What Is Agentic AI?

One of the most frequent questions, “What is agentic AI,” provides an opportunity to explore advancement in artificial intelligence. How better to do that than with the assistance of a generative AI Large Language Model (LLM) that did a fantastic job providing the framework for this blog post? The information below looks at the evolution of generative, extractive and agentic AI; its impact on various industries; how agentic AI provides the foundation for personal assistants in the workplace; a bit on the impact to children and students; and more understanding of this evolving world. Of note and as a reminder, the information below was provided by a large language model with human editing.

What Is Agentic AI

An AI agent in agentic AI refers to a software entity that operates autonomously to perform tasks, make decisions, and take actions based on its programming and learning from data. Unlike more passive AI systems that only respond when prompted, such as generative AI like the AI used to help draft this blog, AI agents can take initiative and perform sequences of actions to complete tasks and achieve specific goals.

These agents are designed to understand their environment, interact with it, and adapt to changes, often without direct human intervention. They can process information, learn from experiences, and execute tasks to achieve specific goals, making them capable of functioning independently in various applications.

The level of agency varies significantly between different systems. These systems are designed to perform tasks, solve problems, or achieve goals by interpreting data, learning from experiences, and adapting to new situations. Some require frequent human oversight and confirmation, while more advanced agents can handle complex tasks with minimal supervision.

AI agents typically perform tasks like:

• Scheduling meetings and managing calendars
• Searching for information and conducting research
• Automating complex workflows
• Monitoring systems and responding to events
• Acting as virtual assistants for specific domains

Common Characteristics of AI Agents

The most common characteristics of AI agents include:

• Goal-directed behavior – a goal is put forth and the agent works toward specific objectives. Further clarification with the LLM provided this, “The goal-directed behavior of an agentic AI typically requires initial human input to define objectives, parameters and constraints within which the AI operates. This input helps guide the AI's decision-making processes and actions towards achieving specific goals.
• Autonomy – the agent makes some decisions without human intervention. This is by far the most intriguing aspect of agentic AI. Here are some examples from various experiences as reference:
  • Autonomous Vehicles: Navigating traffic, making lane changes and adjusting speed based on road conditions and traffic signals.
  • Robotic Process Automation: Automatically processing invoices, updating databases and generating reports without human oversight.
  • Smart Home Systems: Adjusting lighting, temperature and security settings based on user habits and environmental conditions.
  • Financial Trading Algorithms: Executing trades based on market analysis and pre-set strategies without human intervention.
  • Healthcare Monitoring Systems: Adjusting medication dosages or alerting medical staff based on patient data and health trends.

• Environment interaction – AI agents “can perceive their environment and take actions that affect it,” says the LLM. When asked for clarification about the meaning, here is how the LLM replied, “Agentic AI's ability to interact with its environment is a key characteristic that enables it to perceive and respond to changes or stimuli within its surroundings. This involves using sensors or data inputs to gather information about the environment, processing this information to understand the context, and then making decisions or taking actions that can influence or alter the environment. This capability allows agentic AI to adapt to new situations, optimize its performance, and achieve specific goals autonomously, making it effective in dynamic and complex settings.”
• Persistence – Agentic AI operates over extended periods. The LLM was asked for clarification, and it responded this way, “Agentic AI's persistence and operation over extended periods refer to its ability to continuously perform tasks and make decisions without frequent human intervention. This means it can maintain its functions and adapt to changes in its environment over long durations, such as days, weeks, or even months, depending on the specific application and system design. The exact timeframe can vary based on the complexity of the tasks, the robustness of the AI system, and the level of autonomy it is designed to have.”

Evolution of Artificial Intelligence

The evolution of artificial intelligence has been rapid, driven by advancements in machine learning, data availability and computational power. This rapid development has led to significant improvements in AI capabilities, from basic data processing to complex decision-making and autonomous actions. Researchers work to balance autonomy with safety and alignment with human intentions. The evolution of AI can be traced through three significant phases: extractive, generative and agentic AI:

Extractive AI

This phase began in the mid-20th century with the development of early AI systems focused on data retrieval and processing information from large datasets, often used in search engines and data analysis. Key milestones include:

• 1950s to 1960s: The inception of AI as a field, with early work on algorithms for data processing and problem-solving.
• 1990s: The rise of search engines like Google, which utilized extractive AI to index and retrieve information from the web.

Generative AI

This phase emerged as AI systems, like GPT-3 and GPT-4 began to create new content by learning patterns from existing data. The new content included text, images or music. Notable developments include:

• 2014: The introduction of Generative Adversarial Networks (GANs) by Ian Goodfellow which enabled the generation of realistic images and other media.
• 2018-2020: The release of OpenAI's GPT-2 and GPT-3 which demonstrated advanced capabilities in generating human-like text.

Agentic AI

The current and evolving phase, where AI systems not only generate content but also make autonomous decisions and take actions, represents the next step, based on their understanding and objectives. Key points include:

• 2020s: The development and deployment of autonomous systems, such as self-driving cars and advanced robotics which exemplify agentic AI's decision-making capabilities. Ongoing advancements in AI research continue to enhance the autonomy and adaptability of agentic AI systems across various industries.
  These phases illustrate the progression from simple data processing to complex decision-making and autonomous action, reflecting the rapid evolution of AI technology.

Agentic AI In the Legal Field

Agentic AI in the legal field can significantly enhance efficiency and accuracy in various processes. It can automate routine tasks such as document review, contract analysis, and legal research, allowing legal professionals to focus on more complex and strategic work. Agentic AI can also assist in predicting case outcomes by analyzing past case data and identifying patterns, which can be invaluable for legal strategy development.

Additionally, it can help in compliance monitoring by continuously scanning and interpreting regulatory changes, ensuring that organizations remain compliant with the latest legal requirements. This application of agentic AI not only saves time and reduces costs but also minimizes human error, leading to more reliable and effective legal services.

Technology providers in the legal field leverage agentic AI to enhance legal research and analytics capabilities. AI technologies integrate to automate and streamline legal processes, such as document review, case analysis, and compliance monitoring. The use of agentic AI provides legal professionals with more efficient tools for decision-making, reducing the time spent on routine tasks and improving the accuracy of legal insights. This approach helps legal practitioners focus on more strategic aspects of their work while ensuring they have access to comprehensive and up-to-date legal information.

Agentic AI in the legal field is rapidly evolving, driven by the need for increased efficiency and accuracy in legal processes. However, there is some push-back regarding its adoption, primarily due to concerns about data privacy, ethical implications, and the potential for job displacement. Additionally, the legal industry is traditionally conservative, which can slow the adoption of new technologies.

Corporate legal departments are often quicker to adopt agentic AI compared to law firms. This is because corporate legal teams are typically more focused on cost reduction and efficiency, and they have the resources to invest in advanced technologies. Law firms, on the other hand, may be more cautious due to concerns about maintaining the quality of legal services and the potential impact on billable hours. Nonetheless, as the benefits of agentic AI become more apparent, adoption is expected to increase across both sectors.

For legal operations professionals, agentic AI can streamline workflow management, automate routine administrative tasks, and provide data-driven insights for decision-making. This aspect of agentic AI is important. As legal departments scale, data-driven decision-making becomes a critical component of maturity, growth and credibility throughout the enterprise. Both legal teams and legal operations professionals can benefit from agentic AI, as it enhances efficiency, reduces administrative burdens and allows them to focus on more strategic and complex tasks.

Impact of Agentic AI on the Legal Field

Here are many ways agentic AI is impacting the legal field:

• Contract Analysis: Automatically reviewing and flagging potential issues or inconsistencies in legal contracts based on predefined criteria. Contract lifecycle management is slated to explode with blockchain and agentic AI.
• Legal Research: Identifying relevant case law and statutes for a particular legal issue without human input, streamlining the research process.
• Compliance Monitoring: Continuously scanning for regulatory changes and updating compliance protocols accordingly.
• Case Outcome Prediction: Analyzing historical case data to predict the likely outcome of ongoing cases, aiding in legal strategy development.
• Document Drafting: Generating initial drafts of legal documents, such as wills or non-disclosure agreements, based on standard templates and client inputs. Applying this type of artificial intelligence in the legal department is aided by enterprise legal management software. Within the platform, the legal team can implement AI assistants into work intake or self-serve wills or non-disclosure agreements for stakeholders.
• Personal Assistant: Managing schedules, organizing meetings and sending reminders for deadlines and court dates. It can also assist in prioritizing tasks, tracking billable hours and managing client communications.

Using Agentic AI as a Personal Assistant

As a personal assistant, briefly mentioned above, agentic AI, used in products like LexisNexis Protege, leverages its ability to learn from interactions, data, and patterns to perform tasks autonomously. It can manage schedules, send reminders, organize emails, and even make travel arrangements by understanding user preferences and behaviors. By analyzing past interactions and data, agentic AI can deduce human behavior and patterns, allowing it to anticipate needs and make informed decisions. This capability enables it to provide personalized recommendations and take proactive actions, to enhance user experience and efficiency.

To ensure that AI is credible, especially for companies purchasing it for automation or as a personal assistant, consider the following criteria:

• Accuracy and Reliability: Evaluate the AI's performance in terms of accuracy and reliability in completing tasks. Look for case studies or testimonials that demonstrate its effectiveness in real-world applications.
• Transparency: Choose AI systems that offer transparency in their decision-making processes. This includes clear documentation and the ability to audit AI decisions to understand how conclusions are reached.
• Security and Privacy: Ensure that the AI system adheres to robust security protocols and data privacy standards to protect sensitive information.
• Scalability: Assess whether the AI can scale with your business needs, handling increased workloads or additional functionalities as required.
• Integration Capabilities: Verify that the AI can seamlessly integrate with existing systems and workflows, minimizing disruption and maximizing efficiency.
• Vendor Reputation: Consider the reputation and track record of the AI vendor. Established companies with a history of successful deployments are often more reliable.
• Support and Maintenance: Ensure that the vendor provides comprehensive support and regular updates to keep the AI system functioning optimally and adapting to new challenges.
• Ethical Considerations: Evaluate the ethical implications of the AI's use, ensuring it aligns with your company's values and does not perpetuate biases or unethical practices.
• User Feedback and Adaptability: Look for AI systems that incorporate user feedback and can adapt to changing requirements or environments, ensuring long-term relevance and utility.

AI agents can be trusted to work independently to a certain extent, but human oversight is often necessary to ensure they operate within ethical and operational boundaries. To instill credibility in an AI agent, humans should:

• Set Clear Guidelines: Define the scope and limitations of the AI's tasks to ensure it operates within acceptable parameters.
• Regular Monitoring: Continuously monitor the AI's performance and decision-making processes to catch any anomalies or errors early.
• Feedback Mechanisms: Implement feedback loops where the AI can learn from human input and improve its performance over time.
• Transparency and Explainability: Ensure the AI's decision-making processes are transparent and understandable, allowing humans to audit and verify its actions.

Benefits of Agentic AI

The benefits of agentic AI include increased efficiency and productivity, as these systems can automate complex tasks and make decisions faster than humans. They can also operate in environments that are dangerous or inaccessible to humans, such as deep-sea exploration or space missions. Additionally, agentic AI can enhance decision-making processes by providing insights and recommendations based on vast amounts of data, leading to more informed and effective outcomes.

On the other side of the coin, the LLM used to develop the foundation of this blog was asked about the agent advancing in decision-making without human influence which leads to error. Here’s how the LLM replied, “Yes, in agentic AI, there can be instances where the agent makes an error in decision-making without human influence. This can occur due to several factors, such as incomplete or biased data, unforeseen environmental changes, or limitations in the AI's algorithms. These errors highlight the importance of implementing robust monitoring systems, regular audits, and feedback mechanisms to ensure the AI operates within acceptable boundaries and can be corrected or adjusted as needed.”

The best users of agentic AI are typically industries and sectors that require high levels of automation and decision-making capabilities, such as finance, healthcare, manufacturing, and autonomous vehicles. Many of the largest tech companies provide services and updates to improve AI capabilities, often through cloud-based platforms and AI development tools. These companies continuously enhance their AI systems to function effectively as personal assistants or in other specialized roles.

Training An AI Agent

Training an AI agent is similar to training generative AI in that it involves feeding it data and refining its algorithms through supervised or unsupervised learning. However, agentic AI also requires reinforcement learning, where the agent learns from interactions with its environment and adjusts its actions based on feedback.

An agentic AI personal assistant improves its learning by:

• Analyzing User Behavior: Observing and learning from user interactions to better understand preferences and habits.
• Incorporating Feedback: Using user feedback to refine its responses and actions.
• Continuous Learning: Updating its knowledge base with new information and adapting to changes in user needs or environments.
• Task Automation: Gradually taking over repetitive tasks and optimizing processes to enhance efficiency and productivity.

Looking ahead with a crystal ball to predict the evolution of AI refers to future developments and the impact of AI technology. The LLM was asked about the impact of AI on humans in the workforce. It said, “While AI holds the promise of transforming industries and improving efficiency, it also raises concerns about job displacement. While AI can automate certain tasks, it is unlikely to completely make humans obsolete in the workforce. Instead, it is expected to change the nature of work, creating new roles and opportunities while automating repetitive and mundane tasks. The focus will likely shift towards human-AI collaboration, where AI augments human capabilities rather than replacing them entirely.”

In the development of this article on agentic AI, nearly 40 questions were posed to the LLM to provide a thorough framework for understanding this revolutionary landscape. Significant human editing occurred to polish the content with transitions, train of thought, sentence structure (because the LLM prefers concise bullets), and context. Some parts were deleted because of the question and answer did not fit the overall theme of the article. 
Contact us to learn more about agentic AI as a personal legal assistant.