In the 2010s, Software-as-a-Service (SaaS) revolutionized businesses with its predictable revenue, streamlined deployments, and user-experience (UX) centered platforms.
Today, a new technology curve is challenging those foundations: agentic AI. While some flirt with the idea of the end of SaaS, this moment is better understood as a strategic reframing.
SaaS founders shall ask a sharper question: Is my company building technology that agents can amplify or one they’ll bypass?
Understanding that distinction is now central to both product strategy and M&A positioning.
“This transformation is more than incremental. It redefines the relationship between how work is designed and how work is executed.”, explained tech experts Deep Nishar and Nitin Nohria in Harvard Business Review.
From software tools to AI agents: Understanding the shift
What is agentic AI?
Agentic AI refers to software agents capable of independently reasoning, making decisions, and completing multi-step workflows. Unlike simple automations or chatbots, these agents pursue goals, adapt in real-time, and work across systems.
These systems operate more like autonomous collaborators than software tools.
An example from IBM reads:
“In healthcare, agents can monitor patient data, adjust treatment recommendations based on new test results and provide real-time feedback to clinicians through chatbots.”
According to the Nielsen Norman Group, “it’s likely that AI agents will gradually augment or replace traditional support staff and interact directly with users' personal AI assistants”.
A real-life example is Operator, OpenAI’s agent that can perform tasks for you on the web.
“Operator can be asked to handle a wide variety of repetitive browser tasks such as filling out forms, ordering groceries, and even creating memes.”
What is SaaS?
Software as a service (SaaS) delivers software via the cloud with human users interacting through dashboards, menus, and forms. Its value proposition rests on recurring access, usability, and centralized feature sets.
Companies like Salesforce and Atlassian scaled by owning user workflows. However, if agents can bypass interfaces entirely and trigger backend functions through Application Programming Interface (APIs), then the user interface (UI), the core of many SaaS advantages, becomes optional.
Agentic AI vs Traditional Saas: Comparison snapshot
Why the agentic AI vs. SaaS debate matters in M&A
Technological changes have historically led to changes in mergers and acquisitions (M&A). What’s unique about this one is how quickly it’s shifting the definition of value.
Traditional SaaS earned its value through intuitive interfaces and recurring usage, advantages that still matter. However, as agentic AI systems become increasingly capable of interacting directly with APIs, buyers are also beginning to assess how well a product integrates into an agent-driven workflow.
This doesn’t diminish the value of strong UX or engagement metrics, but it does add new layers to the conversation about deal value. Companies that pair UI excellence with modular, agent-compatible architecture may find themselves especially well-positioned today.
Implications for product value in M&A
Agentic AI does not leave SaaS obsolete. It does, however, change what makes a SaaS company valuable.
UI/UX differentiation
For a long time, a superior User Interface (UI) and User Experience (UX) have been key differentiators in SaaS companies. A beautiful, intuitive, and easy-to-use dashboard or interface could make a product more appealing, lead to higher adoption, and create "stickiness" (making users reluctant to switch).
What is changing
AI agents don't need dashboards because they operate via APIs. They prioritize speed, reliability, and clear documentation over visual design. For these systems, your product's' user experience” refers to how efficiently agents can accomplish tasks through structured interfaces.
Implications
- APIs become the interface: Invest in developer-first design: clear endpoints, stable versioning, predictable responses.
- Headless services win: If your product’s core value can be consumed without UI, lean into it.
- UI still matters, for oversight: Human admins might still need a control layer to monitor agents, but it won’t be the primary interaction surface.
Bundled features
Software suites that consolidated many related features were often seen as more valuable (e.g., Microsoft Office, Adobe Creative Suite). The idea was to provide a one-stop shop, improving workflow efficiency by having everything tightly integrated in one place. This bundling created a strong competitive advantage.
What is changing
Agents can now dynamically compose workflows across multiple services using APIs. They don’t need a pre-integrated bundle because they create their own toolchains.
Implications:
- Composable architecture is a strength: Products that expose discrete capabilities can plug into agent workflows more easily.
- Best-of-breed beats bundled: Agents prefer flexibility and specialization over general-purpose monoliths.
- Orchestration is shifting to AI: The "suite logic" now happens at the agent layer, not inside a single product.
Engagement metrics
High user engagement (e.g., daily active users, time spent in app, feature adoption rates) has long been a key indicator of a product's success. High engagement was thought to build a strong moat or strategic defensibility because it implied users were deriving significant value and were deeply embedded in the product.
What is changing
If agents can generate the same output by calling a few APIs, then human usage metrics may lose value. The outcome is what matters, not the hours spent achieving it.
Implications:
- Focus on uncopyable assets:
- Proprietary data
- Specialized models
- Deep integrations into enterprise workflows
- Measure outcomes, not clicks: Success = how well agents achieve objectives (speed, accuracy, cost), not how long a human stayed in-app.
- Beware wrapper apps: If your product is just a thin interface on top of open models or data, it’s easy to replicate.
How founders can prepare for the agentic AI shift
Agentic systems don’t just replace software, they reconfigure the layers that matter. Founders must adapt at multiple levels:
1. Make your product agent-ready
- Design APIs and modular architectures that agents can easily interact with.
- Embed internal agents to showcase native orchestration.
- Build tooling that supports agents: scheduling, prioritization, exception handling.
2. Reframe your go-to-market narrative
- Move from “we do it all” to “we power what matters.”
- Position yourself as infrastructure for agents: data providers, connectors, and validators.
- Speak the new language of orchestration, autonomy, and integration—not just features.
3. Build defensibility beyond the interface
- Strengthen proprietary data and signals agents need.
- Protect domain-specific logic that can’t be generalized.
- Cultivate ecosystems: integrations, developer networks, and channel partners that agents rely on.
4. Engage with buyers early
- Inicie conversaciones con nubes de plataforma, líderes en RPA y agregadores verticales que están construyendo ecosistemas de agentes.
- Asóciese con herramientas nativas de IA que gestionen otras partes del flujo de trabajo de los agentes. Vender a un cliente compartido demuestra cómo encaja su producto en una pila multiagente.
- Documente los casos de uso compatibles con agentes en su Memorándum de Información Confidencial (CIM) y en su hoja de ruta de producto.
¿Necesita replantear su estrategia?
L40° ayuda a los fundadores a adelantarse a la disrupción de la IA agéntica en el SaaS. Entendemos tanto los puntos de inflexión técnicos como la dinámica de las operaciones, y trabajamos con usted para reformular su narrativa antes de que las fuerzas del mercado dicten sus acciones. Si está reevaluando su posición o cree que debería hacerlo, hable con un asesor de L40°. Contáctenos hoy mismo.
