Gartner's number. Up from less than 5% in 2025. This isn't a trend — it's a phase change. The companies building agent-native products right now will own their categories in 18 months.
Gartner dropped a number: 40% of enterprise applications will have AI agents by the end of this year. Up from less than 5% in 2025.
That’s not a trend. That’s a phase change.
The companies building agent-native products right now are the ones that will own their categories in 18 months. The ones waiting for the technology to “mature” are watching their window close in real time.
The move from less than 5% to 40% in a single year isn’t gradual adoption — it’s a cascade. Once the infrastructure matures enough that large enterprises can deploy agents without building everything from scratch, the laggards move fast to catch up. That’s what’s happening right now.
But here’s what the headline number doesn’t capture: there’s a massive difference between an application that “has AI agents” and one that’s been architecturally designed to be agent-native.
Bolt-on agents are a company adding an AI assistant to an existing app. It answers questions, fills out forms, helps with search. It’s useful, but it’s not transformative. Agent-native products are built around agents operating autonomously across workflows. The agent isn’t a feature — it’s the primary way the product delivers value.
The 40% figure includes both. But the companies that will define their categories in 18 months are building the latter.
We’re working with founders right now on products that are being architected agent-native from day one. Here’s what that looks like in concrete terms.
Instead of a clinician typing notes into an EHR, an agent listens to the patient encounter, structures the clinical note, suggests ICD-10 codes, flags potential drug interactions, and submits to the billing queue. The clinician reviews and approves. The agent does the labor.
Instead of rules-based fraud alerts that generate false positives by the thousands, agent-native monitoring systems investigate each alert: pulling transaction history, comparing behavioral patterns, querying external data sources, and escalating with context rather than just a flag. The human analyst makes the decision. The agent does the investigation.
When a shipment is delayed, the agent identifies the cause, assesses downstream impact, notifies affected parties, routes alternatives, and updates the ERP — without waiting for a human to start the process. Exceptions that used to take hours to resolve now get handled in minutes.
The common thread: these aren’t apps with AI features. These are workflows where agents handle the execution and humans handle the judgment. That’s agent-native design.
If you’re a founder or product leader trying to figure out where to place your bets, here’s the framework we use at Bolder Apps when advising on agent strategy.
Start by identifying your highest-friction workflows. Where does your product force users to do repetitive, multi-step work that follows a predictable pattern? Those are your agent opportunities. The question isn’t “where can I add AI?” — it’s “where can I remove labor from the user loop entirely?”
Design for agent reliability before agent capability. The most common mistake in agentic product development is over-indexing on what the agent can do and under-indexing on what happens when it fails. Agents fail. Models hallucinate. Reliability architecture — fallbacks, confidence thresholds, human escalation paths — is what separates production-grade agents from demos.
Build for observability. You cannot improve what you cannot observe. Agent-native products need robust logging of what agents are doing, where they’re succeeding, and where they’re failing. This is infrastructure that needs to be built in from day one, not retrofitted.
Don’t wait for perfect. The teams that win won’t be the ones who waited for the technology to mature. They’ll be the ones who shipped something useful with today’s technology, iterated on real feedback, and built a structural advantage through learning.
We help founders navigate all of this — from agent architecture to reliability design to the specific frameworks that actually work in production. If you’re building something with agents, let’s talk.
Here’s the uncomfortable truth: category leadership in AI-native software is being decided right now. Not in three years when the technology has fully matured. Now.
The 40% figure is a lagging indicator of momentum that’s already in motion. The companies in that figure who built agent-native from scratch are pulling ahead. The ones who bolted agents onto existing products will spend the next two years playing catch-up.
Your competitors are not waiting. The question is whether you’re building or watching.
Bolder Apps has been building AI-integrated products for three years. We’ve shipped agent-native applications across healthcare, fintech, logistics, and consumer apps. We know what works in production and what looks good in demos. See how we approach AI product development.
Gartner projects that 40% of enterprise applications will incorporate AI agents by the end of 2026, up from less than 5% in 2025. This represents one of the fastest technology adoption curves Gartner has tracked in enterprise software history.
An AI feature typically responds to a single request — answering a question, generating text, summarizing content. An AI agent autonomously completes multi-step tasks: planning, using tools, making decisions, and iterating until a goal is achieved with minimal human intervention at each step.
Agent-native products are designed from the ground up with agents as the primary delivery mechanism, rather than adding AI as a feature layer to an existing product. The architecture, UX, and data infrastructure are all optimized for agent-driven workflows.
Start by identifying the highest-friction workflows in your product or organization. Design an agent capability with reliability architecture built in — including fallbacks, confidence thresholds, and human oversight mechanisms. Prioritize shipping something that works over building something theoretically complete. Learn from real usage before doubling down on scale.
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