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 AI agent guardrails?

AI agent guardrails are rules, constraints, and protective mechanisms that keep AI agents operating safely, predictably, and within defined boundaries. They include input validation (blocking prompt injections), output filtering (catching harmful or hallucinated content), tool-use restrictions (limiting what the agent can do), and monitoring layers. Without guardrails, agents can leak PII, hallucinate facts, execute unauthorized actions, or be hijacked by prompt injection attacks.

What is the difference between NeMo Guardrails and LlamaGuard?

NeMo Guardrails is NVIDIA's open-source toolkit for adding programmable safety rails to LLM applications — it covers input, output, dialog, and tool-use guardrails using a domain-specific language called Colang. LlamaGuard is Meta's LLM-based classifier that categorizes prompts and responses as safe or unsafe against a taxonomy of harm categories. NeMo is an orchestration framework; LlamaGuard is a classification model. Many production systems use both together.

How do guardrails prevent prompt injection in AI agents?

Guardrails defend against prompt injection at multiple layers: dedicated classifiers like Meta's Prompt Guard 2 detect injection attempts in user input, NeMo Guardrails can reject or rewrite suspicious prompts before they reach the LLM, and output rails catch cases where an injection succeeded in manipulating the model's response. Azure's Spotlighting technique specifically targets indirect injection — malicious instructions hidden in retrieved documents or tool outputs.

Which AI guardrail framework should I use?

It depends on your stack. Use NeMo Guardrails for fine-grained, programmable safety with Colang policies. Use LlamaGuard 4 for LLM-based content classification (especially multimodal). Use Guardrails AI for composable validators with a hub of pre-built checks. If you're on AWS, Bedrock Guardrails provides managed safety with 99% hallucination detection accuracy. Most production systems combine two or more frameworks in a layered architecture.

Do AI agent guardrails add latency?

Yes, but modern frameworks minimize the impact. NeMo Guardrails supports GPU acceleration and in-memory caching to keep latency low. Lightweight classifiers like Prompt Guard 2 (86M parameters) add minimal overhead. The typical approach is to run fast rule-based checks synchronously and heavier ML-based checks asynchronously or only on flagged content. For most applications, the 50-200ms added latency is a worthwhile trade-off for preventing a $4.88M average breach cost.

AI Agent Guardrails: NeMo, LlamaGuard & Production Safety Layers

Guide to AI agent guardrails in 2026. Compare NeMo, LlamaGuard 4, Guardrails AI & cloud-native options. Build defense-in-depth safety for production.

Frequently Asked Questions

What are AI agent guardrails?: AI agent guardrails are rules, constraints, and protective mechanisms that keep AI agents operating safely, predictably, and within defined boundaries. They include input validation (blocking prompt injections), output filtering (catching harmful or hallucinated content), tool-use restrictions (limiting what the agent can do), and monitoring layers. Without guardrails, agents can leak PII, hallucinate facts, execute unauthorized actions, or be hijacked by prompt injection attacks.
What is the difference between NeMo Guardrails and LlamaGuard?: NeMo Guardrails is NVIDIA's open-source toolkit for adding programmable safety rails to LLM applications — it covers input, output, dialog, and tool-use guardrails using a domain-specific language called Colang. LlamaGuard is Meta's LLM-based classifier that categorizes prompts and responses as safe or unsafe against a taxonomy of harm categories. NeMo is an orchestration framework; LlamaGuard is a classification model. Many production systems use both together.
How do guardrails prevent prompt injection in AI agents?: Guardrails defend against prompt injection at multiple layers: dedicated classifiers like Meta's Prompt Guard 2 detect injection attempts in user input, NeMo Guardrails can reject or rewrite suspicious prompts before they reach the LLM, and output rails catch cases where an injection succeeded in manipulating the model's response. Azure's Spotlighting technique specifically targets indirect injection — malicious instructions hidden in retrieved documents or tool outputs.
Which AI guardrail framework should I use?: It depends on your stack. Use NeMo Guardrails for fine-grained, programmable safety with Colang policies. Use LlamaGuard 4 for LLM-based content classification (especially multimodal). Use Guardrails AI for composable validators with a hub of pre-built checks. If you're on AWS, Bedrock Guardrails provides managed safety with 99% hallucination detection accuracy. Most production systems combine two or more frameworks in a layered architecture.
Do AI agent guardrails add latency?: Yes, but modern frameworks minimize the impact. NeMo Guardrails supports GPU acceleration and in-memory caching to keep latency low. Lightweight classifiers like Prompt Guard 2 (86M parameters) add minimal overhead. The typical approach is to run fast rule-based checks synchronously and heavier ML-based checks asynchronously or only on flagged content. For most applications, the 50-200ms added latency is a worthwhile trade-off for preventing a $4.88M average breach cost.