A conversational model has a simple job: given an input, produce a good output. The decision is one step, the feedback is immediate, and if the response is wrong the user just asks again. An autonomous agent has a harder job. It must decide what to do, do it, observe what happened, decide what to do next, and keep going until the goal is reached. Each step depends on the last. Errors compound. And there is no human in the loop to catch a wrong turn before it becomes a wrong mile.
This is why agent decision-making requires more structure than conversational AI. The question isn't just "what is a good response?" It is "given where I am, what I know, and what I'm trying to accomplish, what should I do next?"
One of the most durable frameworks for structuring this kind of decision loop is the OODA Loop: Observe, Orient, Decide, Act. Originally developed for military decision-making, it maps cleanly onto agent reasoning. An agent observes its current state, orients itself by interpreting what it has observed in the context of its goal, decides on an action, and acts. Then the loop repeats. The value of the framework is that it makes explicit what an agent needs to do at each step, which makes it easier to identify where a decision process is breaking down.
Behavior Trees provide a more structured approach to encoding decision logic. Rather than leaving the agent to reason freely about what to do next, a behavior tree defines a hierarchy of conditions and actions that the agent works through in a predictable order. This makes agent behavior more auditable and easier to debug, at the cost of some flexibility. For agents operating in high-stakes or safety-critical environments, that tradeoff is often worth making.
AI Heuristics are the shortcuts and rules of thumb that agents use when full deliberation is too slow or too expensive. A well-designed heuristic gets the agent to a good-enough decision quickly. A poorly designed one leads to systematic errors that are hard to trace because they look like reasonable behavior.
Reinforcement Learning (RL) is the training approach that teaches agents to make better decisions by rewarding good outcomes and penalizing bad ones. Rather than being told explicitly what to do in each situation, a reinforcement-trained agent learns a policy — a mapping from states to actions — through experience. This produces agents that can handle novel situations, but it also makes their decision logic harder to inspect and explain.
The articles in this section cover each of these frameworks in detail. Together they describe the decision infrastructure that separates an agent that pursues its goal reliably from one that goes off the rails when it encounters something unexpected.
