How AgentS2 and n8n Expand What LLMs Can See & Do

July 06, 2025

LLMs most impressive capabilities still revolve around generating text. But when it comes to navigating complex software interfaces or handling on-screen workflows, they need help. Here’s where Simular’s Agent S2 shakes things up/

Agent S2 is an open-source autonomous agent able to visually interpret and interact with user interfaces. Paired with n8n, a flexible open-source automation tool, Agent S2 can be orchestrated in workflows that tie together APIs, apps, and UIs. Obviously, Using this tools in combination can dramatically expand LLM’s power and visibility.

What are the implcations?

The LLM Visibility Gap

LLMs excel at processing language. They can summarize reports, generate code, and answer customer queries. But they’re blind to real-world interfaces. A model might know how to log into a system in theory, but it can’t see the login button or detect error messages. Recognizing a pop-up blocking its progress isn’t even part of the equation.

Without visibility into what’s actually happening in an interface, LLMs are left guessing. This is one of the key limitations in deploying LLMs for hands-on automation.

Agent S2 for Eyes and Hands

Agent S2 fixes this by acting as a virtual user. It can see the screen, understand visual elements, and carry out complex UI interactions. Its generalist-specialist architecture is especially helpful here, as it remembers and learns from past sessions.

Better yet, pairing Agent S2 with n8n workflows can serve as part of a larger automation pipeline. It might activate in response to an event, for instance (say, an alert trigger). That’s fairly powerful. But what happens when we bring an LLM into the loop?

Making LLMs Aware of the Interface

Real-time UX knowledge is where the possibilities get interesting.

Agent S2 logs its actions so: where it clicked, what elements were visible, what succeeded or failed. These logs can then be passed to an LLM. This gives the model access to real execution context, which it wouldn’t normally have.

For example:

• Did the Submit button disappear after the first click?
• Was a confirmation message displayed?
• Did the AI agent get stuck in a CAPTCHA loop?

Knowing these inputs can help an LLM generate more accurate summaries and recommendations for the next run. It could redefine CRO (conversion rate optimization).

From Theory to Reality

In traditional setups, LLMs operate on what users say or provide. With Agent S2, they can respond to what actually happened. That creates a new loop of intelligent automation:

• n8n triggers Agent S2 to execute a UI task.
• Agent S2 logs its steps and outcomes.
• The log is passed to an LLM for analysis or decision-making.
• The LLM adapts the next instruction set or flags issues.

In addition to improving automation accuracy, it also gives marketing teams better info on how automation behaves in real environments.

Rethinking Observability

A longer-term implication is generation of synthetic training data. Being able to accurately simulate customer journeys means Agent S2 can generate rich interaction histories that LLMs can fine-tune and use to inform marketers over time. You’re moving from giving the LLM data to giving it experience.

With retained learning taking place, you can now take practical steps to eliminate inbound marketing obstacles:

• Customer support help (what workflows cause the most friction?)
• Marketing funnel analysis (where do automated journeys drop off?)
• User interface optimization (where does the agent struggle that a user might too?)

LLMs Will Soon Have Real-World Awareness

LLMs will likely remain text-first models for awhile. But that doesn’t mean they will stay isolated from the user interfaces where work gets done.

Pairing an intelligent S2 agent with orchestration tools like n8n, and feeding the outputs into an LLM could create a hybrid system where:

• The agent sees and acts
• The LLM reasons and adapts
• The orchestrator connects and automates The result is a more informed automation—system, one that understand context not just from data inputs, but from lived execution.