The AI Stack That’s Replacing SaaS Apps

A few years ago, the idea of adding “AI” to your processes felt like a checkbox—buy the software, plug it in, automate some emails, and call it transformation. 

Nowadays, organizations that are actually scaling AI aren’t betting on a single model. They’re building automation stacks—smart, layered systems combining different types of intelligence for different kinds of work. 

At the center of this shift are three components:  SLMs, LLMs, and AI Agents. 

Each plays a role. Each has its limits. The secret is how you combine them... 

Small Language Models (SLMs) 

SLMs are narrow, efficient, and reliable. They’re great at repeatable, rules-based tasks that require consistency but not much interpretation. Because they’re lightweight, they respond quickly and run cheaply. 

Let’s take an example to make it clearer. Suppose you’re a compliance analyst that needs to verify hundreds of KYC documents every week. An SLM, trained specifically on onboarding rules and formatting standards, flags missing signatures, detects outdated IDs, and validates form completion—all in real time. 

However, they cannot improvise. If a request falls outside of their training domain, they won’t know what to do. They need clear boundaries—and frequent updates if your processes evolve. 

Large Language Models (LLMs) 

LLMs can synthesize complex, unstructured information. They’re useful when context matters—whether it’s summarizing a call, drafting a message, or comparing qualitative data across sources. 

The downside? Cost and complexity. They’re slower, more expensive to run, and prone to hallucinations if left unchecked. You’ll need governance frameworks, human oversight, and infrastructure to keep them aligned with your business logic. 

AI Agents 

AI Agents are the 3rd wave of AI innovations. They act as a digital workforce by following logic, interacting with systems, and taking end-to-end action. So, they’re more digital coworkers than just tools. 

For example, in a loan processing workflow, an AI Agent pulls applicant data, verifies supporting documents, checks credit scores, calculates risk, and either approves the loan or escalates for manual review—all without human intervention. 

But because they act, they also introduce new risks: integration failures, unintended consequences, or decisions made outside of policy. So, they require the most upfront design and the strongest guardrails. 

Stacks Aren’t Silos—They’re Layers of a Single Architecture 

Now, how is this tendency to build automation stacks with various AI models compatible with the new end-to-end approach of AI agents in enterprise software?  

Automation stacks are intentional, interoperable layers built within (or alongside) unified platforms like Salesforce, SAP, or ServiceNow. And each layer—SLMs, LLMs, AI Agents—plays a different role in the same ecosystem: 

• SLMs handle microtasks (data validation, classification, routing) 

• LLMs support cross-functional reasoning (reporting, summarization, advisory) 

• AI Agents execute full workflows across systems (loan processing, case resolution, fraud detection) 

As we can see, these models don’t live in separate silos—they plug into the same data layer, follow the same governance rules, and support shared business goals. 

The real challenge: Putting Your AI Stack Together 

So, the industry shift is clear: enterprise software is moving toward composable, intelligent platforms—not scattered tools. The automation stack is simply how that intelligence gets applied in layers. 

But building an effective automation stack isn’t just about picking the right models—it’s about integrating them into a broader end-to-end architecture. And at Inclusion Cloud we’ve seen this firsthand.  

Most of the technical challenges we deal with aren’t model-related—they’re architectural. Things like: 

• Making sure data flows cleanly between systems 

• Exposing the right APIs so agents can act 

• Setting up audit trails so every AI decision is traceable 

• Balancing automation with human oversight in sensitive processes 

So, the engineering isn’t glamorous, but it’s essential. Our teams often spend more time wiring up integrations, defining guardrails, and working with legacy infrastructure than fine-tuning the models themselves. 

That’s the reality behind AI stacks: they only work when the foundation is solid (and we can help you with that).