Agentic AI

Agentic AI refers to artificial intelligence systems that go beyond generating a single response to a single prompt. Instead, they autonomously plan a sequence of actions, execute those actions, observe the results, and adapt their approach until a goal is achieved. Where traditional AI models produce an output and stop, agentic AI operates in a loop: reason, act, observe, adjust, repeat.

The distinction is important because it marks a shift from AI as a tool you use to AI as a collaborator that works alongside you. A non-agentic AI can draft an email. An agentic AI can research the recipient, draft the email, schedule the send, follow up if there is no reply, and adjust its approach based on engagement signals. The difference is not intelligence. It is autonomy and persistence.

Agentic AI has become the dominant approach for applying large language models to real-world business processes. Rather than asking humans to decompose every task into model-friendly prompts, agentic systems accept high-level goals and figure out the intermediate steps themselves. This makes them particularly well-suited for complex, multi-step workflows that span multiple tools and interfaces.

Why it matters for SaaS

SaaS companies face a structural problem: the workflows their products enable are becoming more complex while users' patience for learning those workflows is shrinking. Agentic AI addresses this tension by turning complex multi-step processes into goal-driven experiences. Instead of training users on 15 screens, you can build an agent that accomplishes the same outcome by navigating those screens on the user's behalf.

The business impact extends across the entire customer lifecycle. In sales, agentic AI can deliver adaptive product demonstrations that respond to prospect questions and pivot between features without a human in the loop. In onboarding, it can guide new users through setup workflows that would otherwise require a dedicated customer success manager. In support, it can troubleshoot issues by actually navigating the product to reproduce problems and test solutions.

For PLG companies in particular, agentic AI solves the scale paradox. Product-led growth depends on users finding value without human help, but most enterprise software is too complex for that. Agentic AI provides the guided, personalized experience of high-touch onboarding at the cost structure of self-serve. McKinsey estimates that agentic AI could automate up to 30% of work activities across SaaS operations by 2028, with the earliest gains concentrated in customer-facing workflows like demos, onboarding, and support.

How it works in practice

Consider a SaaS platform for financial planning that requires new customers to connect bank accounts, configure budget categories, set up forecasting rules, and invite team members before the product delivers meaningful value. Today, a customer success manager walks each new account through this process on a Zoom call. It works, but it costs $200 per onboarding and limits the company to handling 40 new accounts per week.

With agentic AI, the system accepts a simple goal: "help this user complete their initial setup." The agent observes the current state of the product, determines which setup steps remain incomplete, and works through them one by one. It navigates to the bank connection screen, explains what is needed, guides the user through authentication, and then moves to category configuration. If the user has questions, the agent answers them conversationally. If the user wants to skip a step, the agent adjusts the plan and returns to it later.

The key characteristic is that the agent is not following a rigid script. It is pursuing an objective and adapting based on what it encounters. If the bank connection fails due to a timeout, the agent does not crash. It recognizes the error, suggests the user try again or switch to manual entry, and continues toward the goal. This resilience is what distinguishes agentic AI from traditional workflow automation, which breaks at the first unexpected state.

Agentic AI vs Conversational AI

Conversational AI is designed to hold a dialogue. It listens to what you say, generates a relevant response, and waits for your next input. Think of customer support chatbots, virtual assistants, and AI writing tools. They are reactive. The conversation is the product.

Agentic AI is designed to accomplish tasks. It may use conversation as a communication channel, but the conversation is a means to an end, not the end itself. An agentic system does not just tell you how to configure a dashboard. It configures the dashboard, narrating what it is doing along the way.

The practical difference for SaaS founders is clear. Conversational AI deflects support tickets by answering questions. Agentic AI resolves support tickets by actually fixing the issue. Conversational AI can explain how to set up an integration. Agentic AI can set up the integration. When evaluating AI investments, this distinction determines whether you are buying a better FAQ or a digital worker.

How Floe approaches this

Floe applies agentic AI across the full customer lifecycle rather than siloing it into a single use case. The same AI agent that demonstrates your product to a prospect can onboard that prospect after they sign up, support them when they encounter issues, and guide them to advanced features as they mature. The agent operates on your live product, observing screens, making decisions, and executing actions in real time.

This approach avoids the fragmentation problem that many SaaS companies face when they adopt AI piecemeal: one chatbot for support, a separate tool for demos, another for onboarding. Floe's agentic architecture means there is a single system that accumulates product knowledge and applies it wherever a user needs help, adapting its approach based on where the user is in their journey.

FAQ

What is the difference between agentic AI and automation? Traditional automation follows pre-defined rules and scripts. If the conditions change, the automation breaks. Agentic AI uses reasoning to adapt to unexpected states, recover from errors, and find alternative paths to a goal. Automation works for predictable, stable workflows. Agentic AI works for dynamic environments where the exact sequence of steps cannot be pre-determined, which describes most SaaS user interfaces.

Is agentic AI reliable enough for customer-facing use cases? Reliability has reached production-grade levels for structured tasks like product navigation, form completion, and guided workflows. The key design principle is graduated autonomy: let the agent handle routine actions independently while confirming with the user before high-stakes decisions. Combined with fallback mechanisms and human escalation paths, agentic AI can deliver consistent experiences across thousands of interactions.

How is agentic AI different from robotic process automation (RPA)? RPA records and replays fixed sequences of clicks and keystrokes. It is brittle: any change to the interface breaks the script. Agentic AI perceives the current state of the interface and decides what to do in real time. It can handle UI changes, unexpected dialogs, loading delays, and novel states that were never explicitly programmed. RPA automates a specific path. Agentic AI pursues a goal across whatever path is available.