Act to Reason, Reason to Act
The Principle
Reasoning and acting are mutually reinforcing, not sequential stages. Actions generate information that improves reasoning. Reasoning generates plans that coordinate action.
Why This Matters
The default mental model treats reasoning as something that happens before acting, you think, then you do. But this misses how expertise actually works. Experts reason while acting, adjusting based on feedback. And they act to reason, gathering information that would be inaccessible through pure contemplation.
For AI systems, this matters because:
- Pure reasoning without action produces hallucination (56% of chain-of-thought failures)
- Pure action without reasoning produces incoherent behavior (failing to track goals, handle exceptions, or coordinate multi-step plans)
How It Works
Reason to Act:
- Decompose high-level goals into executable subgoals
- Track progress and determine when subgoals are complete
- Handle exceptions and adjust plans to circumstances
- Decide when external information is needed
Act to Reason:
- Retrieve information that grounds claims in external reality
- Observe consequences that update beliefs about environment state
- Gather evidence that resolves uncertainty
- Test hypotheses through probing actions
The synergy: reasoning traces help the agent decide what to do, while action results help the agent know what’s true.
When to Apply
- Agentic AI systems that interact with external environments
- Knowledge work requiring both synthesis and verification
- Decision-making under uncertainty where information gathering is possible
- Any task where pure contemplation is insufficient and pure trial-and-error is inefficient
Exceptions
- Tasks where all relevant information is already in context
- Real-time decisions where action-observation loops are too slow
- Domains where actions are irreversible and costly to probe
Related: 05-molecule—thought-action-observation-pattern, 06-molecule—knowledge-spiral