Agentic AI refers to artificial intelligence systems that can autonomously plan and execute sequences of actions to complete a goal — not just responding to a single prompt, but operating as an independent agent that decides what steps to take, takes those steps using available tools, evaluates the results, and iterates until the goal is achieved.
The term "agent" in AI carries a specific technical meaning: an agent is an AI system that can perceive its environment, make decisions, take actions (including using external tools like search, code execution, or database queries), and adapt its approach based on the results of those actions. An agent operates with a degree of autonomy that distinguishes it from a simple question-answering model.
In legal practice, agentic AI means an AI system that can receive a high-level legal task — "research and analyze all relevant precedents on force majeure under New York law in the context of supply chain disruption, then draft a client memo with the three strongest arguments for our position" — and execute that task end-to-end, including formulating search queries, retrieving and reading cases, evaluating their relevance and weight, organizing the analysis, and producing a finished memo.
This is qualitatively different from an AI tool that responds to individual prompts. An agentic system is a workflow, not a response.
The practical significance of agentic AI for legal practice is substantial. The traditional legal workflow for research-and-draft tasks requires an attorney or associate to:
- Formulate a research strategy 2. Run searches across multiple databases 3. Read and evaluate retrieved cases 4. Synthesize the analysis 5. Organize the argument structure 6. Draft the memo or brief 7. Revise and refine
In a traditional AI-assisted workflow, the lawyer uses AI to assist with individual steps — AI-assisted search, AI-generated summaries of specific cases, AI-drafted paragraphs based on the lawyer's outline. The lawyer directs each step.
In an agentic AI workflow, the lawyer defines the goal and reviews the final output. The AI directs and executes the intermediate steps. For a complex legal research memo that would take a junior associate a full day, an agentic system may produce a first draft in 20–40 minutes.
The productivity implication is significant. The professional responsibility implication is equally significant.
How It Works
Agentic legal AI systems typically use a ReAct (Reasoning + Acting) or similar architecture that combines:
Planning. Given a high-level task, the agent generates a plan — a sequence of steps it will take to complete the goal. For a research memo, this might be: identify key legal issues → formulate search queries → retrieve relevant cases → extract holdings → evaluate case weight → organize by argument → draft → review and refine.
Tool use. The agent executes each step by calling available tools: a legal database search API, a document retrieval system, a drafting module. Each tool call returns results that inform the next step.
Observation and iteration. After each tool call, the agent evaluates the results and decides whether to proceed to the next step, refine the current step, or revise the plan. If the first research query returns sparse results, the agent may generate additional search queries with different terms.
Memory. The agent maintains context across the entire workflow — remembering what it has found, what it has already drafted, and what the original task required — so that the final output reflects a coherent, integrated analysis rather than a collection of disconnected fragments.
Output. The agent produces a final output — a memo, a contract redline, a research summary — for attorney review.
Tools like Harvey AI and CoCounsel have implemented agentic workflows for legal research, contract analysis, and document drafting. Ironclad implements agentic-style workflows for contract lifecycle management — automatically routing contracts for review, triggering playbook checks, and escalating for human approval at defined thresholds.
Key Considerations for Law Firms
Define what "review" means in an agentic workflow. If the AI executes 15 steps autonomously before the attorney sees any output, the attorney's review must be meaningful — not a rubber stamp on the AI's conclusions. Define review standards for agentic outputs: what must the attorney verify? How deeply must the cited cases be checked? What is the acceptance threshold for agentic draft quality?
Understand error propagation. In a multi-step agentic workflow, an error at step 2 can shape every subsequent step. If the agent mischaracterizes the controlling precedent at the research stage, the entire memo built on that research may be analytically flawed. Errors in agentic systems can be harder to detect than errors in single-step AI outputs because the flawed reasoning is embedded in the architecture of the final document.
Audit trails are essential. Every agentic task should generate a complete log of the agent's decisions: what searches it ran, what cases it retrieved, what it determined about each case, how it organized the argument structure. This audit trail is not just good practice — it is necessary for attorney review, for quality control, and increasingly for professional responsibility compliance documentation.
Human approval gates. For high-stakes tasks — court filings, client advice on material legal questions, transaction documents — design your agentic AI workflows to include mandatory human approval gates at defined points. Do not allow agentic systems to produce client-facing output without attorney review and approval.
Billing and transparency. If AI agents are executing work that would previously have been billed as associate hours, firms face questions about billing disclosure. Some clients now require disclosure of AI use in engagement letters and invoices. Model Rule 1.5 (reasonable fees) questions arise when AI reduces task time dramatically.
Limitations and Risks
Agentic errors compound. The multi-step nature of agentic workflows means that errors at early stages are not isolated — they propagate forward. A research error informs an analysis error which informs a drafting error. The compounding effect can produce final outputs that are systematically flawed in ways that are not immediately obvious from reading the document.
Agentic AI is not yet fully autonomous for complex legal reasoning. Current agentic legal AI tools perform well on well-defined research-and-draft tasks in established areas of law. They perform less reliably on tasks requiring strategic judgment — evaluating litigation risk, advising on novel regulatory questions, assessing litigation settlement value. Agentic capability does not substitute for legal judgment.
Supervisory obligation is unchanged. Under ABA Model Rule 5.3, partners and supervising attorneys are responsible for ensuring that non-attorney work is conducted compatibly with professional obligation standards. The bar has consistently applied this to AI-generated work product: the supervising attorney is responsible, regardless of how autonomously the AI produced the output. "The AI did it" is not a defense to a sanctions motion.
Confidentiality in multi-step workflows. Agentic systems may make multiple external API calls — to search engines, databases, external tools — in the course of executing a task. Each call potentially transmits client information outside the firm. Verify that your agentic AI tools handle confidentiality consistently across all tool calls in the agentic chain, not just the initial user-facing input.