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 an AI agent governance framework?

An AI agent governance framework is a structured set of policies, processes, and technical controls that define how autonomous AI agents are authorized, monitored, and held accountable within an organization. Unlike traditional AI governance (which focuses on model outputs), agent governance covers the full lifecycle — from identity and scope definition to real-time observability and decommissioning. Learn more in our [guide to autonomous AI agents](/blog/autonomous-ai-agents/).

How is agentic AI governance different from traditional AI governance?

Traditional AI governance addresses static models that respond to queries. Agentic AI governance addresses systems that take sequences of actions — browsing the web, writing code, calling APIs, managing files — often without human approval at each step. The key difference is that agents act in the world, not just predict; governance must cover authorization, action scope, rollback, and audit trails for every tool call.

What are the key components of an AI agent governance framework?

The six core pillars are: (1) agent identity and scope definition, (2) human oversight controls (human-in-the-loop vs. human-on-the-loop), (3) access controls and least privilege, (4) monitoring, observability, and audit trails, (5) multi-agent pipeline governance, and (6) lifecycle management from deployment to decommissioning.

Does the EU AI Act apply to AI agents?

Yes. The EU AI Act classifies AI systems by risk level. Autonomous AI agents that make consequential decisions in areas like hiring, credit, healthcare, or critical infrastructure are likely classified as high-risk and require mandatory conformity assessments, human oversight mechanisms, and detailed audit logs. Organizations deploying agents in the EU should map each agent to the risk classification table and implement controls accordingly.

How do you prevent shadow AI agents in an enterprise?

Shadow AI agents — unauthorized agents built outside IT governance — are best caught through a combination of network egress monitoring (watching for LLM API calls), a formal agent registration process, and an internal agent marketplace where teams can find pre-approved agents. [cowork.ink](https://app.cowork.ink) provides a shared workspace where all team agents are visible and auditable, eliminating the blind spots that allow shadow AI to grow.

AI Agent Governance Framework: Complete Guide (2026)

COMPLETE guide to AI agent governance frameworks — 6 pillars, step-by-step roadmap, EU AI Act compliance, and real implementation patterns. Build SAFER agents today.

Frequently Asked Questions

What is an AI agent governance framework?: An AI agent governance framework is a structured set of policies, processes, and technical controls that define how autonomous AI agents are authorized, monitored, and held accountable within an organization. Unlike traditional AI governance (which focuses on model outputs), agent governance covers the full lifecycle — from identity and scope definition to real-time observability and decommissioning. Learn more in our [guide to autonomous AI agents](/blog/autonomous-ai-agents/).
How is agentic AI governance different from traditional AI governance?: Traditional AI governance addresses static models that respond to queries. Agentic AI governance addresses systems that take sequences of actions — browsing the web, writing code, calling APIs, managing files — often without human approval at each step. The key difference is that agents act in the world, not just predict; governance must cover authorization, action scope, rollback, and audit trails for every tool call.
What are the key components of an AI agent governance framework?: The six core pillars are: (1) agent identity and scope definition, (2) human oversight controls (human-in-the-loop vs. human-on-the-loop), (3) access controls and least privilege, (4) monitoring, observability, and audit trails, (5) multi-agent pipeline governance, and (6) lifecycle management from deployment to decommissioning.
Does the EU AI Act apply to AI agents?: Yes. The EU AI Act classifies AI systems by risk level. Autonomous AI agents that make consequential decisions in areas like hiring, credit, healthcare, or critical infrastructure are likely classified as high-risk and require mandatory conformity assessments, human oversight mechanisms, and detailed audit logs. Organizations deploying agents in the EU should map each agent to the risk classification table and implement controls accordingly.
How do you prevent shadow AI agents in an enterprise?: Shadow AI agents — unauthorized agents built outside IT governance — are best caught through a combination of network egress monitoring (watching for LLM API calls), a formal agent registration process, and an internal agent marketplace where teams can find pre-approved agents. [cowork.ink](https://app.cowork.ink) provides a shared workspace where all team agents are visible and auditable, eliminating the blind spots that allow shadow AI to grow.