BDI Architecture for Efficient Orchestration of LLM-based Multi-Agent Systems

LLM-based multi-agent systems (MAS) are shifting from manually designed workflows toward automated orchestration, which often relies on repeated workflow search and optimization or additional training. We, human-beings, do not reconsider all possibilities whenever a course of action fails, but maintain an intention and revise the plan only when it is no longer appropriate for reasoning. Inspired by this phenomenon, we propose a BDI (Belief–Desire–Intention)-based orchestration framework that dynamically constructs and adapts MAS workflows at inference time without explicit workflow search or additional training. A single orchestrator maintains beliefs about agent states and execution outcomes, represents candidate sub-goals as desires, and commits to a selected sub-goal as an intention. It then constructs and adapts an execution plan based on the current system state and accumulated experience. When plan adaptation is insufficient to achieve the intended objective, the orchestrator abandons its current intention and selects a new sub-goal from its desires. This intention commitment mechanism reduces repeated deliberation while preserving adaptability to changing execution conditions. Experiments across agentic and general-domain reasoning benchmarks show that our framework improves GAIA accuracy by 4.24 percentage points and ALFWorld success rate by 19.40%p over the strongest baselines, while achieving the highest general-domain average of 80.00 with a GAIA execution cost of $0.0127 per task.