Priority-based decisions

As agents grow beyond trivial behavior, we often find that more than one rule could apply at the same time. In deterministic systems, this is not an edge case—it is the norm. This lesson exists to show how we keep decisions predictable when multiple conditions are true, which is essential when building agents that must behave the same way every time they run.

When multiple rules apply

In a realistic agent, state is rarely simple. An agent may be missing data, have pending work, and also be in a failure-recovery mode—all at once.

If we write independent if statements for each situation, more than one action may trigger. That usually isn’t what we want. Instead, we want the agent to choose one action based on the overall situation.

This means we must expect overlapping conditions and design for them explicitly, rather than assuming that only one rule will ever match.

Ordering rules by priority

The simplest way to resolve overlapping rules is to give them an explicit order. Higher-priority rules are checked first, and the first match wins.

In practice, this often looks like an ordered list of rules evaluated from top to bottom:

rules = [
    ("missing_data", lambda state: not state["has_data"]),
    ("retry_failed_task", lambda state: state["last_task_failed"]),
    ("generate_page", lambda state: state["ready_to_generate"]),
]

We then select the first rule whose condition is true:

for name, condition in rules:
    if condition(state):
        selected_action = name
        break

Once a rule matches, the decision is made. No later rules are considered.

This approach makes priority visible and intentional. Anyone reading the code can see which situations take precedence.

Predictable handling of overlapping conditions

Some rules are mutually exclusive by nature. Others overlap by design. Priority ordering lets us handle both cases without ambiguity.

The key idea is not to eliminate overlap, but to control it. By selecting the first matching rule, the agent’s behavior becomes reproducible and easy to reason about.

Given the same state, the same rule will always be selected. This predictability is what makes deterministic agents debuggable, testable, and safe to extend.

Conclusion

We now know how to handle situations where multiple rules apply at once. By ordering rules explicitly and selecting the first match, we keep agent decisions deterministic and understandable. This completes our orientation to priority-based decision logic, which forms the backbone of reliable classical agents.