What Is Agentic AI Doing for CLM That Traditional AI Can’t?

Over the years, contract lifecycle management (CLM) software has solved a practical problem. It puts contracts in one place, eliminates approval roadblocks, and gives every department better visibility. But a well-managed bad contract is still a bad contract, and a well-tracked risk is still a risk. Considering that contracts dictate how revenue is recognized, how risk is allocated, and how relationships are structured, good management alone isn't enough.

That’s where traditional CLM systems fall short, and where agentic AI can fill the gap. It doesn’t just flag risky clauses or surface data points. It’s designed to understand why a term is problematic and how to fix it. It can draw from your playbook, surfacing language that’s already been negotiated and accepted, so your team isn’t starting from scratch. If there’s pushback from a counterparty, it may suggest alternatives based on how similar terms have been handled before. In short, agentic AI doesn’t just help contracts move faster — it helps them move in the right direction.

First, What is Agentic AI?

There’s a lot of talk about agentic AI right now — and not a lot of agreement on what it actually means. Some see it as any AI that can take an action. Others define it more narrowly: systems that pursue a goal, make decisions, and adjust their approach without needing step-by-step direction from a human. What’s clear is that agentic AI isn’t one fixed thing. It’s a spectrum of autonomy, and where a system falls on that spectrum depends on how it’s designed and how it’s used.

Here are some of the more common ways people describe agentic AI:

• Goal-oriented: Able to pursue an outcome across tools, systems, or datasets without being explicitly told what to do at each step.
• Capable of self-direction: Can choose what to do next based on context and adjust the plan if something doesn’t go as expected.
• Able to break down complex tasks: Can divide a bigger objective into smaller steps and execute those steps in sequence.
• Tool-aware: Uses a defined set of tools or actions to solve problems, deciding when and how to apply them.
• Part of a spectrum: Some systems are lightly autonomous and support decision-making. Others are more advanced, with the ability to act, adapt, and learn across workflows.
  
  

Think of these agents as trained operations team members: not replacements, but reinforcements. You guide them, you stay in control, and they handle the work that slows your team down. That opens up space to focus on strategy, growth, and outcomes that matter. And when they’re applied across the entire contract lifecycle — from intake to execution — they unlock visibility and agility teams haven’t had before.

Where Agentic AI Is Headed in Contract Lifecycle Management

Despite varying definitions, here’s what agentic AI could look like when it’s applied to contracting, and why it’s poised to change how teams manage risk, negotiations, and outcomes:

What Dynamic Risk Tracking Could Look Like with Agentic AI

Risk shifts over time through acquisitions, negotiations, and regulatory changes. Unlike tools that treat risk like a fixed value, agentic AI can dynamically update risk scores so legal and procurement teams know which contracts need immediate attention, no matter how business conditions evolve.

For example, when a company acquires another business, it also inherits hundreds or thousands of vendor agreements — each carrying different financial, compliance, and liability terms. Agentic AI could be used to scan every newly acquired contract, identifying both clauses that don’t align with the company’s standard risk framework and previously untracked clauses that may be worth tracking moving forward. 

The same applies to active negotiations. As deals evolve, so do their risks. Agentic AI can update risk scores automatically during contract versioning — factoring in newly added clauses, liability shifts, and deviations from preferred terms. In the future, legal teams won’t have to rely on outdated assessments; they get a real-time view of risk fluctuations as negotiations unfold.

For companies managing large volumes of contracts, this will eliminate the need for manual risk tracking. Instead of periodically reviewing agreements to see what’s changed, teams focus on the contracts that need action now — before issues escalate.

How Agentic AI Could Strengthen Negotiation Strategy

A fast approval process doesn’t mean a company got the best terms. Too often, negotiations focus on speed instead of outcomes, and teams end up repeating past mistakes: recycling contract language that led to disputes, agreeing to pricing models that erode margins, or accepting vendor terms that stall performance down the line.

Whereas traditional AI pulls past agreements for reference, agentic AI is more nuanced. In the future, it may help you analyze patterns across your negotiated contracts and playbook to recommend stronger deal terms. Over time, those insights will help teams refine how contracts are structured, based on what’s actually happening during negotiation. 

Every recommendation also comes with context. Agentic AI explains the reasoning behind its suggestions — what changed, what it’s based on, and where the risk is building — so you’re not left guessing. That level of visibility means your team can step in, course-correct, or train the model further without losing momentum or control.

IntelAgree’s Approach: Built for Agentic AI From the Start

The AI conversation in CLM is noisy. Vendors are racing to layer in whatever models they can — plugging in third-party tools, stitching together LLMs, or building homegrown systems that can’t scale. Most of it sounds impressive. Very little of it is deeply integrated.

IntelAgree took a different path. For nearly a decade, we’ve delivered AI to the enterprise not as an add-on, but as core infrastructure so we don't have to retrofit AI into our product. We built a modular infrastructure from the ground up to support machine learning, deep learning, and now, agentic AI. That foundation is why our system evolves naturally, and why we’ve been delivering contract intelligence at scale for years.

Inside the platform, multiple AI agents work in sync across the contract lifecycle — supporting negotiation, compliance tracking, clause analysis, and more. You’ll see that power in our generative AI-powered tool, Saige Assist. You don’t need a data science team or even historical data to train it. Saige Assist adapts to your contract language, your preferences, and your priorities out of the box.

And because we’ve integrated the latest LLMs — like GPT-4o and O-series models — into our infrastructure, Saige Assist can better understand your contract language, preferences, and negotiation patterns. The result? Smarter recommendations, faster workflows, and better outcomes.

Agentic AI isn’t a black box. It makes decisions you can see, trust, and control. Schedule a free demo to see what true contract intelligence looks like.