# How We Build Agent Environments & Tasks

DevFeed: [How We Build Agent Environments & Tasks](<https://devfeed.tech/articles/how-we-build-agent-environments-tasks-78287.md>)

Original publisher: [Read original article](<https://www.langchain.com/blog/building-agent-environments-and-tasks>)

Author: LangChain Accounts

Published: 2026-08-25T19:38:39Z

Content type: tutorial

Language: en

Sources: [LangChain Blog](<https://devfeed.tech/sources/langchain-blog.md>)

Topics: [AI Agent](<https://devfeed.tech/topics/ai-agent.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Memory Safety](<https://devfeed.tech/topics/memory-safety.md>)

Tags: [agent](<https://devfeed.tech/tags/agent.md>), [better](<https://devfeed.tech/tags/better.md>), [code-review](<https://devfeed.tech/tags/code-review.md>), [dataset](<https://devfeed.tech/tags/dataset.md>), [knowledge](<https://devfeed.tech/tags/knowledge.md>), [synthetic](<https://devfeed.tech/tags/synthetic.md>)

## AI overview

LangChain describes a two-stage process for creating synthetic agent evaluation tasks: first write a task specification from traces, code, or human input, then use it to generate an environment and task. A shared world specification stores reusable knowledge, scripts, and definitions. The process uses coding agents to help build and refine tasks, while human review remains important for representing real-world needs and calibrating difficulty.

## Source excerpt

How we create synthetic agent environments and tasks: a spec generation step, a spec-to-task step, and a world spec that holds shared knowledge.