# Decision Points in AI Agent Development # Plan-then-Execute vs ReAct 📝 🎯 The Hook "How far ahead do you look before you move?" This is an inescapable fork in agent design. Do you build a plan first, then execute it? Or do you alternate between observation and action one step at a time? Whether a plan exists as an explicit artifact -- reviewable, approvable, debuggable -- changes everything about approval flows, cost control, and debugging. LLM calls are expensive, so planless trial-and-error wastes tokens. But in dynamic environments, even a carefully crafted plan goes stale mid-execution. 📋 Overview Plan-then-Execute generates a structured execution plan (JSON/YAML) before any action is taken. The plan includes step ordering, tools and arguments for each step, and expected results. After the plan is finalized (and optionally approved by a human), steps are executed sequentially. ReAct has the LLM repeat an observe-reason-act cycle one step at a time. There is no explicit plan -- the "plan" is implicit in the LLM's internal reasoning at each step and is not externally accessible. 🔍 Decision Points **Task variability** is the primary axis. 📊 **By environment dynamism**: - Static (data and external state rarely change during execution) → Plan-then-Execute - Semi-static (mostly stable but some steps encounter state changes) → Plan-then-Execute + re-planning on deviation - Dynamic (each step's result heavily influences the next choice) → ReAct 📊 **By task nature**: - Procedures are routine and humans want to approve in advance → Plan-then-Execute - Procedures are roughly known but details are determined at runtime → Plan-then-Execute (coarse plan + runtime adjustment) - Procedures are unknown and exploration is needed → ReAct When **failure cost** is high (irreversible operations involved), there is a strong motivation toward Plan-then-Execute. 💡 Key Details 🟢 **Plan-then-Execute excels in transparency and control.** The plan is an explicit intermediate artifact, so humans can verify "is this procedure safe?" before execution. Plans containing irreversible operations can be reviewed in advance, securing safety. Debugging separates "was the plan wrong?" from "was the execution wrong?" Planning also prunes unnecessary steps, reducing token consumption. The weakness is plan staleness -- environmental changes can trigger a meta-loop of plan-fail-replan, so re-planning attempts should be capped at 2-3. 🟡 **ReAct excels in adaptability.** It responds to environmental changes at the granularity of individual steps and handles unexpected situations (API errors, unexpected data formats, additional user information) flexibly. It suits environments too dynamic for upfront planning and tasks requiring exploratory progress. Implementation is simpler too -- no infrastructure for plan generation, storage, validation, or re-planning. The weakness is unpredictability and cost: the number of steps is unknown in advance, and self-loop risk is real. ⚖️ Trade-offs | Dimension | Plan-then-Execute | ReAct | |---|---|---| | Transparency | Plan is an explicit artifact 🟢 | Implicit (LLM internal reasoning) 🔴 | | Human approval | Approve entire plan upfront | Must intervene per step | | Cost control | Upper bound estimable from step count | Unknown in advance | | Adaptation to change | Re-planning needed (added cost) | Per-step adaptation 🟢 | | Debugging | Separate plan vs. execution investigation | Must analyze all step logs | | Implementation complexity | Plan infrastructure required | Loop control only | 🛠️ Use Cases 🔵 **Plan-then-Execute fits**: Processes with irreversible operations, workflows requiring human approval, cost-sensitive processing, routine procedural tasks. 🔴 **ReAct fits**: Debugging and investigation, processing in dynamically changing environments, tasks with unknown procedures requiring exploration, prototype development. 📌 **Default strategy**: Default to Plan-then-Execute, and make plans human-editable. The most practical compromise is "plan first, re-plan only on deviation." Build the plan, execute steps, and trigger re-planning only when execution results diverge significantly from expectations. Re-planning includes failure context to avoid repeating the same mistakes. Cap re-planning attempts at 2-3. #AIAgents# #SoftwareArchitecture#