sign-off alongside legal's own. Skipping that step is exactly how the governance gap shows up in the data: 69% of legal professionals now use general-purpose AI tools like ChatGPT or Claude for work on their own, more than double the year before, but only 46% of firms have formally rolled out AI tools firm-wide, 43% have no AI policy and no plan to write one, and just 9% have a written policy anyone actually enforces (8am 2026 Legal Industry Report). An agent that autonomously pulls documents and drafts client-facing work is a different risk profile than a chatbot open in a browser tab, and buying it through the same ad hoc, nobody-approved-it path that got ChatGPT onto associates' laptops is how a firm ends up as the next entry in the sanctions table above.

In-house legal is moving faster than outside counsel on this front. More than half (52%) of in-house counsel now actively use generative AI, more than double the 23% who did in 2024, and 64% expect to lean on outside counsel less as their own AI capability matures (Association of Corporate Counsel). If you're standing up an intake or contract-review agent to handle that shift internally rather than buying one off this list, the build-side AI contract review agent blueprint is a useful reference for where the human approval gate belongs before anything reaches a counterparty.

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Billable-Hour Economics: What Happens to Leverage

The billing model hasn't caught up to what these agents actually do, and that gap is now the industry's central tension. Law firm tech and knowledge-management spending grew roughly 9.7% and 10.5% in 2025, likely the fastest real growth the legal industry has ever seen, and 41% of firms now report active generative AI use, up from 28% the year before. Yet 90% of legal dollars still flow through standard hourly billing, according to Thomson Reuters' own Legal Tracker data (Thomson Reuters Institute). Work that once took ten hours of research, review, and drafting can now often get done in one, and a firm charging by the hour has an obvious incentive problem: bill honestly for the AI-compressed hour and revenue falls, or don't, and invite exactly the client pushback that's already surfacing as general counsel compare their own AI-driven efficiency to what outside counsel still charges for.

That tension runs straight through the associate leverage model. Firms have trained junior associates for decades by handing them the first pass at contract review, document summarization, and first-draft research, then billing partner-supervised time on top of it. An agent that does a competent first pass on that same work doesn't just compress hours; it removes a rung new associates used to climb toward becoming senior ones, and no firm has fully solved what replaces that training path. Pricing is already bending under the pressure: Legora shifted its Agent Pro tier to consumption-based, credit-metered pricing in 2026, arguing plainly that "the unit economics of legal services will transform: from hours billed and seats licensed, to outcomes delivered" (Legora). Whether that model spreads or stays one vendor's bet, it's a real signal that per-seat, per-hour pricing and a genuinely agentic product are starting to pull against each other.

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Legal AI Agent Buying Mistakes to Avoid

Most legal AI agent rollouts fail for reasons that have nothing to do with model quality.

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What to Do Next

Don't roll anything on this list out firm-wide off the back of a single demo. Pick two tools that fit your actual deployment size: a self-serve option like Clearbrief or Spellbook for a small team, or a firm-wide platform like Harvey, Legora, or your existing Westlaw or Lexis relationship for a larger one, and run a 30-day pilot on real, already-decided matters where a partner or senior counsel checks every citation and every data-handling claim by hand. Put a written AI-use policy in place before rollout, not after; the data above is clear that the firms getting into trouble aren't the ones using AI agents, they're the ones whose people were already using them without anyone approving it first.