The easiest way to scale as contract volume and complexity grow is to invest in tooling that scales your in-house legal team's output without adding headcount.
The automation of time-consuming manual tasks provides clear, immediate value to operations, quickly justifying the costs. However, the solution of previous years, Robotic Process Automation (RPA), struggles to keep pace with the increasing complexity of modern contract lifecycle management.
The advent of artificial intelligence and the new agentic capabilities it offers has ushered in the dawn of a new type of solution: AI-enhanced contract intelligence platforms such as LinkAI for automated reviews and advanced contract analytics.
To understand how the capabilities of Agentic AI surpass those of RPA, let’s remind ourselves of what RPA is and how it adds value to teams.
What Is RPA (Robotic Process Automation)
Using rules and script-based bots (software programs), RPA emulated human interaction with machines by completing a series of tasks normally performed by a human, automatically. This provided a lot of value in time-saving when tasks were repetitive and predictable across standard databases.
How rule-based RPA works
RPA uses a typical If-This-Then-That (ITTT) logic to determine a sequence of events that it could automate. Business users or developers could program these bots with steps that equated to specific actions, such as copying and pasting data using predetermined logic rather than reasoning.
Common RPA use cases in legal
RPA processes were mostly used for:
- Manual Data Entry & Manipulation
- Document Routing Between Stakeholders
- Providing Automated Status Updates
While useful, these use cases quickly became a minimum expectation from any solution, and the needs of the modern in-house legal team demanded more. Enter Agentic AI.
What Is Agentic AI?
Moving well beyond simple task completion, or even the question-and-answer style execution of traditional text-based large language models (LLMs), Agentic AI enables artificial intelligence to proactively solve problems in a way that is oriented towards achieving specific goals. Reaching these goals could include multiple phases, with the AI agents adapting on the fly. This is possible because the agents can analyze intent, make decisions, and understand context.
Autonomous decision-making vs scripted rules
When it comes to specifically legal environments, AI Agents can understand the intent driving specific legal language. Loaded with context from a company-specific playbook, the Agentic AI system can automatically evaluate risk, proactively prompt queries, and conduct automatic redlining without the need for step-by-step instructions from a business user or developer.
This is superior to scripted rules, because it accounts for many possible eventualities rather than following one step at a time. It is a more flexible and powerful approach that provides upstream value from implementation, and over time.
How AI agents adapt to unstructured contract data
Many contracts follow familiar templates and share common language. But changes in clause wording, document structure, or business requirements can still affect how an agreement needs to be reviewed and routed.
Rules-based automation, including RPA, works well when inputs and processes are predictable. When those conditions change, predefined rules may need updates or exceptions.
AI agents can interpret language and context to handle a wider range of variations within defined policies. This gives teams more flexibility as agreements and requirements evolve, while keeping unfamiliar or ambiguous situations subject to human review.
Agentic AI vs RPA: Key Differences
The key differences between these two processes are most apparent in these areas:
- Methodology: rule-based scripts vs natural language models.
- Compatibility: RPA requires standardized inputs rather than complex legal texts.
- Decision-making: RPA follows deterministic yes/no logic; Agentic AI applies contextual reasoning and risk evaluation before acting autonomously.
Flexibility and scalability
In short, RPA is incapable of scale because it is inflexible: a change in standards might break every bot, all of which would require reprogramming. Agentic AI is the opposite, and seamlessly pivots based on incoming changes, proposing solutions to its human stakeholders rather than simply failing.
Handling exceptions and edge cases
The difference here is that whenever RPA encounters an exception, it routes it to a human, but Agentic AI goes beyond this, providing context and reasoned next steps with risk-assessed resolutions.
Implementation and maintenance effort
Running RPA will require the time of skilled technicians who must continually fix broken scripts, while purpose-built Agentic AI solutions such as the LinkSquares platform provide ease of use and time to value out of the box.
AI Agents vs RPA for Business Automation Use Cases
In a nutshell, though RPA can still provide value, AI Agents are capable of dealing with much higher complexity workflows and volume.
Contract review and redlining
In short, RPA cannot provide value here, whereas a solution like LinkAI can. LinkAI parses third-party contracts, cross-references clauses against your legal playbook, inserts pre-approved fallback language directly into Microsoft Word, and provides rationales for every proposed edit.
Approval routing and reporting
Rather than dealing with a specific task such as “route anything valued over $100k to a human”, Agentic AI can assess an entire document for an overall risk profile.
Which Approach Fits Your Legal Workflows
This will depend on your unique circumstances.
When RPA still makes sense
RPA makes perfect, cost-effective sense if you have a low complexity workload and need it for bulk downloads or copy-pasting. As soon as the maturity level of your organization’s contract lifecycle management needs to improve, RPA will no longer be fit for purpose.
When to move to Agentic AI
As soon as your legal department is ready to get started with automations involving unstructured text, reasoning and judgement, and variable factors, it is time to consider Agentic AI solutions.
Conclusion
An Agentic AI solution can see your team quickly and continuously:
- Review and redline incoming agreements
- Continuously monitor and risk-assess changes
- Proactively highlight obligations
And much more, transforming workflows away from the bottleneck of RPA into AI-enhanced business value drivers.
Learn more about LinkAI from LinkSquares here.