HAL 9000 from 2001: A Space Odyssey, J.A.R.V.I.S. from the Marvel Cinematic Universe, and Skynet from The Terminator franchise were just figments of human imagination for decades—technology that we, till recently, considered “futuristic.” However, these autonomous systems that manage spaceships, labs, and even the whole world in these fictional universes are no longer merely the stuff of imagination.

The emergence of AI agents and agentic AI is revolutionizing most industries and is poised to change many aspects of modern life. As businesses look to streamline processes, boost efficiency, reduce costs, and find creative solutions to problems, these advanced forms of AI have emerged to fast-track progress. But what is agentic AI and how does it expand on other forms of AI?

Agentic AI explained

Generative AI (GenAI), a concept we have become familiar with over the last few years, is an AI tool used to create new content like text, images, videos, etc. based on prompts and input data (e.g., ChatGPT, DALL-E). Agentic AI, however, goes a few steps further by focusing on autonomous decision-making and task execution with minimal human intervention. These systems act as autonomous agents on behalf of human users and other AI systems. They understand objectives independently while making decisions, initiating actions, and setting priorities. They can even communicate and collaborate with other AI systems and digital infrastructure while learning from interactions and improving future responses.

While both GenAI and agentic AI represent significant advancements, the latter’s autonomy and complex decision-making capabilities makes it more suitable for diverse tasks requiring independent action and problem-solving.

As described in the Edge Insight on Agentic AI, AI agents, autonomous AI agents, and agentic AI display increasing levels of autonomy when carrying out tasks. Simply put, while AI agents and autonomous AI agents are like “specialized workers” with specific job descriptions and assigned tasks, agentic AI acts as a “senior executive” equipped with the necessary autonomy for broader decision-making and goal-setting.

Use cases and benefits of agentic AI

The agentic AI revolution is already sweeping across several industries, and its use cases are expanding rapidly. Though still early days, this tech has been used to transform industries by automating tasks, enhancing decision-making, and improving efficiency. Here are some of the industries and use cases we see today:

• Healthcare: To analyze patient data, adjust treatment plans, and predict health issues. Platforms like IBM Watson Health have been making strides in diagnostic assistance.
• Retail and ecommerce: To manage inventory placement and order fulfillment in warehouses autonomously. Amazon employs autonomous robots for this purpose, while fast-fashion retailer Zara uses agentic AI to predict demand fluctuations and streamline inventory management.
• Financial services: To automate data entry and compliance checks and process financial documents more efficiently (JPMorgan Chase’s CoiN platform). Some companies, like Two Sigma, also use AI-driven trading algorithms that analyze market data and make trading decisions.
• Transportation and logistics: Seen in self-driving car tech (Waymo’s autonomous vehicles) and supply chain optimization.
• Customer service: For intelligent chatbots and customer assistance (Zendesk’s offerings).
• Other use cases include optimizing manufacturing, cybersecurity threat detection, and energy management, among others.

Agentic AI promises consistent service quality, reduced response times, and cost savings while eliminating human errors and improving development cycles. For example, in critical industries like healthcare, it can deliver faster and more accurate diagnoses, better early detection and disease prevention, and reduced hospital readmissions. Furthermore, since these systems do not have to deal with human limitations like fatigue and sickness, they can provide 24x7 service.

Meanwhile, ongoing research surrounding the technology is focused on improving integration, its learning capabilities, and decision-making, opening it up for even broader adoption in the future.

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Key challenges and risks 

As revolutionary as the tech is, it does come with some challenges, and its teething period could prove decisive in terms of its reception and rate of adoption. Though a dystopian future with AI overlords may be a stretch, some of the underlying concerns are still very relevant in ensuring the tech’s success. At this stage, building trust is critical. 

Foremost among the concerns are the ethical and practical implications, with questions of accountability and transparency being raised. Who is responsible when an AI system makes a flawed decision? Though adoption is seemingly being fast-tracked, it will be crucial to ensure these systems adhere to ethical guidelines and are transparent in their decision-making. Many AI models, especially deep learning systems, function as “black boxes,” making it hard to understand how they arrive at decisions. This could lead to unintended biases and errors as well as difficulty in identifying root causes. Furthermore, since they aren’t inherently equipped to understand human values and morality, their decisions could contradict ethical standards. They could also inherit and amplify biases in their training data, perpetuating societal prejudices and actioning skewed tasks.

There are also safety risks—its adaptability, though one of its most useful features, can lead to unpredictable outcomes, while the vast amount of data it processes could pose data privacy and security risks.

It could also present challenges on a socioeconomic front, potentially resulting in large-scale job displacement and worsening economic inequality. The need for reskilling and adapting workforces as well as pushback from within organizations could further hamper adoption.

Deloitte predicts that 25% of companies using GenAI will launch agentic AI pilots or proofs of concept in 2025, rising to 50% by 2027. They even expect some cases of actual adoption in 2025. Meanwhile, Gartner predicts that at least 15% of daily work decisions will be made autonomously by agentic AI by 2028. It seems mainstream adoption is on the horizon, but ensuring it goes smoothly will require a measured approach, including ethical frameworks, robust governance, and continued research.

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Agentic AI Leaders

OpenAI CEO Sam Altman described AI agents as the next “giant breakthrough,” while senior executives hailed 2025 as “the year of AI agents.” As suggested, the space has been buzzing recently with several high-profile product launches, partnerships, and funding announcements.

Google’s launch of its enterprise AI service, Agentspace, was a significant announcement in the space. Currently available through an early access program, it is designed to create and deploy AI agents. This aligns with moves by other major tech players like Anthropic, Microsoft, and OpenAI as well. 

Furthermore, MultiOn’s announcement of AgentQ was also a highlight, with the company describing the tech as a major milestone for agentic AI, combining search, self-critique, and reinforcement learning to create autonomous web agents capable of planning and self-refinement.

Ema announced a series of partnerships, joining Microsoft’s Pegasus program and forming collaborations with major global enterprises to co-sell its AI agent. Meanwhile, Salesforce launched Agentforce and acquired AI agent startup Tenyx to expand its capabilities.

Investment in the space has also been heating up, with agentic AI startups raising more than USD 2 billion in the last two years. Some of the funding highlights include French AI startup H’s mammoth USD 220 million to develop AI models to boost worker productivity and Writer’s USD 200 million to accelerate enterprise-grade agentic AI development.

As mentioned in our State of Innovation report, which highlighted agentic AI as one of the top five technologies to look out for in 2025, we can expect an investment surge within the sector in the coming years, as companies look to launch new products. The Trump administration has also relaxed AI regulations, positively impacting the market and fostering AI development and foundation model creation.

In the meantime, some of the major players to look out for include OpenAI, which plans to launch an autonomous agent code-named “Operator” in 2025; Amazon, which established an R&D lab last December to develop foundational capabilities of AI agents; and Ema, which launched Universal AI Employee (for activating specialized AI roles) and Agentic Business Automation (AI agents that execute complex workflows).

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Where Edge fits in

The GenAI and agentic AI space is advancing and changing at a dizzying pace, but staying ahead of the AI trends is proving to be crucial for virtually any industry. Identifying relevant trends and putting their impacts into context is where a competent market intelligence tool comes in handy, providing a detailed view of relevant developments, startups, investment trends, ethical considerations, and a host of other critical information in the space.

For more information on Agentic AI, take a deep dive with our exclusive report here.

Edge is a market intelligence platform that helps organizations make quick, confident decisions about emerging tech industries and their players. With detailed coverage across relevant industries—GenAI Applications, Foundation Models, GenAI Infrastructure, Machine Learning Infrastructure, etc.—and over 200 other emerging industries, organizations can keep up with evolving tech trends, competitors, startups, and industry landscapes.

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