Exploring the strengths and weaknesses of agentic AI and the plausibility of its wide-spread adoption.

Tech circles are buzzing about whether autonomous AI agents will kill traditional SaaS or just make it smarter. Even Satya Nadella has suggested that agentic AI could upend the entire SaaS model. Others argue that AI will enhance SaaS rather than replace it, and that technological shifts typically create hybrids rather than extinctions.

Before we predict SaaS’s fate, let’s understand what agentic AI actually brings to the table, where it stumbles, and how it might play out across industries.

What Makes Agentic AI Different

Agentic AI refers to AI systems with agency. They autonomously perform tasks on behalf of users by planning workflows and utilizing tools rather than just responding to single prompts. These agents can analyze data, make decisions, and execute actions with minimal human input. A well-designed agent can continuously monitor and react to events. The idea is that they can take initiative, chain together multi-step processes, and self-adapt based on feedback or changing conditions (and most importantly, get things done on their own.)

Before we predict SaaS’s fate, let’s understand what agentic AI actually brings to the table, where it stumbles, and how it might play out across industries.

Automation at Scale & Adaptability

Unlike conventional software that follows preset functions, AI agents independently and dynamically determine what needs doing and how to do it. A single agent can hop between multiple applications to complete end-to-end workflows without someone clicking a button. Picture this: you ask for a monthly compliance report. The agent logs into your financial systems, retrieves data from spreadsheets, extracts information from emails, and generates the report automatically. This cross-system orchestration extends far beyond what any single SaaS app can manage alone.

Scalability is another strength. These agents work around the clock, handle multiple tasks simultaneously, and scale up without additional human labour. A well-designed agent can manage your marketing budget 24/7, continuously monitoring ad performance and adjusting strategies in real time.

But perhaps the biggest selling point is adaptability. Agentic AI generalizes from its knowledge and determines solutions through probabilistic reasoning and pattern recognition rather than relying on deterministic logic. If a workflow changes or new data appears, the agent adjusts instead of breaking. UiPath (a leader in automation) describes agentic AI as “enabling machines to understand context, adapt to new information, and collaborate with humans to solve complex challenges, essentially redefining what automation can achieve.” To summarize, agentic AI brings together the flexibility of AI (to handle nuance and variability) with the efficiency of software (to execute at high speed and volume).

The Serious Limitations

For all its promise, agentic AI has critical flaws that prevent it from taking over every software job. These revolve around trust, reliability, and control.

Advanced AI agents often behave like black boxes. They make decisions in ways that aren’t transparent to users. Why did the agent choose strategy A over B? What assumption led to that choice? In regulated industries like healthcare and finance, you *must* explain and justify your decisions. An AI agent that can’t provide audit trails is a non-starter for critical use cases.

Reliability is another issue, especially in edge cases. AI agents can be brittle when faced with scenarios outside their training. In the real world, edge cases are the norm, not the exception. A slight deviation will cause an autonomous agent to misfire or behave erratically. For example, an agent tasked with optimizing supply orders might misinterpret unusual inventory data and place incorrect orders across dozens of vendors. (Or order a lot of meat… Anyone remember Son of Anton?)

Or a research assistant might confidently incorporate misinformation into a report since it lacks true common-sense judgment. One mistake can compound into many before anyone notices.

Right now, human oversight remains absolutely necessary. As these systems gain autonomy, the stakes of their actions rise dramatically. An agent might technically achieve your goal, but not how you intended. Told to reduce customer service resolution time, an unchecked agent might start closing tickets prematurely, solving the metric but hurting actual customer satisfaction.

Without human guidance, an agent’s pursuit of an objective can easily diverge from human values or business intent. Many organizations view “human-in-the-loop” as non-negotiable: the AI can draft or execute tasks, but a human supervisor acts as both a safety net and a moral compass.

Where AI Agents Might Replace SaaS

If agentic AI can automate much of what SaaS does, which industries might see absolute displacement? The answer varies by sector.

Marketing & Sales: Teams juggle multiple SaaS platforms today, and an agentic AI could orchestrate across all of them. It might manage digital ad campaigns autonomously, continuously monitoring performance, adjusting budgets, refining targets, and generating new creative assets. In sales, a similar agent could qualify leads, draft outreach emails, and alert human reps when hot prospects need personal attention. But this doesn’t eliminate humans in marketing and sales. Creative strategy and relationship-based selling remain human strengths. However, it could replace many monotonous SaaS-driven tasks with self-driving processes.

Customer Service: Zoom recently introduced an “Agentic Virtual Agent” that can autonomously handle returns or schedule appointments without human intervention. An AI agent in customer service can understand requests, look up customer info across backend systems, take appropriate action, and respond all within one automated workflow. This goes beyond your static FAQ bot. It’s a flexible service rep that works across your SaaS tools. We can imagine AI agents handling routine queries while human agents focus on complex, high-empathy interactions.

Finance & Accounting: Finance teams spend an enormous about of time on data reconciliation, report generation, and compliance checks across multiple software tools. Now, picture an agent that extracts information from invoices, updates the accounting system, and emails a summary to the relevant manager automatically. It sounds great, but the finance sector is heavily regulated, and any AI agent would be under strict oversight. We’re likely to see agentic AI integrated into fintech as intelligent assistants rather than completely replacing core financial systems in the near future.

Software Development: AI copilots already assist developers, but agentic AI takes it a step further. Tools like Devin are positioned as AI software engineers that can independently complete coding tasks. Devin can plan and execute complex development work requiring thousands of decisions by reading documentation, writing code across multiple files, running tests, debugging, and deploying applications. This suggests that agentic AI could take over monotonous grunt work, such as boilerplate coding, bug fixing, and API integration. Developers would be able to focus more on high-level architecture and creative problem-solving.

Healthcare: Agentic AI could streamline processes across administrative SaaS systems, and on the clinical side, early agents monitor real-time patient data and help with care coordination. An AI agent may continuously watch vitals and lab results, and if it detects a concerning trend, it can automatically adjust treatment recommendations or alert clinicians. Due to high stakes, though, any AI actions in healthcare must be overseen by medical professionals. Near-term, we can look for AI agents in supporting or secretarial roles, like automating paperwork, triaging inquiries, and assisting clinicians by analyzing data (like Ellipsis Health’s Sage.)

The Pattern That’s Emerging

Across industries, agentic AI is likely to replace the UI and workflow layer of SaaS. Instead of people manually using applications, an AI agent with backend access could accomplish tasks faster and more fluidly. The SaaS applications might still exist in the background, but the AI agent becomes the new interface layer. This is the “headless SaaS” concept. Launching a sales campaign can be as simple as telling an AI agent your criteria and message, and it handles updating the CRM, email marketing tool, and analytics setup behind the scenes.

One area where agentic AI truly excels is taking over the monotonous, repetitive tasks that suck up human time. These are the tasks that come with using SaaS tools: clicking, copying data, pasting data, running reports, sending routine emails, and testing. AI agents don’t get bored, and they always operate at computer speed.

Web research and aggregation is another example. Gathering information from dozens of websites is mind-numbing for humans but trivial for AI. An agent can check shipping availability across hundreds of supplier websites and instantly compile an accurate delivery plan; a task that might take humans hours is completed in minutes.

Report generation follows the same pattern. Instead of manually querying multiple tables and exporting data, you ask in plain language for the analysis you need, and the agent produces it in seconds rather than hours.

Why Humans Remain Essential

Even the most optimistic AI experts acknowledge that human input, creativity, ethics, and relationship-building remain essential. The human touch isn’t going anywhere.

AI lacks true originality and abstract reasoning. It works off patterns. It can remix but not invent. Humans can, and we excel at making creative leaps and thinking in big-picture terms.

AI agents also lack moral compasses and emotional intelligence. They’ll do exactly what they’re told, which means human oversight is required to ensure outcomes align with ethical standards. Many business interactions benefit hugely from empathy and relationship-building, which AI cannot genuinely replicate.

Humans also provide contextual judgment and common sense. A human in the loop can catch when AI veers into nonsense or when decisions don’t make sense in a broader context. Human critical thinking ensures AI suggestions get sanity-checked.

Augmentation, Not Obliteration

History suggests that major tech revolutions and shifts expand ecosystems rather than destroy them. The cloud didn’t kill on-premise software overnight. Mobile apps didn’t kill the web. I believe Agentic AI will coexist with and reshape SaaSrather than replace it in one fell swoop.

We’ll undoubtedly see convergence and hybrid models, where SaaS vendors incorporate AI agents and AI platforms leverage existing SaaS infrastructure. Shortly, many agentic AIs will sit on top of SaaS tools. Over time, if those agents prove their worth, some SaaS interfaces may fade into the background. But the core business logic won’t vanish. The companies that succeed will embrace this change early and establish a new balance between AI automation and human expertise. They’ll utilize agents for pain points that everyone dislikes (such as repetitive busywork) while keeping humans heavily involved in creative, strategic, and interpersonal areas.

We’ll still have Software-as-a-Service, but it’ll be smarter, more autonomous, and built with AI capabilities. Instead of users adapting to software, software will finally adapt to users. It’s less ‘replacement’ than it is augmentation and evolution.

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DJ Leamen is a Machine Learning and Generative Al Developer and Computer Science student with an interest in emerging technology and ethical development.

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Special thanks to Melike Ceylan-Leamen for the topic of this week’s article!