Agent Architecture and Context Management - AI industry research indicates that excessive knowledge and memory in language models create attention competition with primary tasks [1] - Standard ReAct loops continuously accumulate prompt histories, retaining failed steps and creating interference with original objectives [1][2] - Plan-and-Act frameworks address long-running agent context management challenges that exceed prompt-tuning capabilities by decoupling planning from execution [3] Execution and Performance Metrics - On WebArena-Lite benchmarks, a standard ReAct-style executor without a planner achieved a success rate of 36.97% [6] - Naively finetuned planners degraded performance, dropping the executor success rate to 20.60% due to mismatched site instructions [6] - Properly trained planners improved task execution success rates to 43.63% [7] - Continuous replanning after each action successfully replaced failed attempts in the plan, further increasing the benchmark score to 53.94% [8]