Copy of AI Agents will drive SaaS transformation by Vibhu Kuchhal
TL;DR – SaaS products are here to stay but will?need to evolve to support the AI based experiences.
A lot has been discussed and written about the future of SaaS, with numerous predictions shaping the conversation. What can be said with certainty is that the Software-as-a-Service (SaaS) industry is undergoing a seismic shift, driven by the rise of Artificial Intelligence (AI) based capabilities. We have been seeing AI enabled features lighting up into major SaaS products over the last year, while the competitors catch up. With new, improved AI models getting more and more frequently, innovations in how we leverage them are bound to accelerate.
The AI agents are however capable of shifting the whole user experience paradigm. These intelligent, autonomous systems are not just enhancing our software but revolutionising how we interact with technology. Let’s dive into how AI agents are reshaping the SaaS landscape and what this means for businesses and users alike.
The Basics
Software-as-a-Service (SaaS)
Software-as-a-Service?is a cloud-based service where applications are hosted by a service provider and made available to customers over the internet. This model allows users to access software applications on a subscription basis without the need for extensive hardware or software maintenance. SaaS offers scalability, cost-efficiency, and ease of use, making it a popular choice for businesses of all sizes.
These services are highly specialised, encapsulating user interface / experience, business logic and associated data and capable of providing best-in-class solutions for their intended use case and user base. The flexibility however comes with a price:
The costs and shortcomings are well known in the industry and cost-benefit analysis exercises undertaken to decide the matching product for individual organisational circumstances. Some SaaS providers have addressed the challenges by expanding products with added capabilities such as API / Webservices enabled Integrability, Event based actions, data exports for reporting and more.
Artificial Intelligence (AI) Agents
AI agents are advanced, autonomous programs designed to perform specific tasks with minimal human intervention. Unlike traditional automation systems, they are context-aware, goal-driven, and capable of dynamic adaptation. Think of them as your digital assistants, but on steroids!
Key Capabilities of AI agents
Reasoning and Contextual Awareness: AI agents can handle multi-turn interactions and solve complex problems, such as cross-departmental task management.
Memory and Learning:?They retain context over time, enabling personalised recommendations and adapting based on user interactions and historical data.
Multimodal Capabilities:?AI agents can process text, images, and other data types simultaneously, making them versatile across various applications
The Problem
I have firsthand experience working with or talking to customer stakeholders struggling with several SaaS based and organisational applications copying data from one application to another just to enable a well-defined, consistent view across the whole of organisation. This data duplication is manual, error prone and deeply frustrating for the users and takes a significant percentage of valuable time which could have been better utilised elsewhere.
You say why not automate that all?
Yes! That has been tried more than once and there have been successes, but only for the well-defined scenarios. The code just didn’t have an understanding, and the “program” could only do what was “coded” and we would always require a human in the middle.
Looking at the Generative AI capabilities and ongoing associated technological improvements, utilisation of AI Agents as a part of the user flow is a promising solution to address the stated challenges.
Can AI agents achieve this all by themselves?
Well, I can’t say no. But what I believe is not by themselves.
The solution – SaaS Transformation?
AI-powered agents are transforming the SaaS landscape by automating routine tasks. Instead of employees using multiple systems, AI can handle these processes for a more efficient experience.
For example, a sales representative can ask an AI agent to update contact details or generate quotes without logging into a Customer Relationship Management (CRM) system. The AI agent will gather data from various systems and complete the tasks.
This shift to conversational AI increases productivity, allowing teams to focus more on core tasks rather than navigating enterprise systems. AI agents have the potential to revolutionise business operations, from supply chain management to customer service, by automating repetitive tasks and enhancing user experiences.
As should be clear from the earlier examples, the users have started to expect the following base requirements from SaaS perspective:
We will now examine how the SaaS architecture may need to adapt to meet the specified requirements.
Technical Architecture
The figure below presents a highly simplistic architectural components for a typical SaaS product:
I envision the SaaS product architecture evolving to accommodate AI agents, as shown in the figure below:
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The user Interface and API layer shall be retained to support current and specialised actions. So will be the other layers such as Business logic, product core automations and Data stores. The architecture shall evolve to accommodate AI Agents as added capabilities. ?
Let’s consider each of the components:
AI agents – Segregation of responsibilities
AI agents shall be broadly categorised into two types: External Agents and Internal Agents. Each type shall play a distinct role in enhancing business operations and optimising user experiences.
External AI Agents will be primarily used for facilitating interactions with external AI agents. These agents would be intimately aware of the application context and help translate requests from external sources. Their primary functions include:
We envision these agents to be available as “connectors” in marketplace constructs, providing seamless integration with various external AI services.
Internal AI Agents shall be used to optimise and improve user flows within the organisation, as well as augment capabilities of External AI Agents. Their capabilities include:
These agents shall work behind the scenes to enhance the efficiency and effectiveness of internal processes, ensuring that user experiences are continually optimised and refined.
AI agents shall however require the original implementation to be “compatible.” While the AI agents will be able to orchestrate the flow, they would need to be supported by more specialised services capable of undertaking specific actions such as CRUD operations, scheduling an action, updating a configuration, calling external endpoints. This is very much similar to the usage of skills by digital agents such as Amazon’s Alexa or Apple’s Siri.
Enter Microservices!
Need for Microservices
To enable a consistent and efficient implementation of AI agents, a modular and microservices-based approach is paramount. This architectural framework is crucial for promoting reuse and ensuring seamless integration across various applications and AI based agents within an organisation.
Consistency in Implementation
A modular design enables the standardization of components, which can be independently developed, tested, and deployed. This consistency is vital for supporting uniformity in how AI agents interact with data stores, process information, and execute tasks. By breaking down functionalities into discrete services, organisations can ensure that each module adheres to defined standards and protocols, reducing discrepancies and enhancing reliability.
Promotion of Reuse
Microservices architecture fosters reuse by allowing individual services to be utilised across different applications and contexts. In the realm of AI agents, this means that once a service is developed to handle a specific task, such as data retrieval or event-triggered actions, it can be reused in multiple scenarios without the need for redundant development efforts. This reuse not only saves time and resources but also ensures that proven solutions are consistently applied across the board.
Scalability and Flexibility
A microservices-based approach provides unparalleled scalability and flexibility. As the needs of the organisation evolve, individual services can be scaled independently, ensuring that the system can handle increased load without compromising performance. This scalability is particularly important for AI agents, which may need to process vast amounts of data and perform complex tasks in real-time.
Enhanced Collaboration
The modular nature of microservices encourages collaboration both within and outside the organisation. Internal AI agents can seamlessly collaborate with external and third-party AI agents, using their capabilities to enhance overall functionality. This collaborative environment is essential for creating a cohesive ecosystem where diverse AI agents contribute to a unified goal.
Improved Maintenance and Updates
With a microservices architecture, maintenance and updates become more manageable. Instead of overhauling an entire monolithic system, individual services can be updated or replaced with minimal disruption. This agility is crucial for AI agents as it allows for continuous improvement and adaptation to changing requirements and technological advancements.
Conclusion
Is the SaaS era ending?
Not at all; but it’s evolving. The emergence of AI-powered agents represents a new phase. These agents enable companies to streamline their portfolios and increase adoption rates. SaaS platforms continue to provide value by offering infrastructure, pre-built functionality, data management, permissions control, and protection from external threats.
In the future, it is expected that AI agents will allow users to complete tasks without concern for the underlying systems or tools. AI agents will understand user intent and act autonomously, while SaaS platforms manage data and functionality.
This transition will take time, but the benefits of AI-powered agents are clear. They have the potential to simplify processes and transform enterprise software.
Arinco blog by Vibhu Kuchhal
Oh absolutely, from lead capture agents to appointment scheduling, customer support, CRM integration, I could go on… AI has changed the game and it’s here to stay! Ai doesn’t sleep, take breaks or call in sick.