AI agents are changing the way we interact with AI engines as well as with the world around us. Smart AI-based applications are designed to communicate with the defined business environment, collect data, and perform tasks independently to achieve pre-set goals.
The concept of autonomous AI agents – LLM-driven bots capable of thinking and completing tasks independently – gained tremendous momentum in 2023. Now, the field is flourishing. Research firm CB Insights reports that more than 50 startups have been developing and delivering smart AI agents since 2022, a number expected to rise, possibly even doubling in the coming year.
The Marker recently addressed this trend: “Chatbots like ChatGPT are already a thing of the past. The future lies in AI agents. While chatbots pull answers out of a hat, AI agents are trained to work carefully and methodically to deliver more accurate results.”
Enterprise organizations in Israel are developing smart agents for their own use or to sell to clients, and more and more startups focused on AI agents are emerging. Globally, there are already startups raising significant funds, such as Cognition AI from San Francisco, which is developing an AI agent for programmers. The company was founded only in November 2023 and has already raised $200 million, with a valuation of $2 billion in the latest round.
Although humans define the business goals and objectives, the AI agent is the one that independently chooses the best actions to meet the defined needs. This autonomy makes AI agents highly flexible and particularly useful across a wide range of applications—from customer service to content creation, and beyond. It is no surprise that they are expected to disrupt business operations across various industries. Below are a few examples and explanations of AI agents and how they might impact the business reality for all of us:
Let’s start with the basics: What are AI Agents?
AI agents are software applications powered by artificial intelligence, designed to perform specific tasks independently or with minimal human intervention. Unlike traditional AI systems, which primarily focus on data analysis or providing recommendations, AI agents are action-oriented. They can execute a series of tasks, often integrating multiple systems, to achieve a desired outcome (always defined by a human).
The main characteristics of AI agents:
- Independence: AI agents operate independently once given a task, often making decisions and taking actions without human involvement.
- Task-oriented: These agents are built to complete specific tasks, ranging from customer interactions and solving complex technical issues to HR functions, like onboarding new employees.
- System integration: AI agents often bridge different software systems, pulling data from one system, processing it, and then acting within another system, seamlessly integrating different aspects of the workflow.
- Learning and adaptation: Over time, AI agents can learn from their interactions and experiences, improving their efficiency and effectiveness in completing tasks.
- Scalability: AI agents can handle large volumes of tasks simultaneously, mking them highly scalable solutions for businesses looking to automate repetitive or complex processes.
Examples of AI agent use cases:
- Customer service: AI agents can engage in conversations with customers, process their requests, and offer product or service recommendations, reducing the need for human intervention.
- Human resources: AI agents can automate processes like resume screening, scheduling interviews, and onboarding new employees.
- Technical support (IT): AI agents can diagnose and solve technical issues, install updates, and even manage security protocols. For example, according to The Marker, the startup Atera, which develops a system for IT support teams in organizations, has created a virtual representative that automatically resolves some service requests, such as users needing password resets. Atera started working on such a system as early as 2017, but without satisfactory results. The breakthrough came only with the new language models. Today, Atera markets an agent installed on users’ computers that provides automatic solutions to problems like programs not opening or a slow computer. Atera’s agent can run diagnostics to identify the cause of the slowdown and then make the necessary changes to the computing systems.
So far, we’ve covered the main characteristics and a few practical and business use cases for the near future. But let’s also talk about the future potential:
As AI-based technologies advance and evolve, AI agents are expected to take on increasingly complex tasks and might even manage entire business processes or make strategic decisions entirely autonomously. They could evolve to handle tasks that require more contextual sensitivity, such as personalized marketing campaigns, complex research, or even medical diagnoses.
Challenges and Considerations:
- Ethical considerations: As AI agents become more independent, ethical concerns regarding decision-making, privacy, and accountability will arise.
- Integration: The integration of AI agents with existing systems and workflows could create business, operational, and legal challenges, and may require significant technical adjustments by the organizations.
- Human oversight: It’s important to ensure that AI agnets operate within desired parameters and align with the company’s values and goals. Most organizations today incorporate a layer of human management and monitoring to ensure that AI activities meet the business standards representing the organization.
In conclusion, while excitement around AI agents is high at the moment, the tools in this field are still in their early stages, and their commercial potential has yet to be proven. This potential depends on their ability to deliver reliable, high-quality, and secure results that people and organizations can trust.
Every company focusing on this area is seeking its own solutions to achieve such results. For example, at Atera, they limit their AI agent to tasks where it cannot cause harm to the organization’s systems. Enso, on the other hand, tailors each AI agent they develop to a specific industry. An agent designed to write blog posts for lawyers, for instance, is directed only to legal databases—and not the entire internet—to reduce the chance of including unrelated information.
At Strauss, we will continue to follow this hot trend and share updates on new developments through our channels.