What goes in
Inputs include a task, starting state, allowed actions, and observable information. Real workflows and edge cases define important decisions; success criteria and constraints make attempts scorable.
In development
Agent environments
Agent environments are controlled settings where an AI agent attempts a task, acts through defined tools, observes results, and receives a score.
Discuss a projectFrom source to use
How it works.
- Set the task
Task and starting state
- Run actions
Agent actions and tools
- Evaluate & reset
Outcomes and scoring
How an environment works
The agent uses controlled tools and receives the resulting state. Scoring compares the outcome and relevant process signals with the task criteria, then the environment resets.
What it is for
An environment can test outcomes, tool use, and responses to changing state across repeatable attempts. It can expose failure modes that static examples miss. This offering is in development.
Useful distinctions
Questions about agent environments.
How is an agent environment different from a dataset?
A dataset presents fixed examples. An environment lets an agent act, observe changes, and continue, covering a sequence of decisions and its final outcome.
What can an environment measure?
Measures can include task completion, constraint violations, tool use, intermediate decisions, and final state. Scoring depends on what success means for the task.
Are agent environments available now?
This offering is in development. A project conversation can identify useful tasks and evaluation criteria, but no standard environment catalog is available here.
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