Multi-agent systems enhance complex reasoning but face a critical cost and effectiveness dilemma: API costs grow exponentially while performance degrades due to context degradation and error propagation. This paper proposes an evaluator-guided clause-level pruning and early halting method for lower-cost multi-agent systems named HALT, which implements a macro-to-micro intervention. At the macro-level, a difficulty aware router classifies query complexity, bypassing redundant multi-agent systems overhead for simple tasks. At the micro-level, a lightweight hindsight evaluator monitors generation in real time for complex reasoning, driving a dual-axis intervention policy: spatial pruning removes erroneous clauses, while temporal halting terminates multi-agent interaction early upon reaching a purified consensus. The extensive experiments across three different datasets demonstrate the outperformance of HALT over the baselines, achieving the 97.98%, 90.82%, 87.14% in GSM8K, MMLU, MMLU-Pro in accuracy while drastically mitigating computational overhead, with a maximum reduction of 94.3% in prompt tokens and an unprecedented reduction of 98.9% in completion tokens, which validate the effectiveness of HALT. Code is available at URL.