MiniMax M2.7 drops self-evolving agent stack

MiniMax is betting that the next jump in agent quality comes from training the model to shape its own harness, not from piling on longer prompts and more orchestration glue.

• –The standout claim is 100+ autonomous scaffold-optimization rounds, which is more interesting than another benchmark flex if the gains survive outside MiniMax's own setup.
• –For developers, the promise is less prompt bloat, fewer brittle system instructions, and better tool adherence across long workflows.
• –The skeptical read: internal eval wins and curated agent loops do not automatically translate to stable real-world behavior once environments, tools, and repos get messy.
• –If the model and its tooling really do come to open ecosystems, it could shift agent stacks toward smaller, more specialized workflows instead of VRAM-hungry wrapper monsters.