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Short answer: A custom AI agent can help a law firm research, draft, analyze documents, summarize testimony, organize litigation information, handle intake, and answer routine client questions. It does not become a lawyer, and customization does not move professional responsibility to the software vendor. The responsible attorney must set the scope, protect client information, supervise the work, verify every consequential output, and make the final decision.
In practice, a “custom agent” is usually a general-purpose model surrounded by firm instructions, approved sources, workflow steps, permissions, logging, and escalation rules. Ethics opinions generally treat that arrangement as a technology tool or non-lawyer assistant, not as an exemption from the rules of professional conduct. The safest deployment is narrow, source-grounded, access-controlled, and designed so that a lawyer approves advice, filings, deadlines, and client communications.
What is a custom AI agent in a law firm?
A custom agent combines a language model with controls that make it useful for a defined legal workflow. The controls may include a system prompt describing the firm’s standards, retrieval from a matter’s approved documents or authoritative legal databases, role-based permissions, a required sequence of checks, and a human approval gate. Unlike a blank chat window, the agent can be instructed to preserve quotations, show the source of each proposition, refuse work outside its assignment, and route uncertain results to a lawyer.
Customization improves consistency; it does not guarantee accuracy. The underlying model can still misunderstand a fact, rely on an outdated authority, confuse jurisdictions, omit an exception, or generate a plausible but nonexistent citation. The American Bar Association’s 2025 Innovation Trends report describes a broad consensus that AI should be treated in law and the courts “as a clerk or non-lawyer assistant.” That framing is practical: the agent can prepare work, but an attorney retains control and responsibility.
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Common capabilities
- Research support: identify issues, retrieve authorities from approved collections, compare holdings, and produce a draft research memorandum.
- Drafting: create first drafts of pleadings, contracts, letters, discovery requests, chronologies, and internal memoranda from supplied facts and templates.
- Document analysis: extract obligations, dates, defined terms, privileged material, inconsistencies, or missing exhibits from a document set.
- Deposition and record summaries: organize testimony, identify factual themes, and link a summary to transcript pages or document locations.
- Litigation organization: classify claims, defenses, evidence, deadlines, and discovery issues. Some bar guidance also discusses predicting or organizing litigation information; any prediction must be presented as an aid, not a promise.
- Intake and client service: collect preliminary facts, classify a request, answer approved procedural questions, and send a matter to the right human. It must not silently give individualized legal advice beyond its approved scope.
Alabama guidance expressly lists drafting pleadings, summarizing depositions, generating legal research memoranda, predicting litigation outcomes, and responding to client inquiries among the capabilities lawyers encounter. Each use becomes higher risk as it moves from internal preparation toward advice, a filing, a deadline, or a statement to a tribunal.
What professional duties still apply?
ABA Formal Opinion 512, issued July 29, 2024, says lawyers and firms using generative AI must “fully consider their applicable ethical obligations.” The opinion identifies competence, protection of client information, communication with clients, and reasonable fees as central duties. New York City Bar Formal Opinion 2024-5 also addresses conflicts, diligence, advertising and solicitation, compliance with law, supervision of lawyers and non-lawyers, client consultation, candor to tribunals, non-meritorious claims, fee limits, and discrimination.
Competence and diligence
A lawyer should understand what the selected model does, what sources it can reach, its known limitations, and how its settings affect privacy and retention. That does not require the lawyer to become a machine-learning engineer, but it does require enough technical understanding to make an informed choice and to recognize an unreliable result. Diligence means checking the facts, law, dates, calculations, and procedural requirements before relying on an output.
Conflicts and matter boundaries
The firm remains responsible for conflicts screening and for preventing one client’s information from appearing in another client’s work. Matter-level permissions, separate workspaces, tenant isolation, and retrieval filters should be tested rather than assumed. An agent that can search a firm-wide repository needs rules that exclude unrelated or restricted matters.
Supervision
Using an agent is comparable to delegating work to a non-lawyer assistant: the supervising lawyer must define the assignment, provide suitable instructions, review the result, and correct errors. A “human in the loop” label is not enough if the reviewer merely clicks approve. The reviewer needs time, expertise, and access to the underlying authorities and documents.
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Candor, claims, and discrimination
A lawyer may not submit an unverified AI-generated statement, citation, or filing to a court. The same duties that prohibit frivolous claims, misleading statements, or discriminatory treatment apply when software produced the language. Outputs used for hiring, intake, settlement evaluation, or credibility assessments should be tested for disparate errors and reviewed under the firm’s anti-discrimination policies.
Fees and billing
AI use does not automatically justify charging a client for time that was not reasonably spent or for duplicative review. The firm should decide in advance how AI-assisted work is recorded, explain material billing practices when appropriate, and ensure the total fee remains reasonable. A faster workflow can benefit the client; it is not a license to disguise automation as hours worked.
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It can be, but only after the firm understands the data flow and applies safeguards appropriate to the representation. Model Rule 1.6 principles do not change because information is entered electronically. Before sending representation information, determine whether the provider or its subprocessors can access it, disclose it to another user or third party, retain it, use it to train or improve a service, or produce logs that remain after the matter ends.
Questions to answer before activation
- Where are prompts, uploaded files, embeddings, outputs, and backups stored, and for how long?
- Who can view them: the firm, the vendor, support staff, subprocessors, or other tenants?
- Is customer data excluded from model training by contract and by technical configuration?
- Are encryption, tenant isolation, access logging, deletion, incident response, and business-continuity controls documented?
- Can the firm export an audit trail and permanently delete matter data when required?
- Does the service comply with applicable privacy, court, government, health, financial, or cross-border requirements?
Florida Opinion 24-1 emphasizes confidentiality and accurate, competent service. Arizona guidance warns that a public AI product must not receive client information in a way that exposes it to a third party. Public chat products may retain prompts or use them for purposes outside the representation, so confidential inputs should be minimized, protected, or kept in a controlled environment.
Use data minimization
Give the agent only what it needs for the assigned task. A tool that organizes litigation outcomes may not need Social Security numbers, account credentials, medical identifiers, or a client’s full name. Redact direct identifiers, replace them with matter-specific tokens, and keep the re-identification key outside the model environment when feasible. The ABA’s discussion of building AI tools stresses that confidentiality duties apply to electronic information and identifies minimization as a practical safeguard.
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When is informed client consent needed?
There is no single national yes-or-no rule. The answer depends on the tool, the information involved, the representation, the materiality of the risk, and the jurisdictions governing the matter. ABA guidance requires the lawyer to evaluate disclosure and access risks and obtain informed consent when the circumstances or governing rules require it. Boilerplate engagement language is not automatically sufficient.
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Disclosure is not a substitute for security. A client’s agreement does not make an unsafe architecture acceptable, waive a court’s confidentiality order, or permit a lawyer to submit unchecked work.
How do firms prevent invented cases and bad advice?
Hallucinated authorities are a control problem, not merely a prompting problem. The agent should be constrained to sources the firm has approved, and every material proposition should remain traceable to a document, quotation, page, paragraph, docket entry, or database record.
Build verification into the workflow
- Define the assignment. State the jurisdiction, court, date range, matter, permitted sources, prohibited actions, and required output format. Tell the agent to say when it cannot find support.
- Use retrieval where feasible. Prefer current statutes, regulations, rules, opinions, and firm documents that the system can retrieve directly. Do not treat a model’s internal memory as an authority.
- Preserve provenance. Require source links or identifiers, exact quotations for key propositions, and a clear distinction between quoted text, summary, inference, and unanswered question.
- Test representative matters. Use closed cases and difficult examples covering jurisdiction changes, adverse authority, old versions of rules, ambiguous names, adversarial prompts, and missing documents. Record the expected answer and investigate deviations.
- Log prompts and outputs. Maintain an auditable record consistent with confidentiality and retention policies so a reviewer can reconstruct what the system saw and produced.
- Require lawyer approval. A lawyer must verify every legal proposition, citation, deadline, calculation, factual assertion, and filing before it is used. The agent should not autonomously file, send legal advice, contact a represented person, or make a tribunal submission.
For high-risk work, use a two-person review or a second independent source. A citation that looks real must be opened and read; a quotation must be checked against the original; and a deadline must be calculated under the governing rule rather than copied from a generated list.
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Who is liable when the agent makes a mistake?
The lawyer and firm retain professional responsibility for the work they undertake. Custom instructions, a vendor’s accuracy claims, or a contract that calls the product autonomous do not transfer duties of competence, confidentiality, supervision, candor, client communication, conflicts review, or reasonable billing. The ABA’s 2025 report summarizes the prevailing view that responsibility and control remain with the attorney or judge.
Vendor agreements still matter. Allocation of commercial liability, indemnities, warranties, security commitments, audit rights, service levels, and incident notice should be reviewed by counsel. Those provisions may provide a remedy against a supplier, but they do not excuse a missed deadline, an inaccurate filing, or a confidentiality breach in the client relationship. The firm should have an error-reporting process, a way to suspend a faulty workflow, and a plan to notify affected clients or tribunals when professional duties require correction.
Must a client be told that an AI agent is being used?
Often the answer turns on materiality. If the agent only performs a low-risk administrative task without exposing client information or affecting the representation, a separate disclosure may not be necessary under every rule. If it analyzes confidential facts, drafts advice, changes the cost or speed of the service, or creates a significant risk a reasonable client would want to understand, discuss the use and obtain consent where required.
Use plain language rather than suggesting that a machine is making legal decisions. Describe the human review, the categories of data involved, the principal privacy risks, and the client’s options. Check any engagement terms, protective orders, court rules, and jurisdiction-specific guidance before deployment.
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How do state rules differ?
Formal guidance is developing unevenly. The American Bar Association Center for Innovation reported in 2025 that 24 states and territories had official AI ethics opinions, policies, or guidance as of July 2025, and that 47% of states plus the District of Columbia had adopted formal AI guidance. Those figures are a snapshot, not a uniform national rule.
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For each deployment, check the jurisdiction governing the matter, the lawyer’s admission, the client’s location, the court’s rules, and applicable privacy or sector requirements. A multistate firm should maintain a jurisdiction matrix showing disclosure expectations, confidentiality guidance, tribunal requirements, and any restrictions on automated communications. Recheck it when a matter moves courts or when a bar issues new guidance.
Procurement checklist for a small or midsize firm
Compare products and implementation approaches on controls, not on a model’s marketing claims. A useful evaluation includes:
| Area | Questions to document |
|---|---|
| Confidentiality and data control | Are prompts and files encrypted, isolated by tenant and matter, excluded from training, logged, retained only as needed, and deletable? |
| Source quality | Can the agent retrieve current, authoritative sources and preserve quotations, page references, links, and version dates? |
| Permissions and conflicts | Can access be limited by user, role, client, matter, document class, and ethical wall? |
| Human escalation | Can the system stop and require approval for advice, filings, deadlines, client messages, and tribunal submissions? |
| Auditability | Are prompts, retrieved sources, outputs, approvals, edits, and model changes recorded for an appropriate period? |
| Integration | Does it connect safely to the firm’s document-management, email, billing, and case systems without creating uncontrolled copies? |
| Evaluation | Can the firm test jurisdiction, dates, citation accuracy, confidential-data handling, adversarial prompts, and prohibited advice before launch? |
| Support and incident response | Does the contract specify security contacts, breach notification, service support, export, deletion, and continuity procedures? |
| Governance | Are training, supervision, change management, error reporting, client disclosure, consent, and billing policies assigned to named people? |
A controlled rollout that lawyers can supervise
- Choose a bounded pilot. Start with an internal, reversible task such as clause extraction or document summarization. Avoid autonomous client advice and court filings.
- Map the data. List every input, retrieval source, model provider, subprocessor, storage location, retention period, export path, and deletion step.
- Set acceptance tests. Include normal and edge cases, incorrect premises, conflicting authorities, stale rules, prompt injection in uploaded files, and attempts to cross matter boundaries.
- Write the operating policy. Specify permitted uses, prohibited uses, reviewer qualifications, approval gates, logging, escalation, incident handling, and when client notice or consent is required.
- Train users. Teach lawyers and staff not to paste unnecessary identifiers, how to inspect sources, how to report an error, and when to stop using the system.
- Review before expansion. Analyze errors and near misses, update prompts and retrieval sources, confirm that permissions still work, and obtain management approval before adding a new matter type or external communication.
Which approach is appropriate?
There is no universally “secure enough” product. A small firm should choose the least complex option that meets its confidentiality, source, review, and audit requirements, then document why it is suitable for the matters handled.
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| Approach | Strengths | Risks and best fit |
|---|---|---|
| Public general-purpose chatbot | Quick experimentation and broad language ability. | Unclear retention, third-party access, and training settings can make confidential use inappropriate. Restrict to non-confidential material unless the provider’s controls and terms have been verified. |
| Enterprise legal-AI platform | Stronger tenant controls, legal-source retrieval, permissions, logging, and vendor support may be available. | Still requires contract review, configuration, testing, human review, and jurisdiction-specific governance. |
| Private or firm-controlled deployment | Maximum control over data flows, access, retention, and integrations. | Higher implementation and maintenance burden; the firm must operate security, updates, evaluations, and support competently. |
| Custom workflow on an approved model | Can match a narrow process, templates, escalation rules, and document system. | Customization can hide failure modes if testing, monitoring, and source controls are weak. Treat it as supervised assistance, not autonomous practice. |
The decision should be made by the responsible lawyers and technology or security staff together. A product that cannot explain where matter data goes, who can access it, how it is deleted, or how sources are shown is not ready for confidential legal work, regardless of how polished its answers appear.
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