AI Agents: Are You Missing Out?

AI agents can be described as autonomous software systems able to perform tasks, make independent decisions and interact with their environment without constant human involvement. They us AI to learn and adapt to new situations to perform specific functions or goals.

The key characteristics tend to include:

• Autonomy: They work independently and make decisions initially based on their programming, but also based on information from the environment around them.

• Reactive: They can respond to changes in their environment in real-time.

• Proactive: They can take their own initiative to achieve tasks and functions, rather than relying purely on human input or programmed instructions.

• Social: They can communicate with other agents or even humans when required.

How they work:

AI agents usually work through a number of steps that include:

1. Data acquisition: Gathering information from a number of sources such as the internet or databases, helping them to inform actionable decisions.

2. Cognitive processing: Including decision-making and selection, defining objectives and creating strategies to achieve objectives.

3. Goal determination: Receiving a specific instruction or goal from the user and breaking it down into smaller actionable tasks.

4. Perception: Processing input data using sensors or digital interfaces, and extracting meaning from them.

5. Action execution: Implementing decisions by either physical (motors and robotics) or virtual (software-driven) means.

6. Learning and adaption: Continuously improving their understanding through application of machine learning.

7. Interaction: The ability to communicate directly with humans, or conduct work in the background.

Agent Types:

Different variants of AI agents include

• Simple reflex

• Model-based reflex

• Goal based

• Utility-based

• Learning based

Like me, you may have wondered where the data comes from to feed AI agents. I was surprised to learn the breadth of sources AI agents can use to gain their data from. These can include:

• Documents, such as Word files, PDFs, text, guides etc.

• Video frame extraction and advanced visual analysis

• Cloud storage, such as AWS S3 or Google cloud

• Collaboration tools such as Notion

• Public internet sites

• Custom APIs

• Databases

• Audio files

• Websites

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Here are just some of the many applications of AI agents, showing how wide spread their use currently is, even at this early stage in their adoption.

Smart Devices and Homes:

• Voice controlled AI assistants such as Alexa.

• This can also be extended to smartphone assistants, which may soon become the most widely used applications, as our phones are always with us.

• AI thermostats and energy efficiency tools.

E-commerce:

• Image searches for products to help user discovery and purchasing decisions.

• Automatic recommendations based on individual user purchasing habits.

Finance:

• Automated trading tools which trade based on a multitude of timely data including sentiment and world events.

• Robo-advisers for a personalized financial management.

• Fraud detection.

Healthcare:

• AI-driven diagnosis for serious illnesses.

• Personalized treatment planning.

Autonomous vehicles:

• Autonomous cars and taxi services.

• Delivery drones.

Manufacturing:

• Robotic agents to manufacture and supplement specialized or dangerous labour tasks.

• Quality control to increase manufacturing efficiency.

Human Resources:

• AI-enabled recruitment and screening, reducing the burden on recruiters.

• Employee on-boarding assistance.

• Automated HR guidance.

As advancements in AI technology such as natural language processing and machine learning continue, we expect to see even more use-cases and new advancements in the capabilities of AI agents.

Whilst we may still be at the early innings of this technology, my frame of reference for success will be whether there is a measurable improvement in productivity, efficiency and creation of new economies as a result of new technology such as AI agents.

I’m excited and cautiously optimistic to see what an agentic AI future holds!

Many companies are putting agents to good use. One that came to my mind was Lemonade, a US-based InsureTech company turning the insurance industry on its head with an ai-first approach, seeking to give consumers more personalized treatment whilst lowering operating costs, which can be passed on to consumers.

So, how does Lemonade take advantage of AI agents?

Customer interaction

Customers can raise a claim by informing a chatbot about the incident leading to the claim, which can avoid the need to speak to a human or fill out forms.

This increases customer engagement, lowers cost to serve and reduces the time to pay out to the customer.

Underwriting

The AI agents automatically analyze the customers background, their lifestyle and as a result, their risk profile, potentially avoiding the need for time consuming medical tests. This again enables faster decision-making with lower cost to Lemonade.

Fraud management

Lemonade applies many fraud detection algorithms, which AI uses to assess and avoid possible fraudulent claims. This reduces the overall payout rates allowing Lemonade to offer highly competitive rates as a result.

Claims handling

• Lemonade’s AI chatbot, known as AI Jim is able to settle claims as quickly as two seconds. It does so by assessing claims, checking policy conditions and running anti-fraud checks before processing payments directly to customers.

• This ease of use and fast payment helps to improve customer retention and advocacy.

Customer acquisition and experience

AI algorithms help to identify possible new customers and also optimizes Lemonade’s approaches to retain them once on boarded.

AI agents allows Lemonade to tailor insurance products that are more targeted to individual needs, making the customer feel as though you know them personally.

In spite of having less human engagement with customers, Lemonade has a net promotor score of 708, which is considered high for the insurance industry, and has been ranked number 1 out of 270 companies for renters insurance by Clearsurance.

Such scores highlight that high quality user experience and personalization is no longer dependant on having high levels of human operators.

I’m also reminded that the bar is continually rising for teams who build software or innovative technology, highlighting the need for teams to consider how capability such as AI agents can improve their workflow and make lives more intuitive for their users.

This way of thinking is becoming increasingly essential to remain competitive.

Google Introduces Gemini 2.0 optimized for agentic AI.

SAP delivers AI agents that truly means business

Salesforce unveils Agentforce - what AI was meant to be

Sources

How to Build an Enterprise Agentic AI Strategy that Actually Delivers ROI

Why Agentic AI is the Next Big Thing in the Business Industry

Lemonade Masters AI for Insurance

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