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Artificial intelligence is entering a new phase with the rise of “agentic AI.” Unlike traditional AI systems that respond only when prompted, agentic AI can act autonomously—searching, analyzing, and taking action to complete tasks.
This shift represents a major step forward in AI’s development, bringing it closer to the long-term goal of artificial general intelligence (AGI). On a recent Deep Learning with PolyAI podcast episode, our hosts Damien and Nikola (CEO & Co-Founder) explored the potential of agentic AI to transform industries while addressing the opportunities and challenges it presents.
From reactive tools to autonomous agents
Traditional AI systems have largely been reactive, designed to respond to specific queries or tasks. In contrast, agentic AI takes this a step further. These systems are envisioned to:
- Interact dynamically with the world: Searching the internet, making API calls, and synthesizing information to complete complex tasks.
- Experiment and learn continuously: Imagine an AI managing room temperature—it could experiment by correlating changes with user health data (like heart rate) to optimize comfort.
- Collaborate across ecosystems: AI agents could work together across different platforms to streamline business processes, such as automating procurement by gathering quotes from multiple vendors in parallel.
This leap in capability is not just about making decisions but about making informed and context-aware decisions, driving efficiency and innovation.
Balancing opportunity with risk
The autonomy of agentic AI excites technologists and entrepreneurs, but it also stirs concerns. Cultural portrayals, like the “Skynet singularity” from The Terminator, have ingrained fears about AI overreach. Even in real-world scenarios, increased autonomy raises ethical and operational questions:
- Trust and transparency: How much decision-making should we delegate to AI? Ensuring that systems are observable and auditable will be crucial to mitigating misuse or unintended consequences.
- Risk of overreach: While AI autonomy could streamline enterprise operations, improper use might lead to errors or amplify biases, particularly in high-stakes environments like finance or healthcare.
Despite these concerns, the benefits—reduced operational costs, faster execution of complex tasks, and improved user experiences—are compelling.
Agentic AI in action
The shift toward agentic AI is already underway, with startups and tech giants alike investing in this space. A notable example is the recent $56 million seed funding round for a company aiming to create an operating system for agentic AI. Such initiatives reflect a growing recognition that this technology is the next big wave in AI innovation.
Even today, we see early glimpses of agentic behavior in systems like Google Duplex, which can autonomously make phone calls to update business information. These interactions, though simple, highlight the potential for more complex and seamless agentic ecosystems.
The road ahead
As Nikola succinctly put it, “Let’s go build some cool [stuff] and see where we end up.” This optimism underscores the exploratory nature of the current phase in AI development. For businesses, governments, and individuals, the challenge lies in embracing agentic AI thoughtfully—leveraging its strengths while safeguarding against its risks.
Agentic AI is not just a step towards AGI; it’s a gateway to an AI-first world. As we navigate this transformative era, the focus must remain on collaboration, transparency, and creating systems that enhance human lives without compromising autonomy or ethics.
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Watch the full episode about Agentic AI on our podcast ‘Deep Learning with PolyAI’ here.
