Personal agents are the ultimate manifestation of “build something that agents want”.
The form factor of a product like Muse is you want to be able to hand off a task to the agent and ensure that it is fully completed end to end. To do this, the agent must be able to
zuck essentially launched a new app store for agents today and Meta’s perfectly placed to crush it, you’re looking at a multi-trillion dollar opp if they pull it off:
- instead of apps, agents get equipped with connectors i.e. plugins to any app, service or software tool.
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Jev will be super helpful for agents to make split second decisions in workflows, data classification, judgment calls, and hundreds of other use-cases in the enterprise.
Here's a quick demo with Box and Jev to make that real. The demo pulls an incident report from Box, asks
Agents already make up the majority of inference. This will quickly trend toward nearly all inference over the next year or two.
The vast majority of tokens used in the world will be agents that are executing unbelievable amounts of tasks for us in the background 24/7.
Agents
AGENTIC TRAFFIC NOW MAKES UP MORE THAN 70% OF ALL INFERENCE TRAFFIC 🚀
Agentic workloads are characterized by four elements:
🟠 Multi-turn: a session includes tens or hundreds of turns, leading to high potential KV-cache reuse.
🟠 Long context: system prompts, tool
Incredibly exciting that there are entire universes of AI innovation that still exist that weren’t even on most of our radars.
Being able to process information insanely quickly, at crazy low costs, with high levels of capability is huge for a wide number of enterprise tasks.
After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI?
I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev
• 20-200x faster
• 40-400x
