Most AI agents are still judged by the answer they return.

Apodex 1.1 from @Apodex_AI is training for something harder: whether the model can take over the entire job, keep a real workspace alive across 100s of steps, recover when parts fail, and deliver artifacts that can actually be verified.

Apodex 1.1 is built around a different unit of AI capability: completed, checkable work, rather than a polished answer.

A task can start with raw files, datasets, spreadsheets, papers, images, or code. The system can inspect them, choose a method, run code, keep task state, recover from failures, and produce outputs that can be checked.

Underneath that are 2 scaling paths. Environment Scaling expands the executable file, search, and code situations the model learns from. Agentic Coordination Scaling trains it to split long tasks across agents, bring partial results back into shared state, and revise the plan when evidence changes.

AgentOS keeps files, tool state, artifacts, dependencies, and coordination state persistent across that work. Subagents can return useful results before every branch finishes, so the main agent can redirect unfinished work without discarding progress that is still valid.

Officially published results place Apodex 1.1 with Agent Team in the leading performance band across professional work, finance, science, reasoning, and search.

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