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 a context window in AI?

A context window is the maximum amount of text (measured in tokens) an AI model can read and process in a single interaction. It acts as the model's short-term working memory — everything the agent currently "knows" must fit inside this window.

What happens when an AI agent's context window fills up?

When the context window is full, older content is dropped or summarized to make room for new input. The agent may forget earlier instructions, prior tool results, or even the original task — leading to degraded or incorrect outputs. This is why [context engineering](/blog/context-engineering-ai-agents/) matters for long-running agents.

What is the "lost in the middle" problem?

Research shows that LLMs recall content at the beginning and end of a context window accurately (~85–95%), but content buried in the middle drops to ~76–82% recall accuracy. For agents with long tool call chains, critical intermediate results can effectively "disappear" even when they technically fit in the window.

Is a larger context window always better for AI agents?

Not necessarily. Larger context windows increase inference cost (attention complexity scales quadratically with sequence length), add latency, and can suffer from "context rot" — degraded attention quality as the window fills. Smart context management often beats brute-force large windows.

What is the difference between a context window and training data?

Training data is the vast dataset the model learned from during training — it becomes encoded in model weights and cannot be changed at runtime. The context window is the live, per-session window of text provided at inference time. Think of training data as long-term memory and the context window as working memory.

AI Agent Context Window: How It Limits and Empowers Agents

UNDERSTAND the AI agent context window — token limits, the "lost in the middle" problem, context rot, and PROVEN strategies to manage it. Learn more.

Frequently Asked Questions

What is a context window in AI?: A context window is the maximum amount of text (measured in tokens) an AI model can read and process in a single interaction. It acts as the model's short-term working memory — everything the agent currently "knows" must fit inside this window.
What happens when an AI agent's context window fills up?: When the context window is full, older content is dropped or summarized to make room for new input. The agent may forget earlier instructions, prior tool results, or even the original task — leading to degraded or incorrect outputs. This is why [context engineering](/blog/context-engineering-ai-agents/) matters for long-running agents.
What is the "lost in the middle" problem?: Research shows that LLMs recall content at the beginning and end of a context window accurately (~85–95%), but content buried in the middle drops to ~76–82% recall accuracy. For agents with long tool call chains, critical intermediate results can effectively "disappear" even when they technically fit in the window.
Is a larger context window always better for AI agents?: Not necessarily. Larger context windows increase inference cost (attention complexity scales quadratically with sequence length), add latency, and can suffer from "context rot" — degraded attention quality as the window fills. Smart context management often beats brute-force large windows.
What is the difference between a context window and training data?: Training data is the vast dataset the model learned from during training — it becomes encoded in model weights and cannot be changed at runtime. The context window is the live, per-session window of text provided at inference time. Think of training data as long-term memory and the context window as working memory.