This post is the eighth 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.
An AI workflow platform helps manage agent operations and empowers “orchestration” of all the moving parts
As we discussed in the previous post, firms are increasingly adopting AI agents to perform a full spectrum of functions that legal professionals used to do manually. To be effective, such AI agents are built upon a robust enterprise search platform that is “always on” and continuously informs them with knowledge about the firm’s work and preferred processes.
In a law firm, especially a large one, there are typically many lawyers and many practice groups, each with their own sets of unique workflows that they perform on a variety of matters.
Some examples of legal workflows that could be AI agents might be:
- Summarize a matter for a new associate joining the team.
- Find the best template document for a legal brief, based on a set of criteria.
- Create a post-merger delivery checklist for this transaction.
- Draft an emergency arbitration request.
- Identify missing prospectus risk factors based on similar deals and track changes.
- etc.
The list could be endless.
In fact, AI agents can encompass everything from pinpoint requests for data points to broad, complex workflows involving many steps and client-ready outputs. One can imagine various ways to build an AI agent for any of these individual workflows. There is not today (nor likely to be anytime soon) any single AI agent that can perform all of these tasks. Instead, a range of separate agents will be needed.
As a result, firms will need a platform for creating, testing, managing and deploying different AI agents for all their different purposes. The platform will need various functions: It’s critical to know who built a particular agent, the agent’s intended purpose, what data sources it accesses, and whether it is getting a lot of use. Moreover, a law firm likely has a variety of policies it needs to abide by in its AI use, such as outside counsel guidelines that put restrictions on use of client data. These are also important functions of the platform. In short, an AI workflow platform provides all these critical “AI agent operations” functions—agent lifecycle management, monitoring, and compliance.
Also central to the operation of AI workflow agents is the concept of orchestration, which refers to the structured coordination of discrete tasks performed by agents to support multi-step workflows. Each agent is composed of functional components, including retrieval, classification, transformation, summarization, and others, which can be combined into larger processes. These modular elements serve as the building blocks of agent behavior, and orchestration governs how they interact, in what sequence, and under what conditions. In practice, orchestration ensures that agents are not just a set of isolated capabilities but part of a coherent system capable of handling increasingly complex operations.
Low-code or no-code interfaces can be useful tools to define and refine these workflows, allowing teams to build, test, and adapt agent behavior without deep engineering involvement. A further challenge arises in how agents navigate between deterministic logic (such as rule-based decision trees) and probabilistic outputs, including search relevance scoring, language generation, or entity extraction. Effective orchestration integrates both types of responses into workflows that are repeatable, traceable, and grounded in real-world performance.
Underpinning all of this is the knowledge intelligence layer (discussed in a previous post) which serves up all the internal data, documents, and structured content that form the factual substrate for AI systems. This layer provides grounding for retrieval, context for decision-making, and a reference for validating outputs.
Without a knowledge intelligence layer, agents risk operating on generic or incomplete information. With it, agents can operate with domain-specific awareness and consistency. In this way, the value of an AI agent is not defined solely by its capabilities, but by how well those capabilities are orchestrated to work in conjunction with the organization’s actual knowledge base.
Moreover, effective orchestration actually requires tools with full-scale access to the entirety of a firm’s knowledge and data in real time. One might ask, isn’t it enough to manually assemble the data you need each time you embark on creating a new agent? But play that forward. With hundreds, potentially thousands, of different legal workflows constantly in motion around a large law firm, it would soon become impossible to manage and maintain an effective library of agents in this way. The knowledge index is in fact the critical layer that unlocks scalability—not to mention that this layer is the only way to ensure the AI agents truly know and understand the full scope of experience of your firm. Ideally the AI platform includes the ability to enrich the knowledge layer over time, giving your AI agents ever more context (e.g. metadata and additional system connections of the type we discussed in a previous post). This is as important as the other operational functions.
In sum, a robust set of AI-based workflow agents will require AI agent operations functions—agent lifecycle management, monitoring, and compliance, as well as ongoing integration with and enrichment of the knowledge intelligence layer to ensure agents remain grounded in the firm’s complete and current body of work. This empowers knowledge management and practice support staff to manage the growing number of otherwise disparate tools and keep a handle on how data is being used.
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? (this post)
- How do I adapt workflow agents to the specific needs of my firm? (Coming soon)
- 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.