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 makes an AI agent platform enterprise-grade?

Enterprise-grade AI agent platforms provide: self-hosted or on-premise deployment (data sovereignty), role-based access control (RBAC) with SSO, complete audit logging, container isolation per agent, horizontal scaling on Kubernetes, compliance certifications (SOC 2, HIPAA), and SLA guarantees. Most consumer and SMB platforms lack several of these.

What are the main security risks of enterprise AI agent platforms?

Key enterprise AI security risks include: data sent to third-party servers (SaaS platforms), prompt injection attacks manipulating agent behavior, over-permissioned agents with excessive tool access, lack of audit trails for regulatory compliance, and model output quality failures causing business impact. Mitigation requires defense-in-depth at each layer.

How do enterprises handle AI agent governance?

Enterprise AI agent governance typically includes: RBAC for who can create and modify agents, approval workflows for new agent deployments, regular audits of agent permissions and actions, cost controls and budget limits, incident response procedures for agent failures, and executive oversight via dashboards and reporting.

What scale should enterprise AI agent platforms support?

Enterprise platforms should support 100+ concurrent agents, 1,000+ users with differentiated permissions, horizontal scaling via Kubernetes, and high availability with failover. cowork.ink Business supports 200 agents per Kubernetes node — a 5-node cluster handles 1,000 concurrent agents.

Enterprise AI Agent Platforms: What to Look for in 2026 (Security, Scale, Control)

What to look for in enterprise AI agent platforms in 2026. Security, scalability, governance, and compliance requirements explained. Platform comparison included.

Frequently Asked Questions

What makes an AI agent platform enterprise-grade?: Enterprise-grade AI agent platforms provide: self-hosted or on-premise deployment (data sovereignty), role-based access control (RBAC) with SSO, complete audit logging, container isolation per agent, horizontal scaling on Kubernetes, compliance certifications (SOC 2, HIPAA), and SLA guarantees. Most consumer and SMB platforms lack several of these.
What are the main security risks of enterprise AI agent platforms?: Key enterprise AI security risks include: data sent to third-party servers (SaaS platforms), prompt injection attacks manipulating agent behavior, over-permissioned agents with excessive tool access, lack of audit trails for regulatory compliance, and model output quality failures causing business impact. Mitigation requires defense-in-depth at each layer.
How do enterprises handle AI agent governance?: Enterprise AI agent governance typically includes: RBAC for who can create and modify agents, approval workflows for new agent deployments, regular audits of agent permissions and actions, cost controls and budget limits, incident response procedures for agent failures, and executive oversight via dashboards and reporting.
What scale should enterprise AI agent platforms support?: Enterprise platforms should support 100+ concurrent agents, 1,000+ users with differentiated permissions, horizontal scaling via Kubernetes, and high availability with failover. cowork.ink Business supports 200 agents per Kubernetes node — a 5-node cluster handles 1,000 concurrent agents.