Harvey introduced Harvey Tenet, its first post-trained Kimi K3 checkpoint for long-horizon legal agent work, based on asynchronous reinforcement learning in sandboxed legal environments. Tenet was trained using roughly 150 NVIDIA B300 GPUs over two months and improves held-out performance on Harvey’s Legal Agent Benchmark by almost twice as many tasks and 20% more for LAB: Contracts. It is being offered only as a research preview announced August 20, 2026 (recipe but no weights/API), with enterprise access via Harvey’s platform and plans to move the method into production over time.
Vercel launched Is Agentic, a free tool that scores how easily AI agents can discover, access, understand, and use a public website. It uses Ora’s 118 checklist checks split into discovery, access, usability, and payments scoring tiers. Access to the scoring is now available via a website, read-only API, CLI, and MCP server with no API key and no billing.
Four-agent decision tests run by Anthropic mostly failed when shared evidence pushed the group toward the wrong choice. Most model families got it right in only 17-36% of runs, while a single agent with the full evidence base was correct nearly every time. The takeaway is that agent “groupthink” persists, so better outcomes likely require fewer-but-informed agents and/or stronger mechanisms for dissent and diversity, though a related token price-elasticity finding suggests pricing “useful work” is still the bottleneck.
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