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 are the biggest security risks of AI agents?

The top risks are prompt injection (attackers hijacking agent behavior through malicious input), excessive tool permissions (agents with more access than they need), data exfiltration (agents leaking sensitive information through tool calls), and cascading failures in multi-agent systems where one compromised agent poisons the rest. See our [full risk breakdown](/blog/ai-agent-security/).

What is the OWASP Top 10 for Agentic Applications?

It's a 2026 framework from OWASP identifying the 10 most critical security risks for autonomous AI agents. The list covers prompt injection, excessive agency, tool poisoning, memory threats, and more. It was developed by 100+ industry experts and is the current standard for agentic AI security audits.

How do you prevent prompt injection in AI agents?

No single defense stops prompt injection. Use layered controls: separate trusted system prompts from untrusted user input, validate all tool calls against an allowlist, sandbox code execution, apply output filtering, and add human-in-the-loop approval for high-risk actions. See our guide to [AI agent guardrails](/blog/ai-agent-guardrails/) for implementation details.

Should AI agents have their own identity and access credentials?

Yes. Treat every AI agent as a distinct non-human identity with its own credentials, scoped permissions, and audit trail. Never share human user credentials with agents. Use short-lived tokens, rotate them frequently, and bind each token to a specific task scope.

How does AI agent security differ from traditional application security?

Traditional apps follow deterministic code paths — you can test every branch. AI agents are non-deterministic: the same input can produce different tool call sequences. This means static analysis and perimeter defenses aren't enough. Agent security requires runtime behavioral monitoring, dynamic permission scoping, and controls at the semantic layer, not just the network layer.

AI Agent Security: A Practical Guide to Risks and Controls

PRACTICAL guide to AI agent security in 2026. Covers prompt injection, tool abuse, the OWASP Top 10 for Agentic Apps, and production controls.

Frequently Asked Questions

What are the biggest security risks of AI agents?: The top risks are prompt injection (attackers hijacking agent behavior through malicious input), excessive tool permissions (agents with more access than they need), data exfiltration (agents leaking sensitive information through tool calls), and cascading failures in multi-agent systems where one compromised agent poisons the rest. See our [full risk breakdown](/blog/ai-agent-security/).
What is the OWASP Top 10 for Agentic Applications?: It's a 2026 framework from OWASP identifying the 10 most critical security risks for autonomous AI agents. The list covers prompt injection, excessive agency, tool poisoning, memory threats, and more. It was developed by 100+ industry experts and is the current standard for agentic AI security audits.
How do you prevent prompt injection in AI agents?: No single defense stops prompt injection. Use layered controls: separate trusted system prompts from untrusted user input, validate all tool calls against an allowlist, sandbox code execution, apply output filtering, and add human-in-the-loop approval for high-risk actions. See our guide to [AI agent guardrails](/blog/ai-agent-guardrails/) for implementation details.
Should AI agents have their own identity and access credentials?: Yes. Treat every AI agent as a distinct non-human identity with its own credentials, scoped permissions, and audit trail. Never share human user credentials with agents. Use short-lived tokens, rotate them frequently, and bind each token to a specific task scope.
How does AI agent security differ from traditional application security?: Traditional apps follow deterministic code paths — you can test every branch. AI agents are non-deterministic: the same input can produce different tool call sequences. This means static analysis and perimeter defenses aren't enough. Agent security requires runtime behavioral monitoring, dynamic permission scoping, and controls at the semantic layer, not just the network layer.