Sakana AI launches Sakana Fugu beta
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Sakana AI launched Sakana Fugu beta, a commercial multi-agent orchestration system. Fugu dynamically coordinates frontier models via OpenAI-compatible API, assigning and optimizing agent roles per task. It achieves state-of-the-art performance on SWE-Pro, GPQA-D, and ALE-Bench benchmarks. The platform is available now for commercial beta use.
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- QUOTESA#58@SAKANAAILABS@SAKANAAILABSIntroducing our new work: “Learning to Orchestrate Agents in Natural Language with the Conductor” accepted at #ICLR2026 https://arxiv.org/abs/2512.04388 What if we trained an AI not to solve problems directly, but to act as a manager that delegates tasks to a diverse team of other AIs? To solve complex tasks, humans rarely work alone; we form teams, delegate, and communicate. Yet, multi-agent AI systems currently rely heavily on rigid, human-designed workflows or simple routers that just pick a single model. We wanted an AI that could dynamically build its own team. We trained a 7B Conductor model using Reinforcement Learning to orchestrate a pool of frontier models (including GPT-5, Gemini, Claude, and open-source models available during the period leading up to ICLR 2026). Instead of executing code, the Conductor outputs a collaborative workflow in natural language. For any given question, the Conductor specifies: 1/ Which agent to call 2/ What specific subtask to give them (acting as an expert prompt engineer) 3/ What previous messages they can see in their context window Through pure end-to-end reward maximization, amazing behaviors emerged. The Conductor learned to adapt to task difficulty: it 1-shots simple factual questions, but autonomously spins up complex planner-executor-verifier pipelines for hard coding problems. The results are very promising: The 7B Conductor surpasses the performance of every individual worker model in its pool, setting new records on LiveCodeBench (83.9%) and GPQA-Diamond (87.5%) at the time of publication. It also significantly outperforms expensive multi-agent baselines like Mixture-of-Agents at a fraction of the cost. One of our favorite features: Recursive Test-Time Scaling! By allowing the Conductor to select itself as a worker, it reads its own team's prior output, realizes if it failed, and spins up a corrective workflow on the fly. This opens a new axis for scaling compute during inference. This research proves that language models can become elite meta-prompt engineers, dynamically harnessing collective intelligence. Alongside our TRINITY research which we announced a few days earlier, this foundational research powers our new multi-agent system: Sakana Fugu! (https://sakana.ai/fugu-beta) 🐡 OpenReview: https://openreview.net/forum?id=U23A2BUKYt (ICLR 2026)
- REPOSTHA#19@HARDMARU@SAKANAAILABSWe’re launching the beta for our new commercial AI product: Sakana Fugu 🐡, a multi-agent orchestration system! Blog: https://sakana.ai/fugu-beta Fugu hits SOTA on SWE-Pro, GPQA-D, and ALE-Bench, and has been our internal secret weapon. It dynamically coordinates frontier models, autonomously selecting the optimal agent combinations and roles for each task. Available as an OpenAI-compatible API, you can seamlessly integrate Fugu into your existing workflows with minimal changes. 🐟 Fugu Mini: High-speed orchestration optimized for latency 🐡 Fugu Ultra: Full model pool utilization for deep, complex reasoning Apply for the beta test here: https://forms.gle/BtKkhc2CfLKk1dvNA
