An agent is only as capable as the environment it acts in.
Today’s agents work across filesystems, docker containers, customer repositories, databases, services, and tools. While a trace gives a full agent lifecycle, testing a request requires more: the agent needs to run the task again with the code, data, tools, and services it had the first time.
Today we’re introducing NeoSigma Environments: a playground for agents to replay, explore, and learn from experience.
To work autonomously, agents need a way to verify their own work. Tools and reward signals become useful when an agent can act in a real environment, observe the results, and try again.
NeoEval turns failed production sessions into replayable tasks, graded with your own verifiers. NeoSigma Environments gives those tasks a place to run. Together, they provide a test and training bed for evaluating and optimizing agents against the outcomes that matter to your business.
An agent is only as capable as the environment it acts in.
Today’s agents work across filesystems, docker containers, customer repositories, databases, services, and tools. While a trace gives a full agent lifecycle, testing a request requires more: the agent needs to run the task again with the code, data, tools, and services it had the first time.
Today we’re introducing NeoSigma Environments: a playground for agents to replay, explore, and learn from experience.
To work autonomously, agents need a way to verify their own work. Tools and reward signals become useful when an agent can act in a real environment, observe the results, and try again.
NeoEval turns failed production sessions into replayable tasks, graded with your own verifiers. NeoSigma Environments gives those tasks a place to run. Together, they provide a test and training bed for evaluating and optimizing agents against the outcomes that matter to your business.
https://neosigma.ai/blog/agent-environments