On Agentic AI for Legal Teams

Introduction

AI agents represent a meaningful shift in how legal teams use technology. Traditional generative AI responds to prompts by producing content. Agents go further. They act.

By combining language models with goals, tools, and memory, agents execute multi-step workflows across connected systems. For legal teams, this expands automation beyond isolated tasks into coordinated processes.

Agents can review intake requests, extract key facts, validate deadlines against matter management systems, propose task assignments, and draft communications. Each step informs the next. This is not conversational assistance. It is software operating toward defined outcomes.

That capability creates leverage by reducing coordination work and allowing professionals to focus on judgment-intensive decisions. It also introduces new risk. Because agents operate across systems, weak permissions, unclear escalation logic, or poor controls can propagate errors quickly.

This article explains how agentic systems work, where they are already being used in legal operations, and how to adopt them responsibly with a focus on security and governance.

Understanding the Shift

Most legal teams encounter generative AI through tools that summarize contracts, assist with research, or support drafting. These systems follow a simple pattern: prompt in, response out.

Agentic AI operates differently. Instead of producing a single response, an agent pursues a goal across multiple steps. It can call tools, access systems, retain context, and decide what to do next based on results along the way.

The difference is clear when comparing a chatbot asked to summarize a contract with an agent instructed to process contract intake. The chatbot produces a summary. The agent may classify the request, select the appropriate template, compare terms against standards, flag deviations, route the matter to the correct reviewer, and log the action.

This shift matters because it changes both capability and risk. Agents manage workflows, not just tasks. They move across systems, not just within one. Governance models must reflect that expanded scope.

What Makes an Agent

An AI agent consists of four core elements: a language model, tools, memory, and goals.

The language model provides reasoning. It interprets instructions, plans steps, and generates outputs such as summaries or drafts. While this is the same underlying technology used in chatbots, here it functions as part of a larger system.

Tools enable action. They allow agents to search databases, read documents, send messages, update records, and interact with enterprise platforms. Each tool is an access point that requires authorization and logging.

Memory provides continuity. It allows agents to track prior actions, maintain context, and avoid repeating work. Memory may persist only within a task or across sessions, raising questions about retention and handling of sensitive information.

Goals direct behavior. Unlike chatbots, which react to prompts, agents work toward defined objectives. Who sets those objectives and how they are constrained are core governance concerns.

How Agents Operate

Agents operate in iterative loops. At each step, the agent assesses its current state, determines the next action, executes a tool, evaluates the result, and decides whether to continue.

This loop continues until the objective is met or a stopping condition applies.

For example, an agent preparing a contract review package may identify the agreement type, retrieve the relevant playbook, compare terms, flag deviations, draft a summary, route the materials, and record the activity. Each decision depends on what the agent learned in the prior step.

Agents are adaptive rather than scripted. They adjust based on inputs and outcomes. This flexibility distinguishes them from traditional automation and explains why governance must emphasize oversight, constraints, and accountability rather than static rules alone.

Agent Architectures and Governance Implications

Single-agent systems assign all steps to one agent. These systems work well for focused workflows such as intake processing or document review. Governance is relatively straightforward because permissions and audit trails are centralized.

Multi-agent systems divide work among specialized agents, coordinated through an orchestration layer. This mirrors how legal teams operate but introduces governance challenges around data handoffs, accountability, and agent-to-agent communication.

Human-in-the-loop designs embed review at defined decision points. Agents handle routine processing but pause before taking consequential actions. These checkpoints define acceptable autonomy. Too many reduce efficiency. Too few increase exposure to error.

Practical Use Cases in Legal

Legal teams are already deploying agents across several workflow categories, each with distinct governance requirements.

Contract Intake and Triage

Agents can classify submissions, extract metadata, validate completeness, and route matters based on risk or value. Governance focuses on routing logic and access control.

Playbook Compliance Review

Agents can compare agreements against internal standards and flag deviations. Version control is critical to avoid applying outdated positions.

Due Diligence Coordination

Agents can track requests, monitor data rooms, and route documents to subject matter experts. Strong access controls and audit trails are essential.

Legal Research Assembly

Agents can retrieve authorities and assemble structured research outputs. Governance must address cost controls, citation quality, and currency.

Matter Management Updates

Agents can update systems of record based on communications and documents. Write access requires strict safeguards to prevent inaccurate records.

Compliance Monitoring

Agents can track regulatory updates and contractual obligations, generating alerts and routing tasks with documented human oversight.

Security Architecture for Agent Deployment

Agent deployments extend across systems in ways that traditional security models may not anticipate.

Agents should have distinct identities, narrowly scoped permissions, and clear audit trails. Network segmentation and API gateways limit exposure and provide control points for monitoring.

Data protection depends on classification, encryption, and minimization. Not all data should be accessible to agents, and retention should be limited.

When third-party services are involved, teams must assess where processing occurs, who can access logs, and how data is retained. Sensitive workflows may justify on-premises deployment.

Governance Frameworks for Agent Operations

Security addresses access. Governance addresses accountability, quality, compliance, and oversight.

Policies should define acceptable use, approval thresholds, and prohibited applications. Standards translate policy into measurable requirements.

Monitoring should track performance, quality, compliance, and escalation behavior. Incident response plans should address detection, containment, and remediation of agent errors.

Training ensures stakeholders understand what agents do, where limits apply, and how accountability is assigned. Agents support work. Humans remain responsible.

Risk Management Considerations

Agents should operate with minimum necessary permissions enforced technically, not just documented.

Escalation paths must reflect value, risk, and uncertainty and route matters to appropriate expertise.Audit trails should allow reconstruction of actions and decisions. Agents are not accountable entities.

Stopping conditions prevent runaway execution. Ongoing testing detects model drift and failure modes.Vendor dependencies require contingency planning for outages, pricing changes, or service degradation.

Implementation Guidance

Effective adoption progresses from simple to complex.

Start by mapping workflows and identifying friction points. Begin with bounded tasks that have clear inputs and outputs. Maintain human review during early deployment.Measure outcomes such as time savings, error rates, rework, and escalation frequency. Use evidence to refine configurations and expand scope gradually.

Agent deployment is an ongoing discipline, not a one-time implementation.

Concluding Thoughts

Agentic AI changes what automation can accomplish in legal work. Agents coordinate, maintain context, and execute multi-step processes across systems.

That power requires deliberate governance. Clear permissions, escalation logic, accountability, and security controls enable scale without sacrificing trust.

Governance does not slow adoption. It enables sustainable adoption. When done well, agents extend human judgment rather than replacing it.

Glossary

AI Agent

Software that combines a language model with goals, tools, and memory to execute multi-step tasks autonomously within defined limits.

Language Model

The reasoning component that interprets instructions, plans actions, and generates outputs such as summaries, drafts, or classifications.

Tool Use

An agent’s ability to interact with external systems, including document repositories, databases, email, and enterprise applications.

Agent Memory

The mechanism that allows an agent to retain context about prior actions and decisions during or across workflows.

Human-in-the-Loop

A workflow design where agents pause at defined points for human review before taking consequential actions.

Permission Boundary

The enforced scope of systems, data, and actions an agent is authorized to access or perform.

Audit Trail

A detailed record of agent actions and tool usage that supports review, accountability, and incident response.

Frequently Asked Questions

How is an agent different from a chatbot?

A chatbot responds to a single prompt. An agent pursues a goal across multiple steps, uses tools, retains context, and decides what to do next.

What legal workflows benefit most from agents?

Work that involves coordination rather than judgment, such as intake triage, document routing, compliance tracking, and structured reviews.

Do agents operate without human oversight?

They can, but responsible deployments define when agents must pause and escalate, especially for high-risk or high-value actions.

Are agents secure by default?

No. Security depends on how permissions, system access, monitoring, and logging are designed and enforced.

Who is accountable for agent actions?

The organization remains accountable. Agents execute instructions but do not assume responsibility.

Can agents access sensitive or privileged information?

Only when explicitly permitted. Many teams restrict agents from handling certain data types by design.

Where should teams begin with agent adoption?

Start with narrow, well-defined tasks, maintain human review early, and expand scope based on observed performance.