Show HN: Why single agents suck at math proofs (ensue.dev)

A recent demonstration by the Ensue team revealed the limitations of single AI agents when tackling complex mathematical proofs, such as those found in the Putnam competition. Despite advances in AI capabilities, a single agent struggled to produce a valid proof for a challenging problem. In contrast, a multi-agent system with shared memory successfully generated a machine-verified Lean proof. This underscores the importance of collaborative approaches and the role of memory-sharing in overcoming the bottlenecks faced by individual models, especially in tasks that require intricate, step-by-step reasoning. The multi-agent framework involved distinct roles—Orchestrator, Decomposer, Prover agents, and a Composer agent—all working with a unified memory network through Ensue. This architecture allowed agents to react autonomously to changes, share discoveries, and avoid inefficiencies of conventional systems. The success of this approach highlights how multi-agent collaboration, aided by semantic memory access and reactive coordination, can enhance the problem-solving abilities of AI systems. With the potential to revolutionize complex task execution, the research advocates for moving away from solitary agents to collaborative agent swarms, setting a new paradigm in AI problem-solving methodologies.