2025 marks the rise of enterprise AI agents — autonomous digital systems that are reshaping how work gets done. These AI agents don’t just automate isolated tasks, they grasp context, formulate plans, and execute entire business processes independently. Unlike earlier AI tools that pieced together individual steps under human direction, today’s AI agents drive end-to-end operations, marking a transformative shift in how work gets done.

Think about largely automating the following tasks: anticipating equipment failures, optimizing production quality, streamlining public services, monitoring financial integrity, and enhancing patient care. Organizations in highly regulated and complex industries are poised to reap the benefits of deploying secure AI agents to reinvent workflows and create new automations — whether in financial services, healthcare, energy, manufacturing, or the public sector.

And this is just the beginning. By 2028, Gartner predicts that AI agents will be woven into one-third of enterprise software and shape 15% of daily business decisions. This isn't incremental progress — it's a fundamental transformation in which AI agents evolve from mere tools into trusted partners, augmenting human expertise to drive smarter decisions across the organization.

What are secure, enterprise AI agents?

AI agents are advanced software programs designed to independently reason, access information from various sources, and execute intricate tasks across the enterprise.

"Agentic AI is all about having a system that’s connected to your company's data sources, retrieving what it needs in multiple steps, analyzing that data, and then using it for a task—like generating a doc or updating CRM. And it may ask clarifying questions or confirm with a human. It’s the ‘brain’ that orchestrates everything.” – Maxime Voisin, product manager at Cohere.

Equipped with specialized tools — connections to company-wide systems — they navigate complex workflows step by step, delivering efficiency and precision with minimal human intervention.

These applications redefine how businesses work, enabling scalable operations, smarter decision-making, and seamless integration across systems.

"Agentic workflows and tool use, which is the focus of Cohere when we're building our models for automation, is really where the unlock has been. Models starting to become AI agents using complicated tools and enterprise software has transformed the success rate of automation from ‘it almost always fails’ to ‘it almost never fails’." – Aidan Gomez, CEO and Co-Founder of Cohere

In highly regulated and complex sectors, such as finance, healthcare, manufacturing, energy, and government, autonomous AI agents promise to reinvent and transform operations while enabling uncompromised security and compliance. These industries handle vast amounts of confidential and sensitive information while adapting to a shifting regulatory landscape.

Secure AI agents provide additional operational advantages with more control and customization opportunities designed and tailored to the unique demands of regulated industries. The most secure approach for production is with private deployments. A private deployment ensures that sensitive data remains within an organization’s controlled environment, meeting strict regulatory requirements (e.g., HIPAA, GDPR, AI Act) and significantly reducing risks associated with data transmission.

By combining autonomous reasoning with robust security measures, private AI agents not only streamline complex workflows — like real-time market analysis in finance or sensitive data handling in healthcare — but they also boost human productivity.

How we got to enterprise AI agents

The path to AI agents reflects a natural progression in how enterprises have adopted and matured their AI capabilities. Each phase of large language model (LLM) evolution has built upon previous innovations, enabling increasingly sophisticated systems that can handle more complex tasks with greater autonomy.

Phase 1: Base language models

AI began with fundamental language models designed for text completion and prediction. These models could continue text sequences and fill in gaps, but couldn’t follow specific instructions or engage in meaningful dialogue. While powerful for tasks like code completion or text prediction, they weren't suited for complex business applications.

Phase 2: Instruction following

The next breakthrough came with models that could understand and follow specific instructions. This advancement enabled models to perform specific tasks like summarization, translation, or analysis when given clear directions. However, these models were still limited to working with their training data and couldn't access or incorporate new information.

Phase 3: Conversational AI

Language models then evolved to engage in natural dialogue, with systems like ChatGPT demonstrating the ability to maintain context through conversations and generate contextually appropriate responses. While revolutionary for user interaction, these implementations were still constrained by their inability to access current information or company-specific knowledge.

Phase 4: Retrieval-augmented generation (RAG)

RAG marked a significant advance by connecting language models to external knowledge sources. This enabled AI systems to ground their responses in current, company-specific information, making them practical for business use. The process was straightforward but powerful: retrieve relevant information, incorporate it into the context, and generate an informed response.

Phase 5: Tool use

The next major development was enabling models to interact with external tools and APIs. Models could now not only process information, but also take specific actions through well-defined interfaces. Even so, these implementations typically required explicit instructions about which tools to use and when, limiting their autonomy.

Phase 6: AI agents — the current frontier

AI agents represent the culmination of these capabilities combined with sophisticated reasoning abilities. Today’s agents can:

Access and process information through RAG
Use tools and APIs to take action
Plan and execute multi-step processes
Reason about their approach and adjust as needed
Maintain context across complex workflows

For example, an AI agent handling customer support can analyze incoming tickets, search knowledge bases for relevant information, route tickets to appropriate departments, update tracking systems, and draft initial responses — all while adapting its approach based on the specific situation and available tools. This combination of capabilities makes AI agents particularly powerful for transforming enterprise operations.

Optimizing secure agentic systems

As enterprises move toward adopting AI agents to streamline workflows and boost productivity, they face a critical challenge: managing these agents in a centralized, coherent way. Rather than piecing together multiple tools across departments, enterprises are looking for a unified system where they can build, manage, and scale intelligent agents securely and efficiently.

That’s where North comes in—a platform optimized to run in private–including air-gapped–environments so that organizations can safely integrate all their sensitive data and designed to centralize the creation and deployment of enterprise-grade AI agents. Whether it’s automating routine operations or supporting complex decision-making tasks, North enables organizations to set up pipelines and agents tailored to their unique roles and use cases.

A key example is North for Banking, developed in partnership with Royal Bank of Canada (RBC), which tailors North’s capabilities specifically for financial services. By integrating relevant data sources and enabling secure, compliant workflows, this verticalized solution empowers knowledge workers in banking to move beyond simple information retrieval toward executing sophisticated, value-generating tasks.

What sets North apart is its full-stack control—from the modeling and search layers to the UI and integration capabilities—which allows for deeper functionality than typical copilot tools stuck in basic search-and-chat loops. As Cohere product manager Maxime Voisin explains, “this full-stack control unlocks the ability to tackle more complex problems, yielding greater business impact.”

What’s next

Agentic orchestration and governance is likely to take center stage this year as more enterprises test the potential of multi-agent systems. Without a strong secure agentic framework, enterprises risk creating fragmented, opaque, or even conflicting agent behaviors that undermine trust and effectiveness. But with the right infrastructure and platform in place, businesses can unlock the true promise of multi-agent AI: scalable automation, deeper insights, and more intelligent, context-aware decision-making. As these systems mature, the ability to manage them centrally, monitor performance, ensure data quality, and continuously optimize their behavior will become a critical differentiator—turning agentic chaos into strategic advantage.

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The promise of autonomous and secure AI agents