Our previous post intoduces SPAR.

Why SPAR Matters for AI Agents

SPAR highlights how AI agents go beyond simple automation. Instead of just executing pre-programmed steps, they are designed to perceive their environment, decide what to do, take action, and then learn from the outcome. This cycle makes them adaptive and increasingly effective over time.

An Example of SPAR in Action

Consider a virtual customer service agent. It senses the customer’s question, plans the best response by retrieving and reasoning over knowledge, acts by sending a clear answer, and then reflects by analyzing whether the customer followed up or expressed satisfaction. Each cycle makes the agent better at serving customers in the future.

SPAR offers a simple but powerful lens for understanding how AI agents work. It shows that effective AI is not just about acting, but about sensing context and reflecting on results. In this way, SPAR helps us appreciate both the promise and the responsibility of creating systems that can learn, adapt, and improve.

AI Agents in Action: Autonomous Vehicles

A self-driving car is an excellent example of the SPAR cycle in practice. It must constantly Sense its surroundings through cameras, radar, and other sensors. It then Plans the best route and driving maneuvers, taking into account road rules, obstacles, and traffic. It evaluates options, prioritizes actions, and coordinates resources. The vehicle Acts by steering, accelerating, or braking in real time. This ability sets AI agents apart from simple analytical systems. AI agents use their available tools to carry out actions, and actively monitor their actions. Finally, it Reflects by comparing predicted outcomes (such as stopping distance) with actual results, using this feedback to improve future decisions. Each loop through SPAR makes the car safer and more reliable. It gets better over time.

What makes these four capabilities so powerful in AI agents is that they work together. Rather than following rigid, predetermined instructions, they actually begin to write their own instructions based on their experiences. If the autonomous vehicle realized that braking took longer than expected, it might reason that it was because the road was wet or it was dry and was very dusty due to nearby construction.

What Really Is An Agent?

Pascal Bornet (in Agentic Artificial Intelligence, p.67) makes the observation that there’s no universal consensus on what truly defines an “agent.” That’s important, because “AI agent” is a buzzword right now, and different groups use it differently. This leads us into another whole topic called the Progression Framework. This framework of Pascal Bornet et. al is a structured way to asses and defines the advancing (changing) role of AI agents. So it’s not really possible to get everyone to agree on where the dividing line is between an agent and not an agent. This is why he called it a Progression Framework.