The term “AI agent” is everywhere in tech conversations, but it’s clear that the definition of an AI agent is far from unified. What exactly does it mean? Many CEOs are talking about agentic AI’s transformative potential. However, the lack of a clear, standardized definition of an “AI agent” hinders its effective use in legal work, especially when we are applying these systems in real-world industries such as legal technology.
This ambiguity breeds confusion, discourages adoption and makes it difficult for legal teams to assess performance, ensure reliability, and set realistic expectations. For AI agents to truly thrive, we need a clearer understanding of what they can and can’t do. We must move beyond buzzwords and offering concrete, technical explanations of their operational stages, differences, and applicability.
AI Agents in Legal Tech
When people refer to an “AI agent,” they generally define it by its ability to perform multiple decisions autonomously and take action. Unlike a simple language model, it can independently interact with various systems to achieve a goal. These agents can range from simple programs performing automated tasks to sophisticated models that learn, reason, and interact with complex environments to make decisions and execute plans step by step.
The level of autonomy in AI agents is not always clear. While more than just a language model that repeats information, an AI agent represents increasing independence in reasoning and decision-making. For example, in the context of legal contract management, basic AI can extract dates from a contract. However, a more advanced agent can analyze those dates within the contract’s context. Then identify crucial deadlines, flag potential conflicts with other agreements, and even draft a corresponding agreement letter.
While sophisticated AI agents can incorporate features like retrieval-augmented generation (RAG) for real-time information access, tool integration for external system interaction, memory systems for contextual awareness, and multi-modal processing for diverse data handling, these are not defining characteristics of all agentic AI. True agency lies in an AI’s ability to act autonomously and make decisions to achieve specific goals.
For instance, a basic yet agentic function in legal contract management could involve an AI that automatically drafts and sends a reminder email upon identifying an upcoming contract expiration date. More complex agents might access current regulatory information, use tool integration to update a contract management system, employ memory to track communication history, or process scanned documents using multi-modal processing.
However, the core of agency remains the capacity for independent action and decision-making, regardless of the sophistication of these additional features. Let’s breakdown what each of these agentic features can do, by looking at real life use cases:
Tool Integration
Tool integration enables the agent to interact with external systems through APIs, performing specialized tasks. For example, an agent reviewing a software licensing agreement with RAG could access the most recent versions of relevant open-source licenses and flag any inconsistencies in a contract. Tool integration could automatically check a vendor’s compliance status against a third-party database or populate a contract lifecycle management system with key metadata.
Memory Systems
Implementing memory systems allows an agent to maintain context across interactions, personalize responses, and support long-running tasks. Instead of treating each interaction as a one-off, the agent can build on past exchanges, enabling a much more efficient workflow. An agent could manage a series of multiple contracts for a large project by inferred custom searches based on a natural language query.
Then, instead of analyzing each contract individually, the agent could remember past negotiations, amendments, and communications, allowing it to identify potential conflicts or inconsistencies across the entire project. It means that it can better understand a client’s specific negotiation style and preferred clause languages to speed up the contract process.
Multi-modal processing
With multi-modal processing, things get even more interesting. Agents can process diverse inputs – like text, images, and tables – and provide output in multiple formats. This richer exchange gives agents a deeper understanding of complex legal scenarios.
For instance, an agent could scan and interpret a handwritten contract amendment, pull out the key terms, and automatically update the digital document. It could also read financial tables embedded within contracts, calculate payment schedules, or even analyze visual project milestone diagrams – ensuring it understands the entire context of an agreement, not just its written text.
The future of AI agents
Looking ahead, the future of AI agents lies in advanced architectures – ones that feature chain-of-thought processing, dynamic tool selection, and self-monitoring abilities. Chain-of-thought processing allows the agent to break down complex problems into smaller, manageable steps. Dynamic tool selection enables the agent to choose the most appropriate tools for a given task. Self-monitoring capabilities allow the agent to identify and correct errors. In the legal field, this evolution could go beyond simple document review.
For example, instead of extracting dates from a contract, an AI agent or chatbot could be asked to analyze them, cross-reference them with other contract terms, generate a workflow and automatically generate reminders or alerts related to upcoming deadlines or renewals. That involves the agent taking several steps:
- understanding the initial request,
- accessing and processing relevant data (like contract databases or table schemas),
- formulating queries,
- executing searches, and
- acting on the results (e.g., sending emails, updating records).
The key is the agent’s capacity to perform these actions and make decisions independently, often involving multiple sub-tasks or processes.
For example, an agent could research data privacy regulations for a SaaS agreement, draft data security and liability clauses, and then negotiate payment terms with a vendor, keeping track of the company’s preferred contract language and risk mitigation strategies.
Functional AI agents to augment human expertise
In conclusion, while the term “AI agent” sparks considerable excitement in legal tech. The current lack of a clear and consistent definition poses a significant hurdle to its practical application. For in-house legal teams, the promise of AI lies in augmenting, not replacing, human expertise. They will allow lawyers to concentrate on strategic thinking and broader business engagement. This vision includes AI handling routine tasks, analyzing complex data, and streamlining processes like contract review and compliance checks.
However, realizing this potential necessitates moving beyond basic LLMs towards a more precise understanding and development of sophisticated, truly agentic AI. Without a shared definition, legal teams will continue to face challenges in evaluating suitability, measuring success, ensuring reliability, and maintaining compliance.
Therefore, progress should focus on practical, incremental implementations – such as automating contract review or compliance reporting – while concurrently working towards a clearer, more technical understanding of what constitutes an AI agent, emphasizing its capacity for autonomous action and decision-making. The future of legal technology hinges not just on the integration of these evolving AI agents, but on a shared understanding of their capabilities, fostering a genuine collaboration where technology truly enhances human judgment and expertise, creating meaningful advancements within the industry.
Agiloft is the global value leader in data-first contract lifecycle management (CLM), delivering the only no-code platform with AI on the Inside™ – a fully embedded, configurable AI that enhances efficiency, reduces review times by up to 80%, and accelerates business without the need for additional configuration. Designed to transform contracts into strategic data-rich assets, Agiloft’s Data-first Agreement Platform (DAP) seamlessly connects contract data to over 1,000 business-critical systems, driving operational efficiencies and informed decision-making.
Trusted by global brands like Alkermes, Balluff, Bissel, FuelCell Energy, RUSH, and TaylorMade, Agiloft maintains a 96% customer renewal rate and a 100% satisfaction rating for implementations, ensuring organizations achieve measurable value from day one. Backed by KKR, JMI Equity, and FTV Capital, Agiloft integrates AI at its core, enabling data-informed contract reviews, instantaneous approvals, and risk analysis – empowering businesses to transform contracting into strategic advantages for growth. Learn more at www.agiloft.com.
