Contracts have always been vital to business growth and the business is increasingly coming to terms with this reality. As contracts grow in complexity and importance, in-house legal teams are expected to handle growing contract volumes and an expectation to handle that volume faster, without growing their budget or headcount.
Contract Lifecycle Management (CLM) tools have historically addressed where contracts live and how revisions and approvals are tracked, but organization and auditability don’t address the challenge of managing in-flight negotiations. The revisions tied to these negotiations still require experienced human eyes for line-by-line audits.
Agentic Artificial Intelligence (AI) alleviates this burden, working alongside legal departments, learning from business playbooks, reasoning, and evaluating risks proactively. The more sophisticated the tool, the greater the growth potential.
What Is Agentic AI in Contract Redlining
Agentic AI far surpasses the text-based question-and-answer experience of Large Language Models, which has become synonymous with AI for most users. With Agentic AI, one or more agents (which are really bundles of code that are related to specific instructions) work together to achieve missions that could include multiple phases and changing criteria.
For example, a team of AI agents could be deployed to work alongside human legal teams to enhance the review and negotiation of contracts throughout the redlining process.
How agentic AI differs from traditional CLM automation
CLM automation is useful for tasks that can be automated, such as finding exact-match clauses and monitoring rules-based logic, but falls short when it comes to non-standard phraseology or the complex legal language that usually requires deep expertise.
Now that AI has grown to understand intent, agents can determine how far a contract’s language may stretch the boundaries outlined in an organization’s playbook.
| Capability | Legacy CLM | Agentic AI Contract Redlining |
| Primary Function | Rules-based routing & text matching | Task execution, reasoning & intelligent drafting |
| Analysis | Highlights predefined key terms | Evaluates clause intent, context & cumulative risk |
| Redlining | Manual insertion of static fallback clauses | Generates tailored, context-specific edits directly |
| Workflow | Rigid, linear steps | Dynamic problem-solving with human oversight |
Key capabilities of AI contract redlining software
CLMs help flag known keywords and generate pre-defined templates, but agentic AI contract redlining software takes it to the next level with features the CLM of old simply can’t match, including:
- Playbook alignment: Agentic AI can ingest and learn company-specific, custom rulesets, expectations, goals, and risk tolerances before automatically applying and evaluating these rules against all incoming contracts.
- Proactive insights: instead of one-time or periodic auditing, AI redlining software monitors contracts continuously, highlighting risks as they arrive and before they can cause any issues.
- Transparent reasoning: AI software will go beyond recommending or directly making changes, offering explanations, citations, and the underlying logic behind choices or decisions. These explanations can be challenged, further training the AI over time.
How Agentic AI Redlining Software Works
This type of solution enhances legal team outputs by establishing a system that executes as well as plans. Humans retain control, whilst the software implements frameworks that empower the humans to scale their workflows.
It does this through a simple, step-based process:
- Contracts are ingested
- Clauses and risks are identified
- Edits and recommendations take place instantly, with detailed rationale behind each action
- An experienced legal professional reviews and approves
Naturally, what’s happening under the hood is more complex than in this example, and admins may wish to implement additional steps, but the outcomes remain largely the same: the AI and humans work in tandem to achieve goals faster and more efficiently.
Clause identification and risk scoring
Artificial Intelligence breaks down all ingested contracts into distinct functional components using Machine Learning and Natural Language Processing (NLP) capabilities:
Extract: key clauses such as liability caps, termination rights, intellectual property, and governing law are immediately identified.
Assign a risk score: the new contract is contrasted with the predetermined playbook and assigned a risk profile based on severity. This could relate to hidden liabilities or critical deviations from company policy.
Intelligent edits and negotiation suggestions
Once the above risks have been identified, AI Agents can take action if the human asks it to do so. These edits could include matching a contract’s language to a predetermined library of fallback phrases, generating a dynamic, custom edit that preserves the company style and syntax, or disseminating commentary and rationale to a list of approved legal counsel or business users.
Human-in-the-loop (HITL) review checkpoints
HITL architecture governs the use of Agentic AI contract redliners, and proposed changes are made directly inside interfaces familiar to humans such as Microsoft Word and an intuitive in-app editor. From there, a human can accept, reject, or ask the AI about these suggested changes with one click or prompt.
Why It Matters for Legal and Business Teams
Legal operations managers, general counsel, contract managers, and in-house legal teams that adopt this emerging technology stand to receive both immediate and long-term benefits that compound throughout their organization.
The most meaningful results will quickly make themselves felt in these areas:
Faster turnaround times
AI Redlining software slashes the time needed for contract negotiations because the first round of reviews happens instantaneously once a contract is ingested. This carries through to the remaining stages of the contract lifecycle, reducing overall closing time per contract negotiation, renewal, or termination decision.
Reduced legal risk and errors
The human eye is a critical component of the contract review process, but it is prone to fatigue and error, especially as deadlines loom and workloads increase. Agentic AI works at a consistent quality standard that is aligned to your business rules and preferences, freeing up human expertise for more important work at the later stages of the lifecycle. With Agentic AI, your company playbook keeps working for you around the clock.
Scaling contract review without added headcount
Onboarding new legal counsel is much more difficult and costly than implementing AI automation. Technology that can complete the first pass of important work enables organizations to scale without unsustainable growing headcounts, even as the volume of contracts swells.
Choosing the Right AI Contract Redlining Software
Though AI contract redlining software is relatively new, there is a selection for procurement managers to evaluate and choose from. Instead of a solution that has simply bolted AI on top of a legacy system, it may be prudent to select one built with it in mind from the start for a more seamless experience.
Beyond this AI-first focus, there are two other major areas to investigate when comparing solutions.
Must-have features
- Native integrations: any new solution should be able to integrate seamlessly with critical tools like Microsoft Word, removing the need to learn something new and unfamiliar.
- Playbook configuration: the platform should be able to allow users to configure risk parameters, fallback language, and clause rules tailored to their exact needs via a company playbook.
- Breadth of repository: the AI-enabled product suite must be able to access a holistic view of the entire contract repository to gain needed context. This is vital for maintaining consistency across all relationships, whether they be partner, vendor, or customer.
- Ease of use: it is important that a natural, conversational interface is available to users for querying, so that they can challenge or implement edits directly within the interface.
Integration and security considerations
Outside of useful features, maintaining the private and confidential nature of sensitive contracts should be paramount and factor into any evaluation of new technology. These areas are the ones that need to be high in confidence:
- Data Privacy: customer and other stakeholder data should never be used to train AI models or leave the users' servers and be stored in the platform’s central data hosting. Evaluators must check whether this will be the case as a matter of urgency.
- Ecosystem connectivity: ideally, the new platform should be able to connect with all the other platforms in the ecosystem, including CRMs like Salesforce, any signature management tools, and more.
- Audit trails & transparency elements: every revision, suggestion, or challenge should be proactively justified and tracked at the moment it is created for business visibility.
Conclusion
The evolution that moving towards this AI-enabled technology represents could be summed up as maturing from reactive to proactive. With it, in-house teams spend less time on early revisions and policing company policy, and more time making better decisions, faster.
Learn how the LinkSquares platform helps your team find contracts faster, uncover valuable insights, and act on contract data. Schedule a demo today.