Disrupting the Deal: How Agentic AI is Redefining Contract Management

Just a few years ago, when ChatGPT made AI the topic of dinner table discussions, if I told you that AI agents could draft complex contracts for you, how would you react? What if I’d told you they could negotiate your third-party contracts – like a lawyer – and send them through the right approval loops all the way to signature? You’d laugh – and you’d be right.

But if I tell you today that AI agents can do these things, you’d still question how much of that is possible, but you wouldn’t think it’s laughably impossible.

AI agents are here, and they are promising great things. Large tech companies are placing bold bets on them. But what is an AI agent? And how do they work with contracts?

What are AI Agents?

There are many ways to define AI agents or agentic AI systems. I like to define them as “a system that has agency to make decisions, take action, and interact with external environments beyond the scope of its training data.”

To do that, the AI must be able to generate the correct string of tasks for themselves, complete each assigned task, and work on the next one – reasoning, learning, and making decisions based on new information at each step – until the objective has been met. They essentially create their own workflows to achieve a complex goal.

This ability to create workflows has led to a growing notion that AI agents will eventually disrupt SaaS as we know it. On the other hand, systems of records, like contract lifecycle management (CLM) systems, will remain essential. In fact, these systems will serve as the foundational repositories for enterprise data that AI agents rely on to operate effectively.

It is well known that LLMs have problems with memory and hallucination – two sides of the same coin. Without a large volume of clean, granular data, the AI wouldn’t be able to consistently and precisely perform its tasks without hallucination.  We’ll explore this a bit more in my next blog. That said, contract management workflows are about to undergo a massive shift.

A New Future for Contract Management

With contracts, the first step is understanding the document, its contents, definitions, and metadata. The better the AI understands contracts, the better it performs. But more importantly, the AI learns from the way you contract.

For instance:

• How high does a contract’s value have to be for your finance team to be involved in the review?
• Are all your supplier agreements in North America required to include a clause for quarterly performance reviews?

The AI learns these nuances from your feedback and actions, so that the next time you need a contract reviewed, it knows that you prefer a 90-day written notice to cancel auto-renewal on long-term agreements but can fall back to 60 days, if needed.

The AI agent can edit it on a counterparty paper and share it with the relevant stakeholder for approval.

And All You Had to Do Was Ask

AI agents are ushering an era of UX revolution in the contract lifecycle management (CLM) space. An agentic AI system consists of an LLM and/or a reasoning engine that orchestrates the actions of multiple AI models (LLMs, small language models, etc.) and rules that have been built for various applications. So, all you have to do is tell the LLM what you need and the agents do the rest. On Sirion, AskSirion plays the role of orchestrator, providing a single point of access to our IssueDetection Agent and Redline Agent.

This conversational or “chat” style of interaction is characteristic of an agentic CLM. With a single conversational prompt, you could:

• Create contract drafts: Use your AI agents to generate first drafts of contracts by leveraging templates, historical data, and predefined clauses. They can also tailor contracts to specific use cases, ensuring that all necessary terms and conditions are included.
• Review and analyze counterparty papers: Simplify and accelerate contract negotiations by suggesting edits or redlines. For example, if a clause is overly favorable to one party, the agent can propose alternative language that aligns with predefined negotiation guidelines, aka, your playbook. Over time, the AI learns from past negotiations to improve its suggestions.
• Automate approval workflows and collaboration: Streamline approval workflows by routing contracts to the right stakeholders at the right time.
• Stay on top of renewals and terminations: Proactively manage contract renewals by using AI agents to analyze historic data, consumption patterns, performance metrics. The agent can also independently recommend whether to renew, renegotiate, or terminate a contract.

How Does an Agentic CLM Work?

Agentic CLM brings a revolutionary approach to contract lifecycle management by leveraging AI agents to handle everything from drafting to execution. Here’s a breakdown of the key steps involved in an Agentic CLM system:

• Data Extraction: Extract data (clauses, obligations, metadata, etc.) from contracts at a granular level that provides high-fidelity context for the AI in later stages.
• Perception: Gather extracted data from your contract repository (contracts and adjacent documents) to understand task context.
• Decision-Making: A large language model orchestrates the process by analyzing the extracted data and coordinating specialized modules to detect issues. Techniques like retrieval-augmented generation (RAG) and knowledge graphs (KG) access historical and proprietary information to ensure precise, relevant outputs.
• Action Execution: By connecting with the enterprise environment via APIs, the AI executes tasks such as triggering workflows. Built-in guardrails ensure that any deviations from predefined parameters are flagged for human review.
• Learning and Adaptation: The AI continuously refines its models through feedback loops, using interaction data to adapt to your organization’s contracting practices and improve overall decision-making and efficiency.

Incorporating these steps, an Agentic CLM system not only automates the routine aspects of contract management but also enhances decision-making through continuous learning. The result? A more efficient, accurate, and intelligent way to manage your contracts – one that truly keeps pace with your evolving business needs.

Agentic AI systems are transforming CLM by bringing intelligence, automation, and efficiency to every . While we’re still in the early stages of this technology, the progress so far is undeniable. The future of CLM is agentic, and it’s closer than you think.

Related Resource: The Agentic CLM Change Management Playbook

Watch out for our next blog in this series for a more in-depth look at an Agentic CLM and how data is at the heart of it all.