This is the ninth installment in a series about how to implement legal AI that knows your law firm. In the series, we cover the differences between LLMs and search, the elements that make a good search engine, the building blocks of agentic systems (e.g. RAG), and how to implement a system that is fully scalable, secure, and respects your firm’s unique policies and practices.
Customization fully leverages a firm’s people, expertise, and experience
An effective deployment of AI agents in a law firm begins by aligning with the day-to-day work of lawyers. Many platforms offer a set of default “skills” or capabilities that map to common legal workflows, such as drafting summaries, extracting concepts or clauses, or identifying precedents. While these baseline tools are helpful, the greater value comes from enabling firms to modify and extend these workflows to reflect their specific practices. The ability to define multi-step processes tailored to a particular team's approach to work is what turns general-purpose AI into a meaningful operational asset.
The need for customization is particularly evident across various practice areas. Even seemingly standard tasks, such as summarization, can vary widely in context and requirements. A real estate lawyer may need to extract key financial terms from long-form lease agreements, while a litigator may focus on identifying procedural arguments within deposition transcripts. These aren’t edge cases—they are the norm in legal work. Supporting this variation requires tools that allow legal professionals to shape agent behavior without waiting on software teams or external vendors.
This is where low-code and no-code interfaces play a critical role. Rather than writing scripts or deploying custom applications, legal teams can use visual tools, such as drag-and-drop interfaces, form-based logic, and editable templates, to adjust agent workflows directly. Low-code systems allow for occasional scripting when needed; no-code environments support entirely visual editing. In both cases, the aim is the same: to give professionals the ability to iterate quickly, test modifications, and deploy agents that work the way they do. The result is not just convenience, but a system that evolves with the organization, adapting to its language, structure, and internal knowledge.
There is an emerging category of tools that allow the user to describe what they want to build in words and then “conjure” an agentic workflow that can be tinkered and adjusted from there. These tools—sometimes described informally as enabling “vibe coding”—allow users to describe their intent in plain language and generate workflows or agentic behaviors with minimal understanding of the underlying logic. While this promises increased accessibility, it raises concerns about opacity, reliability, and the loss of explicit control over how systems function.
Law firms have a variety of fiduciary and ethical responsibilities, and one is a duty of technological competence. This likely extends to understanding the fundamental ways that a firms’ AI agents operate. Unfortunately, some vendors will only provide “low code” interfaces allowing a modicum of customization but won’t expose the whole orchestration. They provide built-in skills, workflows or agents but won’t allow customers to build upon them or modify them; if you want that, you have to pay for professional services or start from scratch and hope you have the internal expertise.
A better approach is to have a low code system that is full-featured and fully customizable—one that exposes even how its built-in AI agents work and lets you copy and build upon them as you wish. Here again, we see the value of access to a firm’s knowledge that is modular and “hot-swappable,” meaning that the firm can make changes to its tech stack without disrupting the operation of standard or custom agents.
Ultimately, this customization capability is where human skills sets can play an important part. AI can automate much of what lawyers do, but the ability to customize AI agents gives lawyers an outlet for creative solutions that can differentiate a firm from others. Creating better agents, designed around the real work a firm is good at, is where the interplay between humans and technology really creates value.
Explore the blog series “Legal AI That Knows Your Firm”
Posts in this series:
- The Allure (and Danger) of Using Standalone LLMs for Search
- Why Retrieval Augmented Generation (RAG) Matters
- All Search Engines Are Not Created Equal
- Why good legal search is informed by the entire context of your institutional knowledge—not siloed or “federated”
- How can your AI securely use all of your firm’s data?
- Why an “always on” search engine is a prerequisite for scalable AI adoption
- Building AI agents that are informed by your real-world legal processes
- As the variety of tasks automated by AI agents proliferate, how does a firm manage it all?
- How do I adapt workflow agents to the specific needs of my firm? (this post)
- Does your AI platform set your firm apart from the competition? (Coming soon)
This post was adapted from our forthcoming 24-page white paper entitled "Implementing AI That Knows Your Firm: A Practical Guide." Sign up for our email list to be notified when the guide is available for download.