Inside the emerging AI agent ecosystem: infrastructure, intelligence, control
Artificial intelligence is redefining software architecture. It has moved beyond a supporting role to function as independent agents interacting dynamically with applications. These new AI-driven systems can analyze data, automate tasks, and coordinate workflows across multiple platforms, fundamentally changing how software operates and delivers value in the SaaS ecosystem.
Businesses trying to operate within this environment must understand the AI Agent stack. This stack consists of three core layers, each representing a distinct role in the AI-driven ecosystem:
- Infrastructure: The foundation that provides APIs, data, and tools for agents to operate.
- Intelligence: The “brain” of AI agents, typically powered by large language models (LLMs) or specialized AI models.
- Control: The orchestration layer enables decision-making, planning, and execution across different applications.
Those who understand this new ecosystem and adapt by providing robust APIs, embedding intelligence, or orchestrating complex workflows will position themselves for success in an AI-first future. The following sections will break down each layer of the AI Agent Stack, helping SaaS companies identify where they fit and how they can thrive in this agent-driven era.
Infrastructure (tools and data layer)
This is the bottom layer comprised of connectors, data sources, and execution environments that agents rely on. An easy way to think of it is that this layer is the “arms and legs” of an AI agent.
This layer includes tools (APIs) for agents to act on (e.g., SaaS apps’ API endpoints), as well as memory stores and databases where context is kept. Many SaaS products will live here within the agent-driven architecture.
They will be stored here as specialized tools or data services that agents call upon. Traditional SaaS offerings must ensure their infrastructure layer is solid (scalable, secure, API-accessible) because agents will interface at this level.
Intelligence (AI/LLM layer)
The middle layer is the AI “brain,” which is the layer that does all the thinking. This layer is usually a Large Language Model (LLM) or similar AI model that can understand instructions, reason, and generate outputs. The LLMs within this layer provide the “conscious thought” in AI agents.
They interpret user goals (“schedule a meeting with client X and update the CRM”) and determine how to execute. This intelligence layer may come from third-party providers (OpenAI, Anthropic, etc.) or domain-specific models fine-tuned by SaaS companies on their data.
Either way, it’s a relatively commoditized layer. Powerful models are widely available, so most SaaS firms won’t win by only having an AI model.
Control (orchestration/agent layer)
The top layer is agent orchestration and autonomy. This is where the agent software plans actions and calls tools. The logic decides which tool (or which SaaS API) to use, when, how to combine steps, and how to handle errors or exceptions. Think of this as the new “operating system” that sits above individual apps.
This control layer manages complex workflows, such as the agent breaking a goal into steps, invoking the needed SaaS APIs, monitoring progress, and then adapting. Many new platforms and frameworks are vying to provide this orchestration layer, and it’s within this control layer that much of the traditional app-specific logic is migrating.
These layers together form the emerging ecosystem of “agent software.” SaaS companies must determine where they fit in this stack. Some might supply the infrastructure/tools (e.g., best-in-class API for payments or data storage).
Others might contribute to the intelligence layer (e.g., train a domain-specific AI model). Ambitious ones might even build a control layer (e.g., an agent specializing in a particular workflow and orchestrating many tools, effectively becoming a new kind of SaaS).
Understanding how these layers function is essential, no matter which layer SaaS companies choose to focus on. These layers highlight a fundamental shift in the value chain. Competition is no longer just about complete applications but also about dominating specific layers within the AI-driven stack.
The landscape of SaaS in an AI-agent era
As AI agents become more capable, SaaS companies must rethink their role. The traditional competitive advantage of SaaS—offering the best UI or the most feature-rich application—is giving way to this more layered approach. Companies that fail to adapt risk becoming obsolete, while those that embrace this shift can redefine their value proposition in an AI-first world.
This transition is not just a technological evolution; it’s a strategic one. In Part 3, we’ll explore how SaaS businesses can thrive in an AI-agent future by repositioning their offerings, redefining success metrics, and ensuring they remain indispensable in a world where AI is the primary user.
Whether by becoming the best infrastructure, embedding intelligence, or taking ownership of the control layer, the key to success lies in adapting, integrating, and leading in this new paradigm.
