jberk in BlogInsights

From SaaS to Agents: The New Users of Business Apps

ai agents apps

For over a decade, the landscape of enterprise software has been dominated by applications. From CRM systems to ERP platforms, they have been the backbone of organizational operations.

However, a transformative shift is underway: AI agents apps are emerging as the new interface between users and digital systems, fundamentally altering how we interact with technology. And industry leaders are acknowledging this paradigm change.

As Dharmesh Shah signals, “Agents are the new apps”, and business software, until now used by humans, is increasingly being used by AI agents. But this does not end in a mere change of users. In fact, Satya Nadella, CEO of Microsoft, envisions a future where the traditional notion of business apps collapses in favor of AI-driven processes.

To put it in simpler terms, AI agents could bring the end of SaaS as we know it. But how AI agents are redefining the software landscape? What are the implications for businesses? And, most importantly, what is the future of enterprise software in an agent-driven environment? Let’s check all these out.

Yesterday’s Paradigm: Humans-Centered Apps

For decades, business applications were built with a single assumption: that humans would be their primary users. This paradigm shaped everything—from user interface design to workflow logic to data input methods.

Whether it was sales reps logging data in a CRM, finance teams managing budgets in spreadsheets, or support agents navigating ticketing systems, apps were interactive platforms, requiring human initiation and execution at every step. These traditional business apps followed a predictable model:

User Interfaces (UIs): Designed for manual use—forms, dashboards, buttons, filters.

Process Flows: Structured to guide human users through tasks like data entry, approvals, and analysis.

Integrations: Often rigid or limited, requiring middleware or human-driven exports and imports.

Automations: Mostly rules-based, requiring admins or power users to define exact if-this-then-that logic.

However, although this model served enterprises well for a long time, it came with notable constraints:

Cognitive load: Users needed to learn each system’s quirks, remember where data lived, and constantly toggle between apps.

Workflow fragmentation: Even simple business processes often required multiple apps and multiple logins, slowing productivity.

Scalability limits: Adding more users or processes usually meant more licenses, more training, and more complexity—not more automation.

In essence, the “app era” was human-centric but not necessarily human-friendly. Software was built around the needs of systems and data structures, with humans acting as the glue—interfacing between siloed tools and translating business intent into digital actions. But AI agents apps are a complete different issue.

Today’s Shift: From Users to Agents

So, this approach made sense in a world where humans were the only ones capable of interpreting goals, navigating systems, and making decisions. But not anymore. AI agents are becoming the ones that interact with data, make decisions, and execute tasks, effectively becoming the new users of business applications.

So, instead of multiple human users logging into a system, clicking through menus, and manually entering data, now we have a single AI agent that can handle everything. Human users just describe what they want, and they interpret the request, navigate the application backend, and complete the task.

Instead of multiple human users logging into a system, now we have a single AI agent that can handle everything

Let’s illustrate these AI agents apps with a practical example. Traditionally, a sales manager would log into Salesforce, update pipeline stages manually, and generate reports to forecast revenue.

Now, that same manager simply says, “Update my Q3 forecast and flag risky deals over $100K.” and the AI agent interprets the request, pulls data from Salesforce, updates records, identifies at-risk opportunities, and prepares a summary.

No clicks, no dashboards, no manual work. But our AI agent isn’t just supporting the user—it’s acting on their behalf, using the app as a backend, not a front-end experience. This way, we see a shift from execution to intent: humans define goals, agents deliver outcomes.

The Changes and Challenges of Agent-Driven Workflows

So, we’re going beyond mere task automation to enter an multiagent era in which systems must work with (and for) autonomous digital actors coordinating across departments and even companies to keep business moving in real time.

But what does this look like in practice? Let’s see some of the main changes and challenges of AI agents apps to see better what agent-driven workflows implicate for businesses.

1. Process Visibility: From Workflow Ownership to Workflow Transparency

In traditional enterprise environments, process visibility has always been linked to human ownership—tasks are visible because people manually input data, trigger actions, or update systems. But with the rise of AI agents apps much of this visibility disappears.

Agents don’t need interfaces—they operate behind the scenes, moving data across APIs and triggering systems autonomously. As a result, the “work” becomes opaque. Without careful design, organizations lose visibility into what’s happening, when, and why.

Agents don’t need interfaces—they operate behind the scenes, moving data across APIs and triggering systems autonomously

This raises critical concerns for decision-makers. Without traceability, errors become harder to troubleshoot, while decisions made by agents require new layers of explanation to be trustworthy.

So, companies are now investing in agent observability tools. To be as short as possible, these are basically dashboards that provide real-time transparency, logs that trace actions, and “explainability layers” that help leaders understand the logic behind every automated decision.

2. Governance & Trust: From Permissions to Policy Engines

In human-driven workflows, access is controlled via roles, permissions, and oversight. But, with AI agents apps, businesses need new governance models to ensure safety, compliance, and ethical behavior. This is due to two major reasons.

On the one hand, agents might act too fast, access the wrong data, or make noncompliant decisions. On the other hand, without clear boundaries, one rogue action can affect multiple systems in seconds. That’s why enterprises must:

Introduce AI-specific guardrails like policy engines, agent role-based access, contextual constraints.

Develop “ethical rails”—rules defining what agents can and can’t decide, especially in regulated industries like healthcare, finance, or government.

Integrate audit and logging tools to track every action for compliance.

3. System Readiness: From Apps with UIs to Platforms with APIs

Historically, enterprise apps have been designed for human interaction. They present forms, buttons, and dashboards optimized for user experience. But AI agents apps don’t click buttons or navigate screens—they interact through APIs, webhooks, and event streams.

This shift exposes a stark reality: legacy systems weren’t built for agents. If an app lacks robust, well-documented APIs, it’s invisible to agents. If it requires human steps to complete a process, it creates a bottleneck that undercuts automation ROI.

AI agents apps don’t click buttons or navigate screens—they interact through APIs, webhooks, and event streams.

So, organizations that want to adopt AI agents apps at scale must modernize their software architecture. This means investing in integration platforms (e.g. MuleSoft) that can expose legacy systems to agents. It also means rethinking app design entirely.

This is what will allow them to build for composability, decoupling services, and embracing event-driven models that allow agents to “listen” and respond in real time. In short, the future isn’t apps with better UIs—it’s platforms that are ready for agents to act without one.

4. Human-AI Coordination: From Task Execution to Judgment and Oversight

When AI agents apps take over execution, the role of human talent shifts profoundly. Employees are no longer hands-on task performers; instead, they become supervisors, orchestrators, and decision-makers who step in when judgment is needed.

So, this transition transforms both workflows and job roles. For example, structured, repetitive tasks are increasingly delegated to agents, while humans are now tasked with:

Designing prompts.

Setting agent goals.

Monitoring performance.

Intervening in edge cases or failures.

However, this requires a cultural and organizational reset. Teams must develop new skills in AI literacy and agent supervision, learning how to communicate with and manage agents. Some organizations are even introducing new roles—like “agent operations” or “AI enablement”—to ensure ongoing performance and accountability.

The Agentic AI Software Changes: Implications For Businesses

To enable this shift towards AI agents apps, enterprises will need to rethink how they design, deploy, and connect their systems. Here’s how architecture must evolve:

From Monolithic to Modular: AI agents need systems they can interact with independently. Composable architecture is no longer optional—it’s foundational.

From Static APIs to Dynamic Interfaces: Agents require access not only to data but also to contextual decision-making logic. APIs will evolve from static endpoints to dynamic, flexible orchestration layers.

From UI-first to Agent-first Design: Future enterprise software will offer both a UI for humans and a native interface layer for AI agents—possibly with dedicated Agent Access Services (AAS).

From Process Automation to Cognitive Execution: It’s not just about automating workflows. It’s about enabling agents to understand, reason, and act across domains in real time.

The end of SaaS? Toward Agent-Driven Operating Models

As Satya Nadella recently stated, “a lot of the business logic will move to a new tier… a multi-agent tier that needs to be orchestrated,”. But this emerging layer of AI agents as users not only represents a shift in enterprise software, but also the beginning of the end for SaaS as we know it.

For decades, SaaS has defined enterprise software: siloed apps, each with a user interface, designed for humans to log in, click around, and do the work themselves. But in an agent-driven world, the app becomes the infrastructure, not the experience.

The emerging layer of AI agents as users could represent the beginning of the end for SaaS as we know it

In other words, the agent becomes the interface, and work becomes a continuous, intelligent process—triggered by intent, not interaction. So, this shift isn’t just technical—it’s strategic, and businesses will need to:

Rethink software portfolios: The value of a platform will increasingly be judged not by its UI, but by how well it integrates, exposes APIs, and supports agent-driven automation.

Redesign processes for autonomy: Instead of mapping workflows for people, leaders will need to define outcomes and build the logic for agents to achieve them.

Build new governance models: As agents act with speed and autonomy, guardrails, oversight, and accountability frameworks must be built into the foundation.

Reskill and restructure teams: Human roles will shift from executing tasks to managing exceptions, optimizing agents, and focusing on uniquely human strengths like judgment, creativity, and relationships.

However, we must bear this in mind: this isn’t a future vision—it’s already starting. And as AI agents become the new users of business apps, and eventually the brain of business operations, companies that embrace this shift early will define the next generation of enterprise success.

But don’t worry. At Inclusion Cloud, we can help you build the AI foundations you need to incorporate this new workforce. From consulting and architecture design to custom development, integration, and ongoing support, we can help you scale with confidence.

Let’s set up a meeting and see how we can build the base of your AI transformation!