Agentic AI is here for in-house legal teams. In simplest terms, it’s the latest evolution of AI and moves beyond behind the scenes passive assistance to action-oriented agents that once created can operate for you to plan, reason, and execute multiple workflow steps. Examples of potential agentic ai use cases for legal include:
- Legal research and memo drafting: Agentic AI can be tasked to research a specific legal question and draft an initial research memorandum complete with sources and notes.
- Streamlined contract analysis: Contract review can be scaled by assigning it to an AI agent. The agent can identify key clauses, cross reference information and spot inconsistencies or hidden liabilities across thousands of contracts.
- Case intake: AI agents can summarize case documents, build a timeline of events, and generate a high-level case analysis or strategy memo to support early case assessment and planning. (Thomson Reuters – Agentic AI and legal: How its redefining the profession.)
When it comes to building AI agents, it isn’t about how many agents you can create: it is about the quality of contribution that each agent makes. If you are considering developing your own agents, one or two focused agents that add consistent, repeatable value are a better investment than many small agents used randomly and with unsubstantiated efficiency gains.
Sounds logical, right? However, excited leaders are pushing their teams right now to produce as many agents as possible without structuring development and deployment to quantify and track usage and efficiency gains. Disjointed agents deployed without a plan will only lead to process confusion and will ultimately fail to achieve the desired results.
There is no dispute that AI agents are the direction of AI development for the foreseeable future. Wise legal technology vendors are incorporating agent capabilities within their platforms to improve performance and enable new features. Done correctly, this approach will provide greater flexibility and grow the breadth of their application capabilities.
On the flip side, some legal departments are considering their own agent development. Instead of acquiring purpose-built applications, they are asking internal teams to take on the challenge of creating or improving workflows using agents. This approach will require diligence and planning to be successful.
Getting Started with Agentic AI: The DIY Approach
An organization building its own AI agents needs to begin with their business processes, breaking them down into discrete tasks that can potentially be performed by an agent. Agent builders will need to be familiar with agent capabilities and strengths to recommend process points that can be made more efficient. Carefully considering the on-ramp and off-ramp for agent and human interaction is also required. Understand what the agent will be responsible for and what and where the human will contribute and control.
Deployment requires testing and iteration to ensure the agent is performing as expected and with consistency as it encounters real world process variations and anomalies And finally, measurements need to be taken pre-and-post deployment to identify if the agent is actually improving process performance in time spent, work output achieved, or other impactful metrics.
Supplement Internal Skills with External Agentic AI Expertise
Agent development is not a trivial exercise. Organizations should consider tapping partners like legal service providers, subject matter expert consultants, or outside counsel to develop and deploy agents. Application vendors are another good source to see how they are applying AI agent capabilities to evolve their products.
Agentic AI is an exciting new avenue for enhancing delivery of legal services, but it doesn’t replace process understanding, disciplined deployment, and tracking and measuring efficiency points to show value. Whether you take on the challenge internally or partner with your legal service providers or application vendors, agents will be part of your future in delivering legal support.