> ## Documentation Index > Fetch the complete documentation index at: https://labs.prompthon.io/llms.txt > Use this file to discover all available pages before exploring further. # Reasoning And Control Patterns

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## Summary Reasoning and control patterns define how an agent alternates between thinking, acting, and stopping. They are less about model intelligence than about how the system structures decisions over time. ## Why It Matters Two agents with access to the same model and tools can behave very differently depending on control pattern. One may search effectively, another may loop, hallucinate, or call the wrong tool at the wrong time. Pattern choice therefore shapes: * action quality * explainability * cost and latency * recovery behavior ## Mental Model The imported reference material uses ReAct as the clearest baseline. Its core idea is simple: * think about the current state * take one action * observe the result * repeat That design is powerful because reasoning and action correct one another. It is especially useful when the system needs outside information or tool execution before it can continue. The broader lesson is that control patterns define where reasoning happens: * before action * between actions * after failure * or at explicit stopping points ## Architecture Diagram ```mermaid theme={null} flowchart LR Task["Task"] --> Thought["Thought"] Thought --> Action["Action"] Action --> Observation["Observation"] Observation --> Thought Thought --> Final["Final answer or stop"] ``` ## Tool Landscape Common reasoning and control patterns include: * stepwise think-act-observe loops for open-ended tool use * guarded tool selection where actions are constrained by narrow interfaces * explicit stop or handoff rules that prevent endless loops * traceable reasoning surfaces that expose enough intermediate state to debug decisions without forcing every token into the final answer The important design choice is not whether to show chain-of-thought. It is whether the system has enough internal control structure to keep actions purposeful and recover when evidence changes. ## Tradeoffs * Stepwise loops are adaptable, but they are slower than direct execution and can drift without strong stopping conditions. * Highly interpretable control surfaces make debugging easier, but they can feel verbose and expensive. * Narrow tool surfaces reduce mistakes, but they can also limit flexibility. * Rich intermediate reasoning can improve decisions, but only if the system can keep that reasoning aligned with the actual task. Useful defaults: * prefer stepwise control when tool feedback changes the next best action * add explicit stop conditions before adding more tool breadth * keep the control loop inspectable enough to debug, even if the final product hides most of that internal machinery ## Reading Extensions * [Planning And Reflection](/patterns/planning-and-reflection) * [Protocols And Interoperability](/systems/protocols-and-interoperability) * [Patterns Overview](/patterns) ## Update Log * 2026-04-21: Initial repo-native draft based on imported reference material and lab rewrite rules.