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 the main difference between AI agents and traditional automation?

Traditional automation executes a fixed sequence of steps — any deviation breaks the workflow. AI agents reason about a goal and decide how to achieve it, handling unexpected inputs, exceptions, and multi-step decisions without requiring a human to re-script every edge case.

Can AI agents replace RPA (Robotic Process Automation)?

In many cases, yes. AI agents can handle the same structured tasks as RPA with far greater flexibility — and they also handle unstructured inputs like natural language, PDFs, and images that RPA struggles with. However, for ultra-high-volume, perfectly stable processes, RPA may still be cheaper to run. See our [guide to AI agent use cases](/blog/ai-agent-use-cases/) for specifics.

Is traditional automation cheaper than AI agents?

Traditional automation has lower per-execution costs for simple, stable tasks. But the total cost of ownership — including maintenance, exception handling, and re-scripting for every process change — often makes AI agents cheaper at scale. Agent costs are falling fast as LLM prices drop.

What types of automation tasks are best suited for AI agents?

AI agents shine on tasks involving judgment, unstructured data, or variable inputs: customer support escalations, code review, document analysis, complex scheduling, and any multi-step workflow that spans several tools or requires contextual decision-making.

Do I need to choose between AI agents and traditional automation?

No — most mature teams use both. Traditional automation handles high-volume, perfectly predictable pipelines (ETL, form processing). AI agents handle the messy, judgment-heavy work. Platforms like [cowork.ink](https://cowork.ink) let you orchestrate both in a unified workspace.

AI Agents vs. Traditional Automation: Key Differences

AI agents vs. traditional automation — REAL differences in flexibility, cost, and ROI. See which approach wins for your team and use case. Read now!

Frequently Asked Questions

What is the main difference between AI agents and traditional automation?: Traditional automation executes a fixed sequence of steps — any deviation breaks the workflow. AI agents reason about a goal and decide how to achieve it, handling unexpected inputs, exceptions, and multi-step decisions without requiring a human to re-script every edge case.
Can AI agents replace RPA (Robotic Process Automation)?: In many cases, yes. AI agents can handle the same structured tasks as RPA with far greater flexibility — and they also handle unstructured inputs like natural language, PDFs, and images that RPA struggles with. However, for ultra-high-volume, perfectly stable processes, RPA may still be cheaper to run. See our [guide to AI agent use cases](/blog/ai-agent-use-cases/) for specifics.
Is traditional automation cheaper than AI agents?: Traditional automation has lower per-execution costs for simple, stable tasks. But the total cost of ownership — including maintenance, exception handling, and re-scripting for every process change — often makes AI agents cheaper at scale. Agent costs are falling fast as LLM prices drop.
What types of automation tasks are best suited for AI agents?: AI agents shine on tasks involving judgment, unstructured data, or variable inputs: customer support escalations, code review, document analysis, complex scheduling, and any multi-step workflow that spans several tools or requires contextual decision-making.
Do I need to choose between AI agents and traditional automation?: No — most mature teams use both. Traditional automation handles high-volume, perfectly predictable pipelines (ETL, form processing). AI agents handle the messy, judgment-heavy work. Platforms like [cowork.ink](https://cowork.ink) let you orchestrate both in a unified workspace.