Vision: Agents That Grow With You

2026-05-25 · Yuyang Ding

This is a proposal, not a release announcement.

Below is the research direction we want Uni-Agent to enable, the two flagship agents we are framing the work around.

Project Milo and Miko

We frame the work around two flagship agents: one for the human side of work, one for the engineering side.

🧠 Project Milo: An chat agent that actually gets you. Reads intent and subtext, learns what matters to you over time, and on top of that helps you get work done across schedules, mail, and docs.

💻 Project Miko: An coding agent that actually gets the problem. Reads specs and codebases, reasons through real engineering challenges, and on top of that manages the whole project for excellent end-to-end performance.

Online Reinforcement Learning

The bigger bet behind both agents: once an agent lives next to a user and uses real tools, every conversation is a training signal. Turning that signal into a model that keeps improving is hard on both the infrastructure and algorithm side.

Infrastructure

• RL training as a service. Today’s RL stacks are built for one-shot research runs. RL in a product needs a long-lived pipeline that continuously ingests trajectories, schedules updates, and rotates fresh checkpoints back to serving.

• Agent gateway. A single endpoint that any OpenAI-compatible agent calls without modification, recording full token-level trajectories with consistent tokenization and low latency (verl RFC #5790, PR #25).

Algorithm

• Cleaning noisy user data. Real conversations contain PII, off-topic chatter, and heavy-user bias. Filtering pipelines must strip the junk without losing the high-signal trajectories that drive learning.

• Modeling user intent as reward. Explicit feedback is sparse. Implicit signals like edits and retention are noisy and easy to game into sycophancy. Designing a reward model that captures what users actually want is its own subproject.