Agentic AI and the Startup Investing Landscape

Artificial intelligence (AI) is evolving beyond chatbots and content generation into systems capable of reasoning, planning, and taking autonomous action across complex workflows. This next phase, known as agentic AI, is attracting increased enterprise investment, creating a new arena of tech startups, and beginning to reshape how industries operate.

MicroVentures has more about what agentic AI is, which verticals it may disrupt, and what startup investors may want to consider as this technology unfolds.

What Is Agentic AI?

Agentic AI refers to AI systems that can set goals, reason through multi-step tasks, use external tools, and coordinate with other systems to complete complex workflows. Unlike earlier generative AI tools that primarily assist with individual tasks, agentic AI can act, decide, and adapt with a greater degree of autonomy.

In convergence with physical AI (think robots and control systems), agentic AI serves as the “brain” while physical AI acts as the “body,” together enabling autonomous systems that operate across both digital and physical environments.

This represents a potential shift from AI as a source of information and insights to AI as an executor of entire workflows that previously required sustained human effort.

Agentic AI Adoption Data

Data suggests agentic AI adoption could accelerate rapidly. According to Deloitte’s State of AI in the Enterprise report (January 2026), only 23% of companies are currently using agentic AI at least moderately, a figure expected to climb to 74% within two years.[i] The chart below illustrates this trajectory.

Verticals Being Disrupted

Agentic AI is already having meaningful impact across a broad range of industries, with production deployments underway in several sectors. The following examples are some of the verticals where disruption is currently taking shape due to agentic AI adoption:

• Financial Services: Agents are being used to automate compliance reviews, streamline due diligence processes, and personalize customer interactions.
• Customer Support: AI agents are handling routine transactions and service requests at scale, with organizations reporting reductions in call times and fewer escalations to human agents.
• Retail and Marketing: Retail and consumer packaged goods report among the highest agentic AI adoption rates across industries, with agents being deployed to personalize campaigns across customer segments and adapt pricing in real time.
• Logistics and Supply Chain: Agents are actively being used to monitor inventory, coordinate procurement, and reroute shipments in response to live demand signals and disruptions.
• Healthcare: AI agents are already handling administrative tasks like clinical documentation and patient monitoring, with healthcare reporting high overall AI agent usage across the industry.

Agentic AI Infrastructure

Beyond end applications, agentic AI requires a significant underlying infrastructure buildout. This infrastructure layer can include everything from the tools that allow multiple agents to communicate with one another, to the security and monitoring systems that keep autonomous actions within defined boundaries.

Startups building at this layer may represent a distinct investment opportunity from those focused on specific applications and could be worth evaluating separately as part of a broader investment thesis around AI.

While application-layer startups tend to be more immediately visible, infrastructure-layer companies may benefit from broader adoption across multiple end markets, as any organization deploying agentic AI at scale will likely need the underlying tooling regardless of the specific use case they are pursuing.

Agentic AI Meets Physical AI

One of the more compelling convergence stories in this space is the relationship between agentic AI and physical AI. In manufacturing, for example, agentic AI can reason through a supply chain disruption and autonomously direct robotic systems on the factory floor to adjust production in real time, something that would have required human coordination.

Verticals like manufacturing, logistics, and defense are seeing this convergence most acutely, and the companies building at this intersection may represent some of the more distinctive investment opportunities in the broader AI landscape.

What This Could Mean for Startup Investors

Despite the momentum, investors may want to approach this space with a measured lens. Many organizations are attempting to layer agents onto existing workflows rather than redesigning those processes from the ground up, a pattern that can limit real-world results. The gap between pilot and production remains wide, and the hype around AI broadly can make it difficult to separate genuine traction from early experimentation.

For investors evaluating agentic AI startups, demonstrated production deployments, clear governance frameworks, and measurable traction may be indicators of potential than a compelling pitch alone. Conducting technical due diligence on the underlying technology, and carefully considering which layer of the stack a startup is building in, may be particularly important given the different risk profiles across the application and infrastructure layers.

Final Thoughts

Agentic AI represents a meaningful shift in how enterprises are beginning to think about automation, operations, and intelligent systems. The adoption trajectory is compelling and the verticals being disrupted span a wide range of industries. However, the distance between potential and production-ready deployment remains a concern. For investors, understanding infrastructure requirements, governance challenges, and the convergence with physical AI may be key to identifying where the real opportunity lies in this evolving landscape.

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Want to learn more about MicroVentures portfolio companies? Check out the following MicroVentures blogs to learn more:

• Understanding Customer Acquisition Cost
• What to Look for in Investment Updates
• Understanding Startup Revenue Models
• When is a Startup No Longer a Startup
• Evaluating Go-To-Market Strategy

[i] https://www.deloitte.com/content/dam/assets-zone3/us/en/docs/services/consulting/2026/state-of-ai-2026.pdf

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