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 context engineering for AI agents?

Context engineering is the discipline of designing and managing all the information that enters an AI agent's context window — system prompts, conversation history, retrieved documents, tool definitions, memory, and structured state. Unlike prompt engineering (which focuses on phrasing instructions), context engineering is an architectural practice that determines what the agent knows at each step of its reasoning loop.

How is context engineering different from prompt engineering?

Prompt engineering is tactical and static — you craft instructions once at deployment. Context engineering is architectural and dynamic — you design what information flows into the model at runtime. As one practitioner summarized: "Prompt engineering is copywriting, context engineering is systems architecture."

Why do most AI agents fail?

Most AI agent failures are not model failures — they are context failures. Research from Dextra Labs found that 93% of agent failures were eliminated by transitioning to context engineering practices. The four failure modes are context poisoning, context distraction, context confusion, and context clash. See our [guide to AI agent architecture](/blog/ai-agent-architecture/) for the full breakdown.

What is the "lost in the middle" problem in AI agents?

Studies show that LLMs reliably attend to information at the beginning and end of a context window, but struggle with content buried in the middle. This means blindly filling an agent's context window actually degrades performance — the model misses critical details positioned in the center of a long context. Context engineering solves this by structuring and prioritizing what goes where.

What tools support context engineering for AI agents?

Key tools include LangGraph (stateful agent workflows with checkpointing), Zep (dynamic context assembly with <200ms retrieval), the Claude Agent SDK (built-in compaction and MCP tool management), and LlamaIndex (RAG pipelines for just-in-time retrieval). Platforms like [cowork.ink](https://cowork.ink) orchestrate agents with structured context pipelines out of the box.

Context Engineering for AI Agents: Beyond Prompt Engineering

Context engineering is replacing prompt engineering for AI agents. Learn the 7 layers, 4 failure modes & key techniques. COMPLETE guide.

Frequently Asked Questions

What is context engineering for AI agents?: Context engineering is the discipline of designing and managing all the information that enters an AI agent's context window — system prompts, conversation history, retrieved documents, tool definitions, memory, and structured state. Unlike prompt engineering (which focuses on phrasing instructions), context engineering is an architectural practice that determines what the agent knows at each step of its reasoning loop.
How is context engineering different from prompt engineering?: Prompt engineering is tactical and static — you craft instructions once at deployment. Context engineering is architectural and dynamic — you design what information flows into the model at runtime. As one practitioner summarized: "Prompt engineering is copywriting, context engineering is systems architecture."
Why do most AI agents fail?: Most AI agent failures are not model failures — they are context failures. Research from Dextra Labs found that 93% of agent failures were eliminated by transitioning to context engineering practices. The four failure modes are context poisoning, context distraction, context confusion, and context clash. See our [guide to AI agent architecture](/blog/ai-agent-architecture/) for the full breakdown.
What is the "lost in the middle" problem in AI agents?: Studies show that LLMs reliably attend to information at the beginning and end of a context window, but struggle with content buried in the middle. This means blindly filling an agent's context window actually degrades performance — the model misses critical details positioned in the center of a long context. Context engineering solves this by structuring and prioritizing what goes where.
What tools support context engineering for AI agents?: Key tools include LangGraph (stateful agent workflows with checkpointing), Zep (dynamic context assembly with <200ms retrieval), the Claude Agent SDK (built-in compaction and MCP tool management), and LlamaIndex (RAG pipelines for just-in-time retrieval). Platforms like [cowork.ink](https://cowork.ink) orchestrate agents with structured context pipelines out of the box.