TANGO: TASK-ADAPTIVE AGENT ORCHESTRATION ACROSS THE MULTI-AGENT LIFECYCLE

Multi-agent systems can improve complex reasoning in LLMs, but many frameworks predetermine one or more stages of the orchestration lifecycle: worker construction, communication, or final aggregation. We present TANGO, a taskadaptive framework that manages this orchestration lifecycle from creation to coordination and conclusion. For each task, the meta-agent creates workers with complementary reasoning strategies and tool configurations. Each worker maintains a structured memory state that records its progress, hypotheses, unresolved questions, and supporting evidence. At periodic checkpoints, the meta-agent coordinates the workers by constructing a sparse directed communication topology and selecting the information to route. It then concludes the process by reasoning over terminal answers and structured memories to synthesize the final answer. We evaluate TANGO with Gemini-2.5-Flash across general agentic, scientific, mathematical, and code-generation tasks using GAIA, GPQA-Diamond, OmniMATH, and HumanEval. We report mean accuracy across three runs, Pass@3, and a four-benchmark macro-average for both metrics. TANGO attains the highest observed macro-averages in our comparison. Our method achieves the highest scores on GAIA, Omni-MATH, and GPQA-Diamond, while being competitive on HumanEval. We further investigate how meta-agent control across the multi-agent lifecycle affects performance, how these components interact, and how structured memory enables the meta-agent to orchestrate workers more effectively. The code is available at https://anonymous.4open.science/r/TANGO-27C/README.md.