AI Agents in Legal Services: Automating Document Review and Contract Lifecycle Management (CLM)

While the history of Contract Lifecycle Management (CLM) can be traced back to clay tablets of Mesopotamia, over the past few decades, a host of CLM tools have digitized aspects of creating, negotiating, reviewing, executing, and maintaining contracts. Still, when it comes to AI in legal, technology has yet to truly maximize the process.

As CLM software provider Ironclad explains, “managing signed documents is the least complicated part of managing contracts and contracting processes… Legacy contract lifecycle management solutions have not only failed to digitize the contracting process, they have introduced a separation between companies’ contracts (which are usually DOCX or PDF files) and the information within them (e.g., a contract’s value or duration).” [1]

Orchestrated AI agents have already shown how effectively they can automate client intake for law firms, but by closely examining the ways that lawyers and businesses create, iterate on, and finalize contracts there are opportunities to both streamline and reinvent the processes associated with contracts. This article explores the limited ways that most CLM solutions are applying the technologies associated with AI, as well as the opportunities that emerge when organizations switch to an AI-first approach.

The Typical Contract Lifecycle

The phases of life for most contracts follow a workflow that is centuries old.

1. Creation: The contract is drafted by gathering the necessary information.
2. Negotiation: Terms and conditions of the contract are discussed with the other party and updated once an agreement is reached.
3. Approval: Relevant internal stakeholders review and approve the contract.
4. Execution: The contract is signed and becomes legally binding.
5. Management: Contract performance, tracking milestones, and compliance are monitored.
6. Renewal or Termination: The contract is prepared for renewal or termination.

Each of these stages presents opportunities for Agentic AI to transform and improve CLM, but a strategic approach is required to maximize efforts. “You can’t just bring in AI and press that button, and everything will be fixed,” Julian Tsisin, Director of Legal Technology at Meta, said on a panel at Docusign’s Momentum ‘22. “You have to get your house in order first.” [2]

Current CLM Solutions Offer Limited AI Capabilities

There are a host of SaaS providers with CLM products that organizations have come to rely on, but in the face of AI in law, many of the solutions are only scratching the surface.

• DocuSign’s CLM+ has the familiar capabilities and UIs associated with contract lifecycle management software but adds AI as a sidecar, with tools powered by large language models (LLMs) providing guidance on legal compliance and suggesting updated language through the negotiations process. 
• Concord, another CLM provider, has similar LLM-powered features that can extract data from PDFs and Word docs to automate intake and enhance search capabilities.
• Similarly, Juro has a CLM platform with tools like “AI Extract,” “AI Draft,” and “AI Review” that lean into well-established use cases that properly trained and guardrailed LLMs excel at.
• LinkSquares CLM solutions include LinkAI, which leverages specifically trained models for analysis and contract generation.
• Marketed as a distinct alternative to LinkSquares, the aforementioned Ironclad. 

These bolt-on approaches can save time and provide organizations with a boost but, as with other rigid approaches to automation, they pale in comparison to the benefits of AI agent orchestration.

AI Agent Orchestration Redefines CLM

Improving CLM using AI Agent Orchestration begins by examining each phase in a contract lifecycle and thinking through its component steps. It can certainly be beneficial to have a large language model (LLM) trained on past contracts and organizational guidelines that can generate contract drafts and suggest revisions. Still, this represents a narrow and siloed approach to technology.

For legal firms, there is more to gain by creating a technology ecosystem where a trained LLM with critical guardrails in place is just one expression of AI. With an open platform for orchestrating AI agents, firms can re-examine the contract lifecycle, looking for other ways to improve processes and workflows.

If the negotiation process is happening asynchronously, for example, an AI agent might be used to notify parties on both sides when terms have been revised or updates have been requested. Using a design pattern like “nudge,” the agent can keep negotiations moving forward by giving gentle reminders when people need to make decisions. Another AI agent might maintain a change log that’s available to all parties with the ability to create custom views of updates and requests based on user requests (i.e., “show me all of the changes that the client has requested that will require approval from our partners”).

As Figure 1 shows, the approval stage might also be managed and enhanced by the involvement of multiple AI agents working in concert. 

Figure 1: An Agentic Approach to CLM

Here, multiple agents are sequenced to streamline the approval process by handling things like scheduling, identity verification, and knowledge management. It’s also important to note that the skills that individual agents specialize in (e.g., scheduling, identity verification, and knowledge management) are not exclusive to any of the stages related to CLM. Similarly, none of these AI agents need to be used strictly for CLM.  

Scheduling, identity verification, and knowledge management are functions that have value across departments and processes within a firm. This is where the bolt-on approach falls perilously short. A serious approach to Agentic AI orchestration involves looking at specific use cases like CLM while also searching for tools and frameworks that can make AI agents flexible and scalable across the organization. Getting your house in order, so to speak, involves a holistic and honest assessment of current tools and processes and a willingness to take risks that have the power to move an entire organization forward. 

Key Considerations for AI Agent Orchestration

The first step in automating CLM involves a comprehensive audit of existing tools, workflows, and infrastructure, looking for gaps and charting the systems currently in use. CLM is a good starting point because there are various processes and workflows that offer viable use cases. Many of the bolt-on solutions available in the marketplace lean heavily into the summarization and generative capabilities of LLMs, which are quite powerful, but also somewhat narrow in terms of what Agentic AI is truly capable of.

As key AI agents are defined based on their objectives, teams will have to determine which systems they will need to access, what other AI agents they will collaborate with, and what their success metrics will look like. When setting out to sequence AI agents, it’s imperative to find an orchestration platform that allows for scalability, interoperability, and long-term adaptability. Having an AI agent that can draft a contract while paying close attention to legal and organizational requirements can provide a boost to select individuals inside an organization, but the ability to sequence that agent to work alongside other AI agents that can automate the procedural work involved with CLM (such as scheduling meetings and identify verification) lightens the load for a wider group of employees, as well as clients.

There are many use cases for AI agents across the various contract lifecycle stages. For example, AI agents can flag non-standard clauses, compliance gaps, and financial exposures in real time. Another set of AI agents can scan agreements for non-standard or risky clauses, suggest alternative language, and assist in collaborative negotiations by proposing compromise clauses based on historical acceptance rates. In case of regulatory or business changes, AI agents can update thousands of contracts simultaneously, ensuring rapid, consistent compliance across global agreements. AI agents can be used to analyze contract data and consumption patterns to proactively flag upcoming renewals, recommend renegotiations, or initiate terminations, minimizing missed opportunities and revenue leakage. 

For SaaS and professional service providers, AI agents can track renewals, upsell opportunities, and contract performance. They can also provide data-driven recommendations to increase renewal rates and average contract value, while freeing up internal teams from mundane, administrative tasks. Clearly, AI agents can provide additional functionality compared with vanilla CLM solutions, without any significant need for custom code development or increase in total cost of ownership (TCO).

This is the kind of flexibility that legal service providers should aspire to. With the ability to design, deploy, and iterate on their own collection of AI agents, evolving CLM is just one of the areas that can be exploited to massive effect. 

Conclusion

Applying AI agent orchestration in this manner is transforming the nature of legal services in ways that will impact all the software organizations use. Gartner has predicted that by 2028, “33% of enterprise software applications will include Agentic AI, up from less than 1% in 2024, enabling 15% of day-to-day work decisions to be made autonomously.” [3]

IDC predicts that by 2030, 50% of enterprise applications will use agent-powered interfaces, signaling a major shift toward Agentic AI in business operations, including CLM. According to industry research, 83% of businesses are dissatisfied with their current contracting processes, primarily due to inefficiencies and compliance challenges, driving the demand for Agentic AI-driven solutions. Using AI agents in CLM enables organizations to scale without increasing legal headcount, supporting growth and operational resilience.

For legal services, this points to a future where humans focus less on the tedious aspects of a contract lifecycle and instead put more emphasis on executing contracts that best serve their organization and its clients. Gartner’s estimate of 33% applications, including agentic AI, highlights the advantage businesses can gain by forging a framework and technology ecosystem that allows them to create their own collaborative AI agents that can function across applications and departments.

Sources:
[1] “What is Contract Lifecycle Management? CLM Explained,” Ironclad Journal
[2] Austin Miller, “Integrating AI with Contract Management Software,” DocuSign
[3] “Intelligent Agents in AI Really Can Work Alone. Here’s How,” Gartner