2025 was a transformative year for artificial intelligence, and 2026 is already proving to be equally significant.
While generative AI dominated conversations just a few years ago, the focus is now shifting towards agentic AI, where intelligent systems can take action, interact with business processes, and support employees in more meaningful ways.
As these capabilities continue to evolve, organizations are looking for better ways to connect AI systems to the applications, data and services that drive their operations.
Latest Videos FromTechRadar
This is where technologies such as Model Context Protocol (MCP) are becoming increasingly important.
Rather than creating bespoke integrations for every tool or system, MCP provides a standardized way for AI models and agents to access information and perform actions across an organization’s technology estate.
While MCP is not a requirement for every AI implementation today, it represents a natural next step for organizations looking to move beyond isolated AI use cases and towards more integrated, scalable AI ecosystems.
However, greater integration also introduces greater responsibility. Effective governance remains essential for any AI deployment, but it becomes even more critical when autonomous agents are granted access to business systems, processes and sensitive information.
Organizations must establish clear guardrails that define what agents can access, what actions they can perform, and how their activities are monitored. Without appropriate oversight, businesses risk agents operating beyond their intended scope or creating unintended consequences across interconnected systems.
The impact on Software-as-a-Service
Few sectors will feel the effects of this shift more than Software-as-a-Service (SaaS).
For years, SaaS applications have been built around human interaction. Users access platforms through dashboards and interfaces, navigate predefined workflows, and manually complete tasks. The application itself serves as the primary workspace where work is performed.
Agentic AI introduces a different model.
Rather than navigating software in the same way a person would, agents can interact directly with APIs, services and data sources. This allows them to retrieve information, execute actions and orchestrate processes across multiple systems without relying on traditional user journeys.
That does not mean SaaS applications will disappear. In fact, they will continue to play a critical role in storing structured data, enforcing business rules and managing workflows. What is likely to change is how those applications are consumed.
Instead of being the primary destination where work happens, many SaaS platforms will increasingly act as sources of capability and information that AI agents can utilize on behalf of users. As a result, organizations may find themselves focusing less on which application employees need to access and more on how services and data can be brought together to achieve the desired business outcome.
Human interfaces will still matter. Users will continue to need visibility, exception handling and control mechanisms, particularly when business-critical processes are involved. The challenge for software providers will be balancing traditional user experiences with new AI-driven interaction models while maintaining compatibility, reliability and operational resilience.
Breaking down agent silos
The next stage in the evolution of agentic AI is not simply creating more agents. It is enabling agents to work together effectively.
Many organizations already struggle with fragmented systems, disconnected data and isolated processes. Without careful planning, agents risk creating a new generation of silos, each operating within its own limited context and producing inconsistent outcomes.
To avoid this, businesses must focus on shared context, connected data and interoperable services. The goal is not to have individual agents automating isolated tasks but to enable multiple agents to contribute towards broader business objectives across entire processes.
When agents can access consistent information and operate across organizational boundaries, the value shifts from discrete task automation to coordinated execution. Rather than supporting an individual stage of a workflow, agents can participate in end-to-end processes while remaining aligned to business policies, operational requirements and organizational goals.
This represents an important architectural shift. Software increasingly becomes something that agents consume programmatically, while integration, context and orchestration become central to delivering outcomes at scale.
Mitigating risk in AIaaS
Unlocking these new capabilities requires more than deploying AI tools. Organizations need governance frameworks, security controls and operational processes that allow autonomy to be introduced safely and responsibly.
As agents gain access to more systems and collaborate across workflows, operational complexity inevitably increases. Businesses must define clear policies around what agents can and cannot do, what data they can access, and what approvals are required before actions are taken.
These controls should be embedded into the orchestration layer itself, ensuring governance is applied consistently across all agent-led activities rather than being treated as an afterthought.
Traceability and accountability are equally important. Completing a task successfully is only part of the equation. Organizations must understand how decisions were made, what information was used, and which policies were applied throughout the process. This visibility will be essential for compliance, security and maintaining trust in autonomous systems.
The role of developers will also evolve. Rather than spending significant time building and maintaining point-to-point integrations, they will increasingly focus on designing agent behaviors, defining boundaries, managing orchestration and ensuring solutions operate within established governance frameworks.
The organizations that succeed will be those that balance innovation with control. Too little governance introduces risk, while excessive restrictions can prevent businesses from realizing the benefits of AI altogether.
Agentic AI should not be viewed as a replacement for software development or existing technology investments. Instead, it represents a powerful new interaction layer that changes how organizations access information, automate processes and deliver outcomes.
Businesses that invest now in integration foundations, governance models and workforce skills will be best placed to take advantage of the opportunities this next phase of AI creates.
We’ve reviewed the best business laptops.
This article was produced as part of TechRadar Pro Perspectives, our channel to feature the best and brightest minds in the technology industry today.
The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/pro/perspectives-how-to-submit
