The Next Breakthrough In AI Agents Is Here

Manus uses a planner to break a prompt into subtasks, then hands them to specialized agents that share context and use a suite of 29 tools. An executor combines their results into a final response. The transcript says Manus uses Claude 3.7 Sonnet, Browser Use, and E2B’s cloud sandbox.

The platform is presented as useful for tasks including travel planning, financial analysis, database compilation, supplier sourcing, and presentations. On the GAIA benchmark, Manus scored 86.5%; the transcript compares that with an average human score of about 92% and Deep Research’s best score of about 74%.

The discussion challenges the dismissal of Manus as a wrapper around existing models and tools. It points to Cursor, Windsurf, and legal AI product Harvey as examples of applications that combine foundation models with specialized tooling. Co-founder Yichao “Peak” Gu says the team chose to work alongside model development and welcome new model releases.

The host credits Manus’s multi-agent design with a stated cost of about $2 per task, along with transparency that lets users inspect or change agents and tools. The system’s coordination may become harder as tasks grow, and its UX, tuning, and integrations could be matched by competitors. API pricing or provider policy changes could also undermine cost advantages, so founders should consider proprietary evaluations, embedded workflows, or hard-to-access integrations.