The deterministic precursor to AI agents

Cicero Inc. is a survivor of the first major wave of enterprise automation. Founded in 1988, the company established itself long before Large Language Models or autonomous agents became the industry standard. For decades, it has operated in the realm of Robotic Process Automation (RPA) and process intelligence, building the plumbing required to monitor and automate human-computer interactions in high-volume environments like contact centers.

The company's technological thesis is built on "Activity Intelligence." While many modern AI companies start with the model, Cicero starts with the telemetry. Their software sits at the desktop level, observing how employees interact with various applications. This approach allows organizations to see where friction occurs—such as a customer service representative manually copying data from a legacy database into a modern CRM—and then automate those specific actions.

Activity Intelligence vs. Generative Reasoning

What distinguishes Cicero from the current crop of AI startups is its focus on deterministic automation. Most modern agents use probabilistic reasoning to navigate interfaces; Cicero uses a more traditional, rules-based approach informed by deep process analytics. The advantage in an enterprise context is predictability. In a regulated contact center, a company often prefers a bot that follows a strict script over an agent that might hallucinate a new procedure.

However, the rise of the Agentic Web has put companies like Cicero in a unique position. The "Activity Intelligence" they collect is effectively a high-fidelity training set for the next generation of LLM-based agents. By capturing exactly how a human performs a complex task across multiple windows, Cicero provides the "demonstration data" that modern agent developers crave for fine-tuning models.

Enterprise legacy and market position

Cicero is a small, focused player, reporting between 11 and 50 employees and operating largely in the shadow of giant automation firms. Their primary value proposition is simplicity of use and ease of implementation. They target the "back-office"—the invisible engine of insurance companies, banks, and retailers—where legacy systems are too expensive to replace but too inefficient to leave un-automated.

Their software acts as a bridge. It integrates with existing enterprise systems for customer service, allowing companies to extend the life of older software by wrapping it in a layer of automation. While they have not transitioned into a "pure-play" AI agent company in the Silicon Valley sense, their infrastructure represents the necessary transition state between manual human workflows and the fully autonomous enterprises of the future. They prove that before you can automate a process with an agent, you first have to understand the activity that defines it.