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 difference between AI agents and marketing automation?

Traditional marketing automation follows fixed, rules-based workflows — "if X, then Y." AI agents are goal-driven: they perceive data, reason about the best action, and execute autonomously, even in situations they were never explicitly programmed for. An automation tool sends an email at a scheduled time; an AI agent decides *which* email to send, to *which* segment, and *when* based on real-time behavioral signals.

What can AI agents actually do for a marketing team?

AI agents can handle content briefs, draft and optimize copy, conduct SEO research, manage ad bidding, qualify leads, personalize email sequences, monitor brand sentiment, and generate performance reports — without human intervention on each task. They work best on repetitive, data-heavy tasks that benefit from continuous optimization. See our [guide to AI agent use cases](/blog/ai-agent-use-cases/) for a broader view.

How do I get started with AI agents for marketing?

Start with a single high-volume, low-risk use case — typically content or SEO research. Use a team platform like [cowork.ink](https://app.cowork.ink) to run agents in a shared workspace where everyone can review outputs and iterate on prompts. Avoid multi-agent pipelines until you have single-agent reliability, and always keep a human in the loop for brand-voice decisions.

What are the risks of using AI agents in marketing?

The three main risks are hallucinations (leading models fabricate facts 15–27% of the time), brand safety (agents can generate off-brand or legally sensitive content at scale), and data privacy (agents that access CRM data must comply with GDPR and CCPA). Mitigate these with output review workflows, [AI agent guardrails](/blog/ai-agent-guardrails/), and strict data access controls.

How much ROI can I expect from AI agents in marketing?

McKinsey research shows AI-driven personalization can increase revenue by 5–8% and reduce cost-to-serve by up to 30%. Teams adopting marketing AI agents report 73% faster campaign development, 37% cost reductions in operations, and up to 20% higher conversion rates. Results vary significantly based on use case maturity and data quality.

AI Agents for Marketing: Content, SEO & Campaign Automation

AI agents for marketing automate content, SEO, campaigns & more. PROVEN ROI data, real risks, and a step-by-step maturity model. Start here.

Frequently Asked Questions

What is the difference between AI agents and marketing automation?: Traditional marketing automation follows fixed, rules-based workflows — "if X, then Y." AI agents are goal-driven: they perceive data, reason about the best action, and execute autonomously, even in situations they were never explicitly programmed for. An automation tool sends an email at a scheduled time; an AI agent decides *which* email to send, to *which* segment, and *when* based on real-time behavioral signals.
What can AI agents actually do for a marketing team?: AI agents can handle content briefs, draft and optimize copy, conduct SEO research, manage ad bidding, qualify leads, personalize email sequences, monitor brand sentiment, and generate performance reports — without human intervention on each task. They work best on repetitive, data-heavy tasks that benefit from continuous optimization. See our [guide to AI agent use cases](/blog/ai-agent-use-cases/) for a broader view.
How do I get started with AI agents for marketing?: Start with a single high-volume, low-risk use case — typically content or SEO research. Use a team platform like [cowork.ink](https://app.cowork.ink) to run agents in a shared workspace where everyone can review outputs and iterate on prompts. Avoid multi-agent pipelines until you have single-agent reliability, and always keep a human in the loop for brand-voice decisions.
What are the risks of using AI agents in marketing?: The three main risks are hallucinations (leading models fabricate facts 15–27% of the time), brand safety (agents can generate off-brand or legally sensitive content at scale), and data privacy (agents that access CRM data must comply with GDPR and CCPA). Mitigate these with output review workflows, [AI agent guardrails](/blog/ai-agent-guardrails/), and strict data access controls.
How much ROI can I expect from AI agents in marketing?: McKinsey research shows AI-driven personalization can increase revenue by 5–8% and reduce cost-to-serve by up to 30%. Teams adopting marketing AI agents report 73% faster campaign development, 37% cost reductions in operations, and up to 20% higher conversion rates. Results vary significantly based on use case maturity and data quality.