The shift from monolithic, inflexible systems to agentic, adaptive architectures marks the next evolution in enterprise technology, promising greater autonomy, flexibility, and productivity.

KEY TAKEAWAYS:

• Agentic systems redefine tech stacks by shifting away from the rigidity of traditional SaaS tools towards easy integrations and on-the-fly customizations.
• Unlike non-agentic systems that have rigid GUIs and need constant updation, agentic systems adapt in real-time to user needs, interpreting natural language inputs to create workflows and generate contextual interfaces as necessary.
• Agentic systems ensure true cross-application and data integration by maintaining ‘neutrality’—allowing users to work across ecosystems holistically.

We’ve recently written about why traditional SaaS is on the way out, and how the future will be agentic. As enterprises make this technological leap, where software goes from playing an assistive, incremental role to being autonomous, intelligent, and collaborative, they will also have to build an enterprise ecosystem that can succeed with Agentic AI.

This means updating everything from the processes and procurement methods of software to the mindset and metrics through which its success is measured. Importantly, tech architectures will evolve from the vertically siloed, static tech stacks that grew with traditional SaaS towards more horizontal, integrated systems, where data flows dynamically yet securely, and decisions can be made real-time by AI agents.

This technological shift from non-agentic to agentic systems will address the many critical limitations of current systems, improving flexibility, transparency, and user experience. Agentic AI will overcome the monolithic nature and weaknesses that characterize today's SaaS platforms.

Breaking free from Monolithic Architectures

Non-agentic systems employ microservices at an architectural level. In today’s SaaS applications, users are bound to a predetermined set of components, such as cloud infrastructure, data storage mechanisms, business logic, querying mechanisms, and graphical user interfaces (GUIs).

While microservices are great for breaking down technical components, they fail to provide the flexibility businesses need to design their own solutions at the product level. For example, if a company using a CRM system wants to implement a custom pricing algorithm or integrate with a specific third-party logistics system, they often need to resort to complex workarounds to achieve this.

In contrast, an agentic AI system will dynamically adapt to new requirements by incorporating functionalities and integrations without disrupting the overall system.

This is possible because the workflow-logic in an agentic system is implemented outside the core business logic. Think of it as software where not just the components but the workflows themselves are interchangeable, customizable, and adaptable.

The difference is best illustrated in the graphics below:

In agentic architectures, the boundaries that separate different software modules are intentionally porous, enabling easy integration and customization on the fly. Want to swap out your CRM’s default pricing algorithm with one that is specific to your niche industry? Such modifications aren’t simply possible with agentic systems, they are also seamless, with the new functionality adapting smoothly into the rest of the architecture.

The Flexibility of Agentic Workflows

Workflows in non-agentic systems come with a set of challenges, including predefined, rigid paths, technically complex setups, and a limited ability to adapt to changing requirements. Enterprises are forced to conform to the logic embedded within the software, rather than molding software to their unique needs.

Consider how actions across multiple systems—say, CRM, ERP, and ticketing—often require navigating several different interfaces. Users must follow rigid, predefined processes that rarely match the real-life, dynamic scenarios in which they operate. This lack of flexibility not only limits user experience but also creates barriers that stunt process innovation.

But with agentic systems, workflows are adaptive, evolving on the fly based on natural language commands and personalized preferences. Imagine prompting an agentic system with, "Connect my CRM to ERP and initiate an alert anytime inventory drops below a specific threshold," and watching the system interpret and execute that intent—creating cross-application workflows dynamically, without intervention from IT.

The real advantage for agentic AI systems lies in natural language processing. Instead of being confined by static process flows and technical jargon, agentic systems enable users to describe their needs in plain language, transforming those inputs into actionable workflows. The result is a fluid orchestration that adjusts based on real-time user inputs, external triggers, and evolving business needs.

From GUIs to Human-AI Interfaces

Traditional non-agentic systems rely heavily on GUI-based interactions between humans and computers, which has proven to create a brittle user experience. Over the past three decades, substantial investments have been made in improving GUIs and UX design across software, but a core problem remains. These interfaces require constant upgrades to maintain a "fresh" look and feel, resulting in a cycle of continuous redevelopment and user retraining.

Consider, for example, the evolution of MS Office, and the introduction of the infamous "Ribbon" interface, which disrupted productivity across its user base and caused them to relearn how to perform basic tasks. SaaS tools tend to undergo major UI overhauls periodically, requiring extensive retraining for employees and the risk of churn from users.

Today's GUIs are often rigid, limiting structures that need precise data models, extensive coding, and specific training for proper use. By the time users become proficient with a particular interface, it may change. If a CRM system has a specific workflow for entering customer data or creating reports, and this doesn't align with a company's own processes, users need to adapt their work to fit the software—rather than vice versa.

Agentic systems, on the other hand, introduce a paradigm shift by replacing direct human-computer interaction with a human-AI-computer model. In this new model, AI acts as the intermediary between human intent and computer execution. The interaction with AI occurs through natural language, eliminating the need for evolving GUIs and reducing the learning curve for users. And when GUIs are needed by users, agentic systems can generate those too on the fly.

Imagine an employee who wants to apply for vacation. In a traditional HR tool, she would need to navigate multiple screens and systems, starting with checking her remaining vacation days, reviewing all her ongoing projects, verifying if any are in a critical phase during her planned time off, and then formally applying for vacation in the right system. Her manager would then repeat similar steps while reviewing and (hopefully) approving the request.

But with an agentic HR system, the employee could simply type in, "I want to take 5 days off starting 1st Feb. Check all my projects and submit a request if everything looks good." Agentic AI would understand the context and intent, access the necessary systems, check project statuses and vacation balances, and submit the request if appropriate. The manager would receive a notification from the system, indicating the employee's vacation balance, the state of active projects, allowing for an informed decision.

The employee then receives a notification reflecting on the manager’s decision, and both have achieved the entire process without logging into more systems or navigating extra GUI screens. If visual representation or interaction is needed at any point, the AI dynamically generates the appropriate GUI, tailored to the specific task.

By shifting much of the functionality to AI agents, we can create more flexible, intuitive, and adaptable user experiences. Agentic AI can generate new ways to present data on the fly, is customizable to individual user preferences, and addresses the limitations of current rigid GUIs.

The Agentic Future: What Lies Next

As we consider the future of software and business automation, agentic systems are going to redefine how humans interact with technology. The concept of ‘neutrality’ will play a crucial role in this shift, as cross-application functionality without bias becomes essential.

Cross-application capabilities: Just as systems of record struggled to provide seamless cross-application analytics, non-agentic SaaS systems have difficulty providing true integration across different ecosystems. Agentic systems overcome this with a neutral stance, enabling effective integration without bias toward one ecosystem.

Data Integration without Bias: Agentic systems need to interact with diverse data formats and vendor-specific structures—doing this impartially enables richer and more effective integrations.

Avoiding Vendor Lock-In: One of the biggest concerns users have with traditional SaaS systems is the risk of vendor lock-in. Neutral agentic systems allow users to integrate with any platform, reducing that risk and empowering companies to evolve without constraints.

APIs as Connective Tissue: APIs will become the primary interface for agentic systems, connecting software components, databases, and services. This API-centric model allows for flexible interactions, adapting in real-time to changing needs.

Human-AI-Computer Models: With AI as the primary interaction interface, GUIs will become task-specific tools that are rendered based on what the user actually needs, eliminating the cognitive burden of inconsistent designs across different systems.

Unified Interaction Point: Instead of managing multiple logins and switching contexts, agentic systems will offer a single, unified interface where users can interact with all their digital tools seamlessly, primarily with natural language.

While existing SaaS vendors have an inherent conflict of interest in creating truly agentic systems, given they could cannibalize their current product offerings, we at Ema have built a horizontal agentic AI platform with the above technological shifts in mind, with a vision to make the relationship between humans and technology more seamless. Agentic Business Automation (ABA) is therefore not just an incremental improvement, but a leap akin to the industrial revolution, redefining how humans work with software, which can finally prioritize adaptability, personalization, and intelligence.