6 Agentic AI Trends Transforming Enterprise in 2026
By Stefan Ostwald, Co-founder and Chief AI Officer at Parloa
In 2025, we witnessed the widespread adoption of agentic AI in the enterprise, driven by a shift from reactive, generative AI models to more autonomous, goal-oriented AI capabilities. A November 2025 survey by McKinsey revealed that 88 percent of organizations regularly use agentic AI in at least one business function, a 10 percent increase over the same survey findings a year ago. However, only one-third of organizations said they have moved agentic AI out of experimentation and scaled it across the business. Why do teams keep these projects locked in beta?
For this to change in 2026, we expect to see six key trends.
Agent-to-Agent Ecosystems
In 2025, many organizations still treated AI as an experimental tool instead of a foundational layer of the business. That mindset has slowed down transformation and kept AI siloed in innovation teams, rather than embedding it in everyday operations. In 2026, we’ll see AI agents move from operating as isolated tools and start forming true ecosystems, where agents routinely communicate and collaborate to get work done.
While 2025 was characterized by humans using single agents to automate narrow tasks, we were still constrained by the individual capabilities of each agent. The next step, which is expected to advance significantly in 2026, involves agent-to-agent (A2A) communication: one agent will be able to discover, invoke, and coordinate with other specialized agents to solve more complex problems.
This will be enabled by a kind of “agent register” or app store, where different agents and their capabilities are listed and can be programmatically accessed. Over time, this register will evolve into a marketplace where agents and their creators can be monetized, with transparent pricing and usage models that ensure fairness and accountability.
By 2027, organizations will give individual agents budgets and high-level goals, enabling them to autonomously purchase services from other agents in the marketplace to accomplish their tasks. The core trend is not just agents communicating with each other, but agents trading services with each other, creating a new economic layer where machine-to-machine collaboration and commerce become the standard.
Verticalized Agents
In 2026, enterprises will demand clear, defensible ROI from AI deployments, as generic chatbots will increasingly fail to deliver in industry-specific environments. As a result, verticalized AI agents—those tailored to the precise language, data structures, workflows, and compliance requirements—will become a strategic necessity.
For example, agents purpose-built for the travel industry, trained for specific workflows and customer needs in this vertical, will emerge. Achieving verticalized agents will require deep customization, throughout the agentic tech stack.
As this shift accelerates, expect to see the rise of agent labs: companies that apply AI expertise to domain engineering, producing end-to-end vertical agents. These agent labs will become the primary engine of enterprise AI value creation in 2026, while vendors offering generic, one-size-fits-all agents will increasingly struggle to meet performance expectations in high-stakes, industry-specific settings.
On-Device Agents
Another powerful trend for 2026 is the shift toward AI agents running directly on personal devices, such as smartphones, laptops, and wearables, rather than relying solely on the cloud. This shift will emerge in two forms.
First, privacy-first, on-device agents will keep sensitive data local, addressing growing consumer and regulatory pressure around data security and surveillance. Second, context-rich, on-device agents will leverage continuous, fine-grained signals from the device—such as location, behavior patterns, app usage, and biometrics—to deliver deeply personalized, real-time assistance within a secure, localized environment.
Apple is the clearest example of this trend: after lagging in cloud AI, it is re-entering the race by doubling down on on-device intelligence, using its tightly integrated hardware–software ecosystem to deliver AI that is both private and highly contextual.
Proactive, Autonomous Agents
As the year progresses, AI agents will increasingly take initiative—evolving from passive, command-based tools into proactive partners that anticipate needs and act autonomously. Historically, assistants waited for explicit instructions (“What’s the weather?”).
However, the next generation of agentic systems – largely inclusive of the aforementioned, personalized on-device agents – will behave more like operational aides: continuously monitoring context, predicting user needs, and taking action before humans are notified.
This shift unlocks genuinely helpful, real-world behaviors—an agent that automatically rebooks your canceled flight once a notification hits your phone, secures a replacement when a favorite product comes back in stock, reschedules a meeting when a conflict arises, or flags a financial issue before it becomes a problem.
AI Agents Accelerating Scientific Discovery
AI agents will become significant accelerators of scientific progress, moving from research assistants to genuine discovery engines. These agents will read literature, generate hypotheses, design experiments, simulate outcomes, and even control lab automation—compressing months of scientific work into a matter of days.
The cumulative impact of this capability will culminate in a landmark moment: the first major academic breakthrough produced autonomously by an AI agent, with humans serving primarily as reviewers and validators.
Self-Optimized AI Agents
AI agents, by design, will increasingly become self-optimizing systems—continuously improving their own performance, reliability, and behaviors without explicit human intervention. Rather than waiting for updates or retraining cycles, these agents will have the power to monitor their progress toward achieving goals, learn from successes and failures, and autonomously adjust their strategies in real-time.
Beyond pure performance gains, agents will optimize for stability, cost, safety, and user satisfaction, treating these as ongoing objectives rather than fixed design constraints.
Action Beyond Trends
These six trends collectively paint a picture of an AI landscape fundamentally different from 2025. The shift does not merely upgrade capabilities; it completely transforms how enterprises integrate AI into their core operations.
In 2026, organizations will move AI agents beyond isolated tools managed by innovation teams and turn them into indispensable, autonomous members of the workforce who work side by side with humans.
For organizations to move beyond the pilot phase and realize the promised ROI of agentic AI, they must proactively invest in the infrastructure that supports inter-agent commerce and collaboration, prioritize domain-specific customization, and embrace the move from reactive command systems to truly proactive, self-governing intelligence. The future of enterprise efficiency and innovation hinges on this evolution.
About the Author
Stefan Ostwald is Co-founder and Chief AI Officer at Parloa.
Editor’s Note (CXQuest): Agentic AI directly impacts CX orchestration.
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