Will AI Agents Replace SaaS? Or Make It Invisible?

AI Agents replacing Apps

What did Satya Nadella mean by “AI agents replacing apps”?

At Microsoft Build 2024, Satya Nadella laid out a bold vision:

“The application is being replaced by the agent… Business logic is moving into agents. The rest collapses into CRUD.”

In a follow-up conversation with investor Bill Gurley, Nadella went further:

“Business applications — they’re essentially just CRUD databases with a bunch of business logic. All that logic is going to move into the agent tier… That’s where everything collapses.”

What he means is this:Most enterprise applications (CRMs, ERPs, HRIS platforms) are primarily data storage (CRUD) plus logic on top — approval rules, pricing formulas, reporting conditions.

Nadella’s prediction is that in the agent-first future:

• The data remains in structured SaaS systems (as systems of record)

• The logic that defines how data flows, transforms, or triggers actions will live in AI agents

Instead of clicking through screens to configure workflows, users will interact with intelligent agents that understand intent and manage the logic directly via APIs, reducing the need to ever “open the app.”

This marks a shift from apps being the interface to becoming the infrastructure — while agents become the new execution layer.

Are companies actually using ai agents like this today?

Yes — major platforms already support agent-driven change:

• Salesforce Agentforce 2.0 allows agents to create fields and flows via the Metadata API

• Microsoft Copilot Studio can publish real workflows from a simple prompt(Docs)

• GitHub Copilot Workspace writes code, runs tests, and opens PRs autonomously(GitHub Next)

Do most users really customize their SaaS apps often?

Surprisingly, yes — just not always visibly.

While core workflows often remain stable, micro-customizations (like adding fields, tweaking logic, updating reports) happen regularly. In enterprise systems like Salesforce, Dynamics, and NetSuite, these small changes happen weekly across departments.

Gartner and Forrester both highlight that low-code adoption is driving non-technical teams to make more customizations, faster — often outside centralized IT.

These are exactly the kinds of changes that AI agents can observe, learn from, and automate, freeing teams from repetitive admin work.

Does this mean SaaS apps will eventually disappear?

Not likely — but the interface might.

As BCG puts it:

“Agentic AI converts static interfaces into dynamic collaborators.”(BCG 2024 AI Agents Report)

SaaS systems are becoming invisible infrastructure: still powering the business, but no longer something users directly touch every day.

What kind of tasks are agents best at today?

Agents are already useful for:

• Creating new fields, formulas, and flow logic

• Orchestrating pricing updates across CRM and billing

• Generating reports from unstructured documents or uploaded spreadsheets

• Automating config changes that previously required human admins

These are often high-friction, low-risk tasks — ideal for automation.

Can AI agents make lasting changes — not just temporary chat suggestions?

Yes — and this is the key difference between copilots and true agents.

Copilots (like those in Word or Excel) typically work in a transient way — showing results in a chat pane or offering suggestions. These aren't durable unless manually accepted.

In contrast, agents:

• Modify schemas (e.g., fields in CRM)

• Push to Git or deployment pipelines

• Generate real business logic (e.g., workflows, formulas, automation)

In other words, agents commit change, not just preview it. This turns them into real productivity drivers, not just assistants.

What’s the benefit over just using a chatbot or copilot?

Chatbots surface information.Agents act on it.

The evolution is:

Tool	Limitation	Upgrade
Chatbot	Only answers, no action	Agent executes
Copilot	Suggests change in UI	Agent pushes change via API
App UI	Manual, slow	Invisible logic, controlled by intent

Agents also log their actions (e.g., via GitHub or Gearset) — creating traceability without needing UI steps.

What does this look like in a typical business environment?

A real-world flow might look like:

“Add a field called ‘Sustainability Score’ to the Opportunity object. Average it to the Account level, then display it on dashboards.”

The agent:

• Creates the field via Metadata API

• Adds logic to flow or formula

• Updates report filters

• Opens a PR or syncs it to your deployment pipeline

This is how we’ve helped businesses streamline Dynamic Pricing, Forecasting, and Private LLM-driven systems.

How do agents help reduce the backlog of internal IT and operations requests?

Backlogs in IT and RevOps are often full of low-risk, high-friction requests:

• “Can you add this field?”

• “We need a formula to update for new pricing.”

• “Build this report for leadership.”

These tasks are usually too small for a dev sprint, but too technical for business users. As a result, they wait in queues.

Agents bridge this gap:

• Take natural-language intent

• Write or modify logic (via API or metadata)

• Commit changes safely through pipelines

This dramatically shortens delivery time for micro-changes — without burdening IT. Many of our Private LLM deployments are aimed specifically at clearing these “long tail” backlogs.

What types of teams benefit most?

This model is ideal for:

• Ops and RevOps: automating approval flows, pricing tiers, or reports

• IT teams: reducing config tickets and human bottlenecks

• Product and analytics: faster iteration, tighter feedback loops

Industries like finance, retail, and supply chain are leading adopters due to complexity and volume of change.

What’s needed to get started with AI agents?

1. Expose metadata or config APIs securely

2. Wrap agent actions in governance (CI/CD, approvals, logging)

3. Start with micro-changes (e.g. fields, reports, workflows)

4. Track change velocity — compare manual vs. agent-led cycles

   
   

If agents automate the interface, does SaaS still matter?

Yes — and it’s not going away.

Agents don't store your data, manage permissions, or provide regulatory audit trails. SaaS platforms still serve as:

• Systems of record

• Sources of truth

• Policy-enforced infrastructure

“Agentic AI will be embedded within SaaS platforms — enhancing them, not replacing them.”— Gartner 2024 

What’s the final takeaway?

AI agents aren’t here to replace your SaaS — they’re here to make it feel invisible.

If your team spends time clicking, configuring, or waiting on admin help, agent-based automation might be your next layer of acceleration.

Want to explore how this could apply to your environment? Visit phenx.io/contact to connect.