Agentic AI is driving the evolution of enterprise AI, transforming how businesses perform crucial operations. At the core of Agentic AI is the concept that artificial intelligence can seamlessly integrate into business operations akin to an employee, actively participating in strategic planning and driving company growth. 

At Clairo AI, we empower our users to integrate Agentic AI capabilities into their enterprise AI strategies by creating agents tailored for specific business tasks and operations. With Clairo AI, businesses can harness the power of Agentic AI to foster growth, improve customer experiences, and streamline operations, ensuring they remain at the forefront of technological progress.

What are Agentic AI systems, and how do they differ from Generative AI?

Many are saying that the future of Generative AI is agentic. However, Generative AI and Agentic AI do not completely overlap, and they have some key distinctions. The primary difference between Agentic AI and Generative AI lies in their autonomy when carrying out tasks. 

Stripping it back, Agentic AI refers to artificial intelligence systems designed to perform tasks mostly independent from human involvement, often with a goal-oriented approach. OpenAI’s 2023 research paper, Practices for Governing Agentic AI Systems, defines agenticness as “the degree to which a system can adaptably achieve complex goals in complex environments with limited direct supervision.” These AI agents can perceive their environment, make decisions based on input data, and act independently to achieve specific objectives. They are often embedded in applications requiring decision-making, problem-solving, and adaptive behaviour. Comparatively, the Generative AI tools that have gained widespread popularity, like ChatGPT, are able to answer a huge breadth of questions and provide knowledge to a user based on the input data they have been trained on, they are limited in their ability to perform real-word tasks. 

What are the core features of Agentic AI?

1. Autonomous Decision-Making: Agentic AI systems are designed to make real-time decisions without human intervention. This capability is crucial in applications requiring immediate responses and complex decision-making. For example, in the legal field, an AI agent can be designed to perform contract analysis that is specifically designed to find and address biassed or risky language, identify non-compliance issues, and suggest modifications.
2. Goal-Oriented Behavior: These systems operate with specific goals in mind, and they work towards achieving these predefined objectives. For example, in healthcare, an AI agent may assist in diagnosing diseases and recommending treatments.
3. Adaptability: Agentic AI can learn from interactions and adapt to new situations, making it suitable for changing and unpredictable environments like financial markets or emergency response scenarios.
4. Operational Efficiency: By automating both routine and complex tasks, Agentic AI can significantly enhance efficiency and productivity in various sectors, from industrial automation to customer service.

How can you leverage Agentic AI in your industry?

Agentic AI in Healthcare:

An example of how Agentic AI can be deployed in the healthcare industry surrounds the management of patient records. An agent is able to autonomously analyse vast amounts of patient data from electronic health records, ensuring real-time updates and guaranteeing accuracy. Additionally, it can predict health risks by analysing patterns in the data, providing early warnings for conditions and illnesses. Healthcare agents can therefore ensure patient safety and automate administrative tasks, enabling healthcare professionals to focus more on patient care rather than administrative tasks.

Agentic AI in the Legal Industry:

In the legal industry, Agentic AI can be deployed for legal document parsing. The agent is able to autonomously review contracts for compliance, identifying risky clauses and suggesting amendments based on the latest legal regulations and best practices. This system not only speeds up the legal research process and reduces the risk of human error but also ensures that the firm's legal documents are always up-to-date and in compliance with current laws.

Agentic AI in the Financial Sector:

A financial services firm can utilise Agentic AI to optimise its portfolio management and fraud detection processes. The Agent is able to autonomously monitor and analyse vast datasets, including market trends, financial news, and individual client portfolios in real-time. Leveraging predictive analytics when managing portfolios, the agent is able to identify investment opportunities and risks, automatically rebalancing portfolios to optimise returns while managing risk exposure. This can therefore enable the firm to offer personalised investment strategies tailored to each client's financial goals and risk tolerance.

Agentic AI in Customer Service:

In customer service, agentic AI can revolutionise operations by handling inquiries efficiently and with precision. For instance, in an e-commerce setting, an AI agent can engage with customers through chat interfaces, swiftly addressing queries about product details, order statuses, and FAQs using natural language processing. It can personalise interactions based on customer history, recommend products, and escalate more complex issues to human agents as needed. This AI capability guarantees availability, scalability during peak times, and continuous improvement through feedback analysis, ultimately enhancing customer satisfaction by providing instant, accurate responses while optimising human resources for more specialised tasks.

Drawbacks of Agentic AI

Despite its numerous benefits, Agentic AI also has its drawbacks. The adoption of new AI approaches comes with concerns about its safe and ethical adoption, and Agentic AI is no different. Ensuring that these systems make decisions that align with human values and do not cause harm is a significant challenge. There are concerns about accountability and transparency, particularly when AI agents make critical decisions without human oversight. OpenAI’s aforementioned paper outlines the most important practices for the safe adoption of these tools. The paper emphasises the importance of establishing foundational and across-the-board safety practices for all those who might be working with agents in order to foment best practices.

Moreover, like most AI services, cost - both economical and environmental - is a factor due to the complexity of training these systems. Clairo AI takes cost out of the equation. By utilising sustainable data centres, we are able to keep costs down for our users and significantly reduce the carbon emissions traditionally associated with running GPUs. Our secure system also ensures that your business’ sensitive, proprietary data never escapes the platform. You can be sure that your agents are making the most informed decisions without compromising the integrity of your data.