What AI models does the agent use?

The agent has access to Claude, DeepSeek, Gemini, and other top models. It automatically routes to the best model for each task — you never have to think about API keys or token limits.

Yes. Each agent runs in an isolated environment. We don't train on your data, and you can export or delete everything at any time. The underlying engine (GoGogot) is open-source, so you can audit exactly how it works.

What can the agent actually do?

Browse the web, process files (CSV, PDF, images), run scheduled tasks, remember context across sessions, and communicate via Telegram or Slack. Think of it as a capable junior employee, not a chatbot.

How does memory work?

The agent maintains persistent memory across sessions. It remembers your preferences, past conversations, and task context. You can also explicitly tell it to remember or forget things.

Can I self-host the agent?

Absolutely. GoGogot is 100% open-source. Run it on your own hardware for free. cowork.ink is the managed version — we handle servers, model costs, updates, and uptime so you don't have to.

Is there a free trial?

We offer a 7-day money-back guarantee. Spin up an agent, give it real tasks, and if it doesn't save you time, we'll refund you — no questions asked.

What is AI agent documentation?

AI agent documentation has two meanings: using AI agents to automatically generate documentation from your codebase, and writing documentation about how an AI agent itself behaves — its decision logic, failure modes, and boundaries. Both matter for teams shipping production agents.

How do AI agents automatically generate documentation from code?

AI agents analyze your source files, commit messages, and tests using an LLM, then produce structured documentation (READMEs, API references, inline docstrings). They're triggered on PR merge or a scheduled CI job. Multi-agent systems that separate reading, writing, and verification steps produce significantly more accurate output than single-pass generation.

Can AI agents keep documentation up to date as code changes?

Yes. When connected to a CI/CD trigger (GitHub Actions, GitLab CI, etc.), a documentation agent runs on every code change and opens a pull request with updated docs. A human reviews and merges the PR — that review gate keeps quality high without slowing down the team.

What is AGENTS.md and do I need one?

AGENTS.md is an emerging standard (popularized by OpenAI and backed by the Linux Foundation) for documenting how an AI agent behaves — its purpose, capabilities, failure modes, and scope limits. Any team deploying agents to production should maintain one alongside their code. See our guide to [AI agent observability](/blog/ai-agent-observability/) for related best practices.

What are the limitations of AI-generated documentation?

AI-generated docs can miss complex business logic, misread ambiguous function names, and produce plausible-sounding but inaccurate descriptions for poorly typed code. They work best on well-structured, typed codebases. Always include a human-in-the-loop PR review step before merging generated docs.

Auto-Generate Documentation with AI Agents: A Practical Guide

Learn to AUTO-GENERATE documentation using AI agents — step-by-step CI/CD pipeline, AGENTS.md standard, and tool comparison. Get your docs writing themselves TODAY.

Frequently Asked Questions

What is AI agent documentation?: AI agent documentation has two meanings: using AI agents to automatically generate documentation from your codebase, and writing documentation about how an AI agent itself behaves — its decision logic, failure modes, and boundaries. Both matter for teams shipping production agents.
How do AI agents automatically generate documentation from code?: AI agents analyze your source files, commit messages, and tests using an LLM, then produce structured documentation (READMEs, API references, inline docstrings). They're triggered on PR merge or a scheduled CI job. Multi-agent systems that separate reading, writing, and verification steps produce significantly more accurate output than single-pass generation.
Can AI agents keep documentation up to date as code changes?: Yes. When connected to a CI/CD trigger (GitHub Actions, GitLab CI, etc.), a documentation agent runs on every code change and opens a pull request with updated docs. A human reviews and merges the PR — that review gate keeps quality high without slowing down the team.
What is AGENTS.md and do I need one?: AGENTS.md is an emerging standard (popularized by OpenAI and backed by the Linux Foundation) for documenting how an AI agent behaves — its purpose, capabilities, failure modes, and scope limits. Any team deploying agents to production should maintain one alongside their code. See our guide to [AI agent observability](/blog/ai-agent-observability/) for related best practices.
What are the limitations of AI-generated documentation?: AI-generated docs can miss complex business logic, misread ambiguous function names, and produce plausible-sounding but inaccurate descriptions for poorly typed code. They work best on well-structured, typed codebases. Always include a human-in-the-loop PR review step before merging generated docs.