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Posted April 23, 2025 at 10:15 am
The hype cycle around artificial intelligence (AI) often moves faster than the capabilities it touts. But in 2025, we are witnessing a critical shift in the conversation: from predictive text generators to agentic AI1—systems not just capable of reasoning, but of doing. The shift from passive to proactive intelligence is subtle in concept but profound in implication.
As investors, technologists and strategists, we need to recalibrate our understanding of what these AI agents are, what they can reasonably do and when we might expect returns on the billions of dollars pouring into this space.
Definitions are still squishy, but a useful working model is this: AI agents are autonomous systems that can make decisions and take actions toward a goal. Not just chatbots that reply with flair, but entities that can reason through multi-step problems and act without constant human supervision.2
To borrow a metaphor from Capital One’s chief AI scientist, talking about agentic AI today is like the parable of the blind men and the elephant—everyone is touching a different part.3 Some define it narrowly (assistive bots), others more broadly (fully autonomous digital workers). But the most agreed-upon litmus test comes from Gartner: does the AI make a decision, and does it take an action?4
Early enterprise deployments offer proof of concept:
These aren’t just productivity tools; they are beginning to behave like team members.
Today’s agentic AI excels in structured environments:
Looking into Manus—a general-purpose agent from China—showed impressive results: it autonomously searched for journalists, parsed housing listings and curated nomination lists. It even admitted when it got “lazy.”8
But its limitations are revealing9:
In short: agents can handle projects you might give an intern, but not yet a chief of staff.
There’s also a compute gap10. As agents move toward more complex reasoning, Nvidia estimates next-gen AI may need 100 timesthe compute resources compared to last year’s models.11 That’s not a marginal hurdle; it’s a generational infrastructure challenge.
In the near term—2025 to 2026—investors should expect agentic AI to find its strongest foothold in structured task automation. These are environments like HR, customer service and operational workflows where clean data and repeatable decisions are the norm. The most interesting plays here may be infrastructure and early enterprise deployment layers: companies like Nvidia (for compute), Salesforce (for enterprise orchestration) and vertical software-as-a-service (SaaS) platforms that embed agents directly into user workflows. Vertical refers to industry vertical and example companies could look like Veeva Systems (Health Care), Toast (Restaurants) and Procore (Construction).
Casey’s General Stores, the third-largest convenience store chain and fifth-largest pizza chain in the U.S., has strategically integrated AI to enhance its pizza ordering and delivery services. In September 2023, Casey’s implemented a conversational AI voice ordering system, known as the Automated Voice Assistant (AVA), across its entire network of over 2,500 stores spanning 16 states.12
The adoption of AVA was driven by Casey’s commitment to reducing friction for both guests and team members by leveraging technology to simplify and streamline the ordering experience. It’s useful to look at this example as an important company embarking on a journey to embed more and more technology-based solutions, even if it may not yet be directly using the most advanced AI agents today.
Between 2026 and 2028, we can reasonably expect agents to scale into semi-structured environments. This means more integration into business workflows that have some ambiguity but still operate within defined guardrails—think sales ops, supply chain optimizations and finance. During this phase, winners will begin to emerge among the orchestration layers—the platforms that can manage multiple agents across tasks and departments.
By 2028 to 2030 and beyond, we may start seeing autonomous agents reasoning across functional domains, coordinating decisions across departments, and even initiating cross-functional actions without human input. These systems could function like digital chief operating officers (COOs) or multi-domain analysts. The investment landscape by then may shift to full-stack agentic AI companies, but will likely also see consolidation as dominant platforms establish moats.
Investors betting on the “agentic turn” should avoid assuming a uniform disruption curve. Large language models (LLMs) are good at essay questions, not messy multitasking. Entry-level jobs that involve judgment calls, ambiguity, and human nuance are harder to automate than they look.13
But the shift is directional. Companies are already questioning whether to hire junior analysts or invest in agents. Apprenticeship models may re-emerge, where senior professionals train both people and machines.
AI agents are beginning to make notable contributions to business revenues and operational efficiencies across various industries. While the market is still evolving, several key developments highlight the growing financial impact of AI agents:
While these developments indicate a positive trajectory, it’s important to note that the widespread financial impact of AI agents is still emerging. As adoption continues to grow and technologies mature, the business impacts are expected to become more pronounced in the coming years.
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Originally Posted on April 21, 2025 – Agentic AI: The New Frontier of Intelligence That Acts
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