After months of conversations with IT leaders, execs, and developers across different industries, I wanted to share a few insights about the critical design decisions companies are facing when rolling out AI agents—especially mid-size to enterprise organizations.
We’ve officially moved beyond the old SaaS playbook. AI agents don’t just assist humans; they act. That shift is forcing businesses to rethink how their systems are structured—from architecture and compliance to how teams collaborate with machines.
As Satya Nadella recently said, most business apps are just CRUD systems with logic built in, and that logic is now moving into the AI layer. When AI agents start orchestrating across databases, backends start to collapse. The same transformation is happening in UI and app layers—what once made sense for humans doesn’t necessarily make sense for agents. Platforms like Microsoft and ServiceNow are openly working to reduce the number of siloed apps and build unified systems that are AI-native by design.
Rethinking Architecture in the Age of AI Agents
How is this different from traditional SaaS?
Let’s take examples like ServiceNow or Salesforce. In the old SaaS model, software gave you tools—forms, workflows, dashboards—but the human was still responsible for driving the process step by step.
A typical flow might look like this:
- A ticket gets created
- You check it
- You decide what to do
- You run diagnostics
- You close the ticket
The system basically sat there, waiting for your input at each stage.
Now let’s look at how things change with AI agents. You define a goal—“resolve this ticket”—and the agent runs the entire process:
- Reads the issue
- Diagnoses it
- Takes action
- Updates the system
- Notifies the user
The shift is massive. It impacts how we design systems, how we think about compliance, and how we define the roles of the people involved.
5 Key Design Decisions to Make Before You Hit a Wall
Before jumping into full-blown implementation, there are some crucial design questions to answer. These will shape how your agents behave—and how reliable, safe, and scalable your solution becomes. And if you want to understand where the AI agent movement is heading, and how the stack is evolving, this article breaks it all down.
1. Autonomy
Does the agent act independently, or does it require human approval?
More importantly, what kinds of decisions should be automated—and which should stay human?
2. Reasoning Complexity
Is the agent following a set of fixed rules, or can it use LLMs to interpret vague or complex instructions?
3. Error Handling
What happens when the agent fails or runs into ambiguity?
Where do you place intervention points or fallback mechanisms?
4. Transparency
Can the agent explain why it made a decision, or does it just deliver outcomes?
How do you audit and monitor its behavior?
5. Flexibility vs. Rigidity
Can the agent adapt workflows on the fly, or is it locked into a script?
When Should Humans Stay in the Loop?
There’s one golden rule here:
The higher the risk, the more important human review becomes.
Some examples:
High-stakes tasks (need human review):
- Approving large payments
- Medical diagnoses
- Changes to critical IT infrastructure
Low-stakes tasks (can be automated):
- Sending standard emails
- Assigning a support ticket
- Reordering inventory
But risk alone doesn’t define where humans belong. Ambiguity is another key factor.
Structured vs. Ambiguous Tasks
Even tasks that look simple on paper can cause trouble if the input isn’t clear. We can split most tasks into two types:
🔹 Clear and well-structured
These are ideal for automation.
Example: Sending automatic payment reminders.
🔹 Open-ended or ambiguous
These usually require human judgment to interpret.
Example: A customer message like “My billing looks weird this month.”
Does that mean an overcharge? A missing discount? A double payment? The agent won’t know unless it can ask for clarification—or unless a human steps in.
Legal and regulatory boundaries
In some industries or regions, full automation isn’t even allowed. Regulations require a human to make the final decision, regardless of how smart the system is. That’s a design constraint you’ll need to account for early.
When does full automation make sense?
There are clear green lights for full autonomy:
✅ Tasks that are repetitive and structured
✅ When you trust the data and agent logic
✅ When the business or legal risk is low
✅ When there’s a fallback plan if the agent gets stuck
Multi-Agent Systems (MAS): Another Option for Complex Workflows
For more complex tasks, there’s a third option: use a multi-agent system. Rather than dropping a human into the loop, multiple specialized agents can collaborate to complete a process.
Take a product return in e-commerce, for example:
- One agent validates the order
- Another contacts the logistics partner
- Another processes the refund
Together, they complete the task faster and more accurately than a single generalist agent.
But MAS brings new questions:
- How do you ensure clear communication between agents?
- What if two agents propose conflicting actions?
- How do you keep the whole system transparent and auditable?
Who makes these design decisions?
Rolling out AI agents isn’t just a technical decision—it’s cross-functional. Here are some of the key people who need to be involved:
- Product Owner / Business Lead – defines goals and acceptable autonomy
- Compliance Officer – ensures decisions follow regulations
- Solution Architect – designs system logic and integration points
- UX Designer – defines how humans and agents interact
- Security & Risk Teams – assess risk levels and control mechanisms
- Operations Manager – monitors performance and tunes workflows
Final Thoughts: From SaaS to Agentic Architecture
21st-century enterprises need 21st-century infrastructure.
What worked for the SaaS era simply doesn’t hold up when you’re working with AI agents. We’re entering a new phase of enterprise software design—one where the platform isn’t a collection of disconnected apps, but a unified architecture.
This is the direction that tech giants like Microsoft and ServiceNow are heading. Satya Nadella has made it clear: the goal is to reduce the reliance on UIs and redundant apps, moving toward a world where business logic lives in the AI tier. One architecture, one data model, one platform.
And as Bill McDermott emphasized in his Knowledge 2025 keynote, even the concept of CRM as we know it is outdated. Why? Because customer service is no longer just a sales or marketing task—it’s an end-to-end responsibility. Every department, from the front end to the back office, contributes to the customer experience. Either it works seamlessly, or it doesn’t.
At Inclusion Cloud, we want to help you build an agentic architecture—a future-ready foundation that supports the next generation of enterprise automation. We offer strategic consulting and provide the resources you need to define and execute all the design decisions that come with implementing AI agents.
If you’re ready to move beyond the limits of SaaS and into a truly AI-native model, we’re here to help you make it real.
