Users of Business Applications Shift from SaaS to Agents Model

Users of Business Applications Shift from SaaS to Agents Model

In the realm of business software, a seismic change is underway. AI agents, the new interface between people and digital systems, are swiftly replacing traditional apps as the preferred mode of interaction. Industry titans are recognizing this seismic shift, with Dharmesh Shah declaring, "Agents are the new apps."

Traditional business software, designed with humans firmly in mind, is being abandoned in favor of AI-driven processes envisioned by Satya Nadella, CEO of Microsoft. In essence, we might be witnessing the twilight of SaaS as we know it. So, how exactly are AI agents reshaping the software landscape? What does this mean for businesses? And what lies ahead in this frontier of agent-driven enterprise software? Let's delve deeper.

Yesterday's Paradigm: Human-Centric Apps

For years, business applications have been designed with the assumption that humans would be their primary users. This bias affected everything, from user interface design to workflow logic to data input methods. Whether it was sales reps using CRM systems, finance teams managing budgets on spreadsheets, or support agents navigating ticketing systems, apps were interactive platforms, demanding human interaction and involvement at every step.

However, this approach came with notable constraints. Users were saddled with cognitive load, forced to learn each system's quirks, remember where data was stored, and constantly juggle apps. Workflows were fragmented, even simple processes requiring multiple apps and multiple logins, slowing productivity. Scalability was limited; adding more users or processes invariably 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 systems and data structures, with humans acting as the translators-interfacing between siloed tools and translating business intent into digital actions. But AI agents are a whole new ball game.

Today's Shift: From Users to Agents

In a world where humans were the only ones capable of interpreting goals, navigating systems, and making decisions, this approach made sense. But not anymore. AI agents are taking over, interacting with data, making decisions, and executing tasks, effectively becoming the new users of business applications.

Now, instead of multiple human users logging into a system, clicking through menus, and manually entering data, we have a single AI agent that can handle everything. Human users just describe their requirements, and the AI agent interprets the request, navigates the application backend, and executes the task.

Let's illustrate these AI agents with a practical example. A sales manager would previously log into Salesforce, update pipeline stages manually, and generate reports to forecast revenue. Today, the same manager simply states, "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, all without a single click.

Our AI agent isn't just assisting the user; it's acting on their behalf, using the app as a backend, not a front-end experience. This shift marks a move from execution to intent: humans define goals, while agents deliver outcomes.

The Changes and Challenges of Agent-Driven Workflows

With the rise of AI agents, we're stepping into a multiagent era in which systems must work for autonomous digital actors, coordinating across departments and even companies in real-time to keep the business moving. But what does this look like in practice? Let's explore some changes and challenges of AI agent-driven workflows to better understand agent-driven workflows' implications 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. However, with the rise of AI agents, much of this visibility disappears. As agents operate behind the scenes, moving data across APIs autonomously, the work becomes opaque. Organizations risk losing visibility into what's happening, why, and when, raising critical concerns for decision-makers.

To address this, companies are investing in agent observability tools, effectively providing dashboards, logs, and "explainability layers" to 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. With AI agents, businesses need new governance models to ensure safety, compliance, and ethical behavior. Two major reasons lie behind this requirement:

1. Agents might act too fast, access the wrong data, or make noncompliant decisions.
2. Without clear boundaries, one rogue action can affect multiple systems in seconds.

To tackle this, enterprises must introduce AI-specific guardrails, such as policy engines, agent role-based access, and contextual constraints. They must also develop ethical rails, defining what agents can and cannot decide, especially in regulated industries like healthcare, finance, or government. Additionally, integrating audit and logging tools is crucial for tracking every action for compliance.

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

Historically, enterprise apps have been designed for human interaction, presenting forms, buttons, and dashboards optimized for user experience. However, AI agents don't click buttons or navigate screens; they interact through APIs, webhooks, and event streams. This shift exposes the reality: legacy systems weren't built for agents. If an app lacks robust, well-documented APIs, it becomes invisible to agents. If it requires human steps to complete a process, it creates bottlenecks, undercutting automation ROI.

To scale agent-driven workflows, organizations must modernize their software architecture. This involves investing in integration platforms (such as MuleSoft) that can expose legacy systems to agents and rethinking app design to build for composability, decoupling services, and embracing event-driven models. These changes allow agents to "listen" and respond in real-time.

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

As AI agents take over execution, the role of human talent shifts profoundly. Employees move from hands-on task performers to supervisors, orchestrators, and decision-makers who intervene when judgment is needed. However, this transition requires a cultural and organizational reset. Teams need to develop new skills in AI literacy and agent supervision, learning how to communicate with and manage agents.

In summary, as AI agents become the new users of business apps, businesses must adapt to agent-driven workflows, rethinking software architecture, designing policies, overcoming challenges, and reskilling and restructuring teams. Embracing this shift early will enable businesses to capitalize on the next generation of enterprise success.

With the right guidance, organizations can effectively incorporate AI agents into their operations, ushering in an era of streamlined workflows, improved decision-making, and heightened competitiveness. At Inclusion Cloud, we can help you set the foundation for your AI transformation, offering consulting, architecture design, custom development, integration, and ongoing support. Let's discuss your needs and chart the course for a successful AI-powered future!

Here are three sentences that contain the given words and follow from the given text:

1. To ensure ethical behavior in the multiagent era, companies are investing in policy engines, as they provide a means to govern AI agents by setting clear boundaries for their decision-making processes.
2. In the finance industry, where regulatory compliance is crucial, businesses are leveraging AI-specific guardrails to manage their AI agents, ensuring that these tools make decisions in alignment with industry standards and legislation.
3. As the shift towards agent-driven workflows progresses, software developers are increasingly embracing technology like artificial intelligence (AI) and technology platforms with APIs, as these tools enable AI agents to interact with business applications effectively and seamlessly.

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