Agentic AI: The Next Stage for AI Integration

At Future Workforce, our goal is to bring you the latest developments in AI and digital technologies, helping you adapt your business to the trends and preparing you for the future. In this article, we’ll define Agentic AI, explain how it works (and how well it works), and give insight on its benefits for your industry.

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What is Agentic AI?

Definition: “An AI system that can pursue set goals by carrying out actions autonomously, over an extended time period, without the need for constant human oversight or pre-defined behaviours.”

Agentic AI, also known as AI Agents, Agentic Automation, and Agentic LLMs – among other names – is a contemporary AI concept. Goal-oriented and adaptive, Agentic AI aims to take the need for human processing out of the equation – executing actions automatically to achieve a specified goal. There are different models of Agentic AI already on the market, with versions like Agent-Eand AgentGen claiming to have specifically enhanced planning capabilities.

Built on LLMs, Agentic AI offer the next level in AI capabilities. They can be given a written goal or a set of actions, and output a list of steps to take in order to achieve this goal. By integrating it with external systems, the Agentic AI can feed the results it receives back into the relevant channels, informing future outputs and driving towards the ultimate objective.

Unlike traditional models that tend to give a 1:1 ratio of input prompt to output, Agentic AI offers a wider range of capabilities, such as:

• Decision making
• Planning
• Learning from its experiences
• Multi-iteration execution

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How does Agentic AI Work?

Agentic AI works via “chaining” – taking multiple steps to achieve a single request by breaking a task down into its individual components. This makes it easier to adapt along the way, ensuring that unexpected variables can be accounted for and overcome in order to achieve the objective.

The outputs of Agentic AI are typically defined text actions that are formatted in JSON. These outputs can then be fed into integrated systems, which can then execute an appropriate response and feed the results back into the loop. Eventually, this chaining process will lead to either the task being completed, or a detailed history as to why it couldn’t be achieved.

In theory, any LLM could be programmed to become an Agentic AI, using the Few-Shot Learning (FSL) framework to build an understanding of the process involved, and the output the prompter is looking for. This can then be further refined to achieve the goals and work with the processes of your systems.

Few-Shot Learning: A Machine Learning (ML) framework, enabling an AI model to build an understanding of data with access to limited data sets.

Generally, any LLM can use tools in this way, requiring only a description of how to do so as part of the prompt (which can be done via a Few-Shot Learning (FSL) framework). This can be further fine-tuned for specific goals and processes, and connected to your systems through a process that OpenAI calls “function calling”.

Function calling has been supported since June 2023, but hasn’t been properly utilised until recently where businesses discovered the iterative, self-perpetuating planning functionality that can be achieved via further exploration of the concept.

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Is Agentic AI Effective?

When it comes to the effectiveness of Agentic AI, perceptions are still in the air. It is a young technology, so the true impact won’t have been fully explored or revealed.

For example, the extent of Agentic AI’s dynamic planning is disputed; a paper on LLM planning capabilities says sides are split between overly-optimistic views that LLMs can plan and reason “with just the right prompting or self-verification strategies” and overly-pessimistic views that “all that LLMs are good for in planning/reasoning tasks are as mere translators of the problem specification from one syntactic format to another”.

Meanwhile, another paper found that the best current model for autonomous planning, GPT-4, had an average success rate of 12% across all domains – showing how Agentic AI is in its infancy, but does have the capacity to succeed.

All in all, Agentic AI is a growing trend to keep an eye on – but maybe not one to hand over the reins to your entire business just yet.

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The Impact of Agentic AI Across Industries

Agentic AI comes with all the benefits of normal AI, along with exclusive advantages and opportunities unveiled by its multi-stage chaining process. Some core benefits of Agentic AI include optimising your key business processes; combining the strengths of humans and technology; and providing measurable improvements to your accuracy, overheads, and service quality.

At the same time, Agentic AI can also offer unique benefits to a huge number of industries. Here is a quick rundown of how Agentic AI is affecting various industry processes, helping identify where it could make an impact on your operations:

Banking and Finance

• Responsive decision making for trading and investments
• More efficient market interactions with the potential for much higher returns
• In-depth analysis of market trends that can by dynamically fed back into investment strategies
• Administrative support for banking processes like loan applications, research, reporting, and accounts
• Fraud identification through pattern recognition and analysis of data sets large and small

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Cybersecurity

• Monitoring network traffic, usage, and detecting anomalies
• Responding to cyber threats – in real time, without constant human oversight
• Enhancing security while freeing up human experts to focus on more complex challenges

Retail

• Autonomously managing supply chains
• Optimising inventory levels and forecasting demand
• Handling complex logistics planning
• Boosting human resources, customer service capabilities, and complaint escalation

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Logistics

• Processing vast amount of logistical data
• Making real-time decisions for supply chain routing
• Significantly improving operational efficiency
• Reducing costs for shipping and deployment

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Insurance

• Identifying and classifying the damage type in a claim
• Gathering missing information or highlighting its absence
• Automating responses at different stages in the process, based on human decisions
• Providing a clear chain of documentation for every step involved

Learn how we’re helping insurance businesses adapt to the AI revolution >

Ethical Challenges with Agentic AI

The main ethical challenge of Agentic AI is the reduction in human oversight, and the increase in automation.

To properly implement it in live business operations, guardrails need to be put in place to prevent the unethical use of data, as well as limiting access to human elements – especially externally – and to seek human intervention when necessary.

Security

Another consideration from the cybersecurity front is the potential for prompt injection, attacks via third-party channels, and privilege escalation (gaining access to actions and data that the user doesn’t ordinarily have the rights to) – threats that exist for regular AI, but could be compounded by the adaptable, multi-stage, and hands-free nature of Agentic AI.

Ethics

Ethical Agentic AI should have its decision making aligned with a proper code of conduct and values. It must be able to demonstrate reasoning for its decisions, helping to break down the often-obfuscated decision-making process of AI.

Accountability

There also need to be accountability for the decisions of Agentic AI, particularly when involving data privacy, security, and sensitive information. Businesses will be accountable for their AI’s actions, even when they don’t actively initiate each individual task in the Agentic AI sequence.

Employment

Finally, businesses must take into account the potential impact of Agentic AI on the job market. Training must be enacted to ensure the workforce can adapt to the changing technology, and to utilise AI to enhance their work rather than to act as their replacements. This is further compounded by the need of entry-level support and training to build foundational knowledge required to progress to higher-level, more strategic positions that can’t be replicated by an AI model.

Explore more ethical considerations for AI with regards to banking and finance >

Implement the Latest AI Innovations with Future Workforce

Agentic AI – a productivity master, but only when implemented with the oversight to handle ethical concerns, permissions, and limitations. For a smooth integration into your processes that enhances your productivity, reduces overheads, and leads to greater returns, Future Workforce have your solution – just get in touch with our expert team to begin!

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