Software-as-a-service (SaaS) providers have seen their business model evolve and, in many cases, fully transform. Instead of delivering software products and services that rely on human interactions to generate value, such as pulling reports from a customer relationship management or enterprise resource planning system, SaaS companies are now using agentic AI to learn and act autonomously to produce results.
The implications for SaaS providers are profound, significantly changing both the way they run their business and how they build, deploy, and monetize their software.
How Agentic AI Works
According to Enterprise Strategy Group (now part of Omdia), agentic AI refers to an AI system capable of autonomous action and decision-making. These systems can understand, decide, and act independently with minimal human intervention or oversight.
AI agents can think, plan, remember, and learn from their environment. They can adapt to changing conditions and improve over time to satisfactorily perform increasingly sophisticated workflows. To build and use these agents, SaaS companies need a comprehensive platform that’s built on a robust, scalable compute and storage infrastructure. The platform should offer a choice of different AI models for training and inference, integrated observability, and proven security.
To compete and take advantage of this rapidly evolving technology, SaaS companies need to partner with technology providers that have the tools and services to support their agentic AI framework. The providers should be able to offer immediate and substantial scalability for mission-critical workloads, simplified tooling, and secure data integration.
The Evolution of SaaS Into an Agent-Driven World
Demand for SaaS-based offerings will remain strong, but the long-term viability and appeal depend on how SaaS companies embrace the concepts of agentic AI and incorporate them into their solutions. This movement is already underway, as numerous SaaS vendors, such as Freshdesk, Zendesk, and Slack, have taken the first steps to integrate AI agents into their SaaS frameworks and offerings.
Agentic SaaS builds upon SaaS’s core principles of agility, cost efficiency, technical innovation, and economies of scale by acting as another catalyst for growth and innovation within existing SaaS models. Companies that can quickly create new agentic AI solutions or integrate AI agents into their current solutions architecture will drive new revenue opportunities and enhance the customer experience.
There is a lot to consider when looking to capitalize on agentic SaaS prospects. For instance, SaaS companies need to determine how they will introduce agents into their multi-tenant SaaS environment. They can:
- Productize the agents, where the agent itself is the SaaS offering and is delivered via an agent-as-a-service model.
- Embed the agents within existing SaaS offerings to extend or enhance the capabilities of those products and services.
- Put the agents in front of an existing SaaS application to orchestrate the underlying experience.
As SaaS companies evaluate the right technical path to follow with agentic AI, there are also a host of business considerations to appraise. They will need to:
- Select the right ways to position their capabilities against other SaaS competitors.
- Determine how to align and manage their sales efforts.
- Understand their pricing options.
- Put in place long-term solution roadmaps that make agentic AI an integral part of their long-term business and product strategies.
How AWS Can Help
Amazon Web Services (AWS) offers a wide range of tools and services to help SaaS companies optimize their agentic AI transformation to improve their competitiveness and long-term viability. Much of this starts with Amazon Bedrock and AgentCore, which offer a complete set of services for deploying and securely operating agents at scale.
AgentCore provides a secure, serverless runtime with complete session isolation and the longest-running workload available today. It provides tools and capabilities to help agents execute workflows with the right permissions and context, along with controls to operate trustworthy agents. AgentCore Runtime helps SaaS companies address the need for robust security and trust with integrated guardrails and identity verification, as well as memory isolation to prevent data leaks across agents.
Another key part of the AWS portfolio supporting SaaS companies on their agentic AI journey is the Amazon Nova customization in Amazon SageMaker AI. Nova models enable customers to customize the model across its development life cycle—such as pre-training and post-training, including both fine-tuning and alignment—with support for parameter-efficient fine-tuning (PEFT) and full fine-tuning. Nova represents a comprehensive suite of model customization capabilities for any proprietary model family.
Additionally, AWS offers a unique tool to help SaaS companies ensure the large language model they select interacts reliably and appropriately with their data with Amazon S3 Vectors. As the first cloud object store with native vector support, S3 Vectors help reduce vector storage costs by up to 90% while maintaining sub-second query performance. S3 Vectors enables agents to remember more, reason deeper, and maintain comprehensive context from every customer interaction, document, and business insight. S3 Vectors integrates directly with Amazon Bedrock Knowledge Bases for cost-effective retrieval-augmented generation applications and Amazon OpenSearch Service for tiered vector strategies.
Finally, a key capability that SaaS companies can leverage with AWS is the broad and deep AWS Marketplace. In fact, SaaS companies can buy AI agents and tools in AWS Marketplace with streamlined procurement and multiple deployment options. In today’s fragmented AI landscape, AWS Marketplace offers a centralized catalog of curated agents, tools, and solutions from AWS Partners.
For more information about how AWS works with SaaS companies in the agentic AI era, please check out this whitepaper.
