As businesses grapple with increasingly complex contracts and mounting regulatory demands, traditional contract management solutions often struggle to keep pace. As a contract professional who embraces technology and is constantly seeking new ways to work more efficiently, you might be wondering about your options in this era of innovative solutions.
Your answer might come from Agentic AI—a new generation of intelligent, autonomous systems that promises to transform and redefine the approach to contract management from static, manual processes into dynamic, self-optimizing workflows.
The AI landscape is growing rapidly. New innovations have already transformed how we interact with technology and manage the complexity of modern business. Agentic AI stands out as particularly promising. While generative AI, such as large language models (LLMs), has captured public interest for producing human-like text, images, and code, agentic AI introduces a new dimension that can significantly impact the contracting business—autonomous action, goal pursuit, and adaptability.
By using agentic AI, you can turn contracts into living documents managed by digital agents capable of independently navigating negotiations, monitoring compliance, and adapting to evolving business contexts. You can also make unprecedented efficiency possible to produce accuracy and agility across the contract lifecycle.
Unlike traditional AI – which often focuses on pattern recognition, predictions, or content generation – agentic AI systems can demonstrate capabilities such as persistence, initiative, adaptability, and a sense of agency – all of which can operate independently over extended periods, handle unforeseen scenarios, and coordinate their actions toward overarching objectives.
This means agentic AI has the potential to:
- significantly reduce manual workload and administrative overhead;
- achieve higher accuracy and consistency in contract language and compliance;
- improve visibility into contract lifecycles and bottlenecks;
- ensure faster turnaround for contract approvals and negotiations;
- safeguard proactive risk mitigation and compliance management; and
- produce continuous process optimization based on real-world data.
However, appropriate precautions should be taken and is important to keep in mind that:
Sensitive contract data must be protected to ensure data privacy and security.
Human oversight must be ensured. Autonomous systems should augment, not replace, expert judgment.
Change management: organizations must adapt new or improved processes and explore new ways of working alongside agentic AI.
Navigating key aspects of agentic AI
Building agentic AI involves designing systems that can operate autonomously—with minimal human intervention—to pursue specific goals. These systems are capable of initiating actions, adapting to changing conditions, and maintaining focus over time. It’s natural to feel uncertain about accuracy and control when exploring this space—those concerns are valid. But don’t let them cloud your perspective. Our goal is to help you navigate the core concepts of agentic AI so you can explore its potential with greater clarity, confidence, and peace of mind.
Agentic AI draws inspiration from the concept of an “agent” in computer science and cognitive science: a system with sensors (to perceive), actuators (to act), and internal processes (to reason and plan). These systems can receive high-level goals, decompose them into sub-tasks, plan execution, monitor progress, and adapt strategies as circumstances change.
In this context, here are some agentic AI attributes that can make a significant impact on any journey towards efficiency gains in contracting:
- Autonomy: Agentic AI operates independently, requiring minimal guidance from humans after initialization. It can make decisions, prioritize actions, and resolve conflicts between competing objectives.
- Goal-directedness: Unlike reactive systems, agentic AI is designed to achieve specific outcomes. It can take a high-level instruction such as “optimize this contract workflow” and dynamically plan steps to attain success. This can lead to a specific outcome faster.
- Persistence: Agentic AI empowered systems can work on long-term tasks, monitor progress, and adjust their approaches if new information or obstacles arise. This demonstrates resilience and adaptability.
- Context awareness: Agentic AI continuously perceives its environment, gathering data to inform decision-making. It can sense changes, evaluate new constraints, and react appropriately.
- Learning and adaptation: While many agentic AI systems leverage machine learning, their key distinction is the ability to adapt strategies in real time, learning from successes and failures to improve future actions.
- Interactivity: Agentic AI agents can communicate with users, other AI agents, or external systems, soliciting feedback, requesting clarification, or collaborating as needed.
Task decomposition and planning: Agentic AI can break down complex goals into actionable tasks, sequencing and organizing them for efficient execution. This includes handling dependencies and parallel processes.
Typically, an agentic AI framework leverages these core aspects to orchestrate an autonomous, end-to-end workflow. From perceiving and processing information, to reasoning, planning, and executing tasks through the right tools, agentic AI shifts operations from an AI-assisted, human-led mode to a largely self-directed, AI-enabled environment with minimal human oversight.
To illustrate how these capabilities come together, the image below represents a simple, hypothetical agentic AI architecture. A human request is the input that provides the goal that the agent will pursue. The agent will acknowledge the directions by applying perception and through reasoning will set up a plan and use the available tools to execute the plan and achieve the desired outcomes. The agent will also interact with signals from the environment continuously learning and adapting to changes.
AI and generative AI represent distinct capabilities within the AI spectrum. Understanding their differences and synergies is crucial for envisioning next-generation applications.
Simply put, Agentic AI functions differ from generative AI in two basic ways:
- Generative AI is like a talented artist or writer who can produce content on demand, and
- Agentic AI is like a project manager who sets goals, coordinates resources, tracks progress and adapts it as needed, which potentially calls on generative AI for creative tasks within the broader workflow.
In other words, generative AI interprets human instructions—typically through prompts—and, with the support of data and machine learning, generates tailored outputs or solutions. In contrast, agentic AI adopts a machine-first, autonomous approach that is subsequently enhanced by human insights.
Generative AI, the creative engine, refers to models that can synthesize content—text, images, music, code, and more—based on learned data patterns. These systems excel at producing realistic, creative, and contextually appropriate outputs in response to prompts. Their primary focus is on creating and imitating, not on taking independent action or pursuing a goal.
Key features of generative AI include:
- Content generation in natural language or other modal qualities
- Pattern recognition and sophisticated imitation
- Responsiveness to user prompts, but lack of initiative or persistence
Agentic AI, the autonomous orchestrator, can incorporate generative AI models as “tool users,” employing their creative abilities to help achieve goals, but agentic AI also introduces a higher-level layer of reasoning and action. For example, an agentic AI managing contracts might use a large language model to draft a new agreement, another tool to track deadlines, and yet another to send reminders or escalate issues. It does not merely respond to one-off prompts; it actively manages and adapts, steering the entire process from initiation to completion.
How can Agentic AI help transform contract management?
With a better understanding of agentic AI capabilities, hopefully at this point you can imagine why contract management is a prime candidate for transformation by agentic AI. Managing contracts — whether in legal, procurement, sales, or compliance contexts — involves a complex web of tasks: drafting, reviewing, negotiating, obtaining approvals, monitoring obligations, tracking deadlines, and ensuring compliance.
Human oversight remains essential, but the sheer volume and complexity of contracts often stretch teams thin. Time and effort are frequently distributed across high, medium, and low complexity contracts in ways that dilute strategic focus and underuse specialized expertise.
Agentic AI offers a compelling solution: it can intelligently triage tasks, optimize resource allocation, and handle routine processes autonomously, freeing your team to concentrate on high-value work.
But this raises the question: what comes next in the AI revolution?
It’s not just about promising better time management; it unlocks scalable capacity without the friction of headcount expansion battles, which enables organizations to do more with the talent they already have.
Agentic AI can address these challenges across several dimensions:
1. Automated drafting and review
Agentic AI can autonomously generate first drafts of contracts by analyzing templates, organizational policies, and relevant regulations. When a contract is received, the AI can review its contents, flag unusual clauses, and suggest edits—all autonomously, using generative models as needed.
Unlike static AI tools, an agentic system can manage this process end-to-end, ensuring drafts are completed, reviewed, and routed for approval without manual intervention. In addition, agentic AI can support amendment management. When business conditions shift, agentic AI systems can proactively identify affected contracts, recommend necessary amendments, and initiate approval workflows.
2. Workflow coordination and task management
A contract’s lifecycle involves multiple stakeholders and sequential steps. Agentic AI can act as a workflow orchestrator, automatically assigning tasks, sending reminders, and escalating issues if deadlines are missed and routing for approval and ultimately, signature. It tracks the status of each contract, anticipates bottlenecks, and reallocates resources as needed to keep processes moving smoothly.
3. Compliance monitoring and risk management
By continuously monitoring contract terms, performance, and regulatory changes, agentic AI can proactively flag risks—such as upcoming expiries, non-standard clauses, or non-compliance events. Autonomous agents can propose mitigation steps, initiate renegotiations, or alert relevant stakeholders, reduce the risk of costly errors and miss obligations.
Agentic AI can also support obligation monitoring by tracking key dates, deliverables, and obligations across large contract portfolios, automatically sending alerts or triggering actions if milestones are missed or compliance issues arise. Agentic AI can continuously analyze contract data, detect trends, flag anomalies, and generate audit-ready reports to support risk management and regulatory requirement.
4. Data extraction and insight generation
Agentic AI can extract key data points (dates, parties, obligations, financial terms) from contract documents, populate databases, and generate dashboards for real-time oversight. It can identify trends, such as recurring negotiation points or frequent amendments, and propose process improvements.
5. Continuous improvement through feedback loops
Because agentic AI systems learn from ongoing operations, they can refine templates, negotiation strategies, and approval workflows over time. By analyzing which contract clauses lead to disputes or delays, they can recommend changes to minimize future risks and enhance efficiency.
6. Seamless integration with enterprise systems
Agentic AI can connect with document management systems, e-signature platforms, Enterprise Resource Planning (ERP) software, and communication tools. This integration creates the potential for the agentic system to move contracts seamlessly between stages, ensure data consistency, and maintain a clear audit trail. Currently, agentic AI is part of leading contract lifecycle management solutions (CLM), but it can also be added to an existing CLM.
7. Enhanced collaboration
By serving as a central “intelligent coordinator,” agentic AI can bridge gaps between departments, facilitating communication and ensuring everyone stays informed of contract statuses (professional ranks or standings) and required actions.
Agentic AI represents the next frontier in artificial intelligence, moving beyond content generation to autonomous, goal-driven action. By acting as tireless, adaptive coordinators, agentic AI systems can dramatically enhance efficiency and accuracy in contract management, freeing professionals to focus on strategic decision-making and complex negotiations. As organizations continue to digitize and scale, the adoption of agentic AI will be central to achieving operational excellence, managing risk, and unlocking new levels of competitive advantage.
We hope this article has helped you connect the dots between the broader conversation around Agentic AI and its tangible impact on your day-to-day work. The potential is not abstract - it’s actionable. Agentic AI can empower you and your teams to redirect time and energy toward the moments where human judgment, creativity, and strategic thinking are truly indispensable. Whether embedded within a CLM platform or deployed through standalone architectures which supplement a CLM platform, this technology marks a turning point—offering organizations a viable path to do more with less, while elevating the role of professionals in the contracting process.
ABOUT THE AUTHORS
DAYSE SILVA brings over 18 years of expertise in the legal industry as a seasoned Contract Management professional. She has collaborated with global teams and clients, gaining deep knowledge of Contract Lifecycle Management (CLM) technologies and processes. Her experience includes implementing best practices to streamline contract reviews, accelerate turnaround times, and lead multicultural teams across diverse industries and client portfolios. Dayse consistently delivers contract management solutions that prioritize efficiency and business relevance.
She is now focused on leveraging her extensive experience in contract matters, strategic thinking, problem-solving capabilities, and leadership skills to build a diverse, high-performing team and a dynamic, purpose-driven career.
ANDREA HORTON brings over 25+ years of legal, business and strategy experience to assist clients with Legal Operating Model design, Contract Lifecyle Management, Risk Management Frameworks, Financial Crime, and other Regulatory Change initiatives. She has demonstrated a proven ability to design and implement effective global solutions to address emerging risks and provide strategic solutions with a focus on process optimization, efficiency, automation and exceptional client experience.