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Building Effective Agents: A Lawyer's Guide to LLM Systems

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If you’re looking to adopt AI into your organization, but are unsure what the hype about agents are, we’re here to help. The most successful implementations of AI agents don’t rely on complex technical frameworks but instead use straightforward, flexible approaches. Here's how this applies to lawyers and their work.

What Are Agents?

Agents are like highly skilled legal assistants powered by AI. They can handle tasks either by following strict workflows or by making dynamic, real-time decisions to adapt to the task at hand.

A workflow, in this sense, is a system where tasks are handled step-by-step using pre-designed processes. For example, identifying and flagging missing terms in a contract could follow a predictable pattern. An agent, however, is far more dynamic. It can determine how to approach a task based on the information it receives, such as deciding whether to rewrite a clause entirely or provide a detailed critique for a negotiator. This flexibility makes agents especially valuable for open-ended or unfamiliar legal scenarios.

When Should Lawyers Use Agents?

Agents trade latency and cost for flexibility and adaptability. While simpler AI tasks (e.g., summarizing a document) can be handled with a single LLM call, agents excel when tasks require:

  • Complexity: Drafting clauses for unfamiliar jurisdictions or navigating intricate negotiations.
  • Scalability: Reviewing and updating large volumes of contracts or generating bespoke revisions for multiple agreements.
  • Uncertainty: Tasks that lack clear step-by-step processes and demand dynamic decision-making.

For legal workflows, agents particularly shine in:

  • Drafting first-pass documents like NDAs or employment contracts.
  • Dynamic contract reviews that identify risks, propose tailored revisions, and ensure consistency across agreements.

Building Blocks: How Agents Work

The foundation of any AI agent is an augmented large language model (LLM). This means that the LLM isn’t just a standalone system—it’s enhanced with tools like search engines, memory systems, or APIs to make it more capable. For legal teams, this could mean enabling the AI to pull case law, analyze statutes, or consult firm-specific playbooks.

Several common patterns help structure how agents work:

  • Step-by-Step Processing (Prompt Chaining): In this approach, the AI breaks down tasks into smaller, manageable steps. For instance, it might first summarize a contract, then highlight high-risk clauses, and finally suggest alternative language for those sections. Each step builds on the previous one to ensure accuracy and clarity.
  • Routing Tasks: This pattern directs the AI to apply specialized workflows based on the input. For example, when reviewing contracts, the AI might classify an agreement as an NDA, licensing contract, or partnership agreement and then apply customized prompts or rules for that category.
  • Parallelized Processing: Tasks can sometimes be split into multiple parts that run simultaneously. A legal example could involve analyzing different sections of a complex contract—such as indemnity, limitation of liability, and governing law—at the same time, with the outputs combined at the end for a comprehensive review.
  • Dynamic Task Delegation (Orchestrator-Workers): Here, a central AI acts as a manager, dividing a task into subtasks, assigning those to specialized AIs, and then synthesizing the results. Imagine reviewing a contract where the orchestrator AI assigns specific clauses to worker AIs for analysis and redrafting, ensuring consistency while saving time.
  • Iterative Improvement (Evaluator-Optimizer): In this setup, one AI drafts an output while another evaluates it for quality and suggests refinements. This iterative loop ensures the final output meets your expectations, whether it’s a polished clause or a client-ready memorandum.

Practical Applications for Lawyers

Agents can transform the drafting process for unfamiliar jurisdictions by dynamically adapting their approach. They can consult external databases to gather relevant legal precedents, iteratively refine drafts based on these findings, and incorporate user feedback in real-time. This ensures that the output aligns with specific legal and jurisdictional requirements.

For large-scale contract reviews, agents leverage techniques like parallelization and orchestration to enhance efficiency. By dividing the review process into independent sections—such as indemnities, warranties, and governing law—agents can analyze multiple areas simultaneously. These outputs are then synthesized into a cohesive and comprehensive report, ensuring thoroughness while saving time.

Agents also excel in interactive client support when integrated with tools like knowledge bases or CRMs. They can dynamically generate draft communications tailored to specific client needs, automate responses to common queries, and access relevant case details instantly. This streamlines client interactions and enhances responsiveness.

Tips for Adopting Agents

Start by integrating AI into small, clearly defined tasks. For instance, you might use it to summarize contracts, identify missing terms, or generate suggested edits for specific clauses. As your comfort with the tool grows, you can scale up to more complex applications, such as dynamic drafting or multi-clause analysis.

If you’re considering using existing frameworks, proceed with caution. Tools like LangGraph or GUI-based platforms can simplify implementation but often add unnecessary complexity by abstracting too much of the AI’s behavior. For greater control, consider using LLM APIs directly. This allows you to tailor the system to your specific needs and maintain transparency about how it operates.

Clear, user-friendly design is critical. When developing tools for an agent, ensure they are intuitive and well-documented. Think of it as drafting clear instructions for a junior associate—you want the inputs, outputs, and decision points to be obvious. Iteratively test and refine these tools to reduce errors and improve reliability.

Key Takeaway

Success with AI isn’t about building the flashiest tools—it’s about finding practical solutions to legal problems. Begin with your current workflows, add AI where it creates the most value, and let simplicity and clarity guide your adoption. By starting small and scaling thoughtfully, you can unlock the full potential of AI for your practice.