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.

How do AI agents handle errors differently than traditional software?

AI agents fail non-deterministically — the same input can produce different outputs, hallucinations, or silent quality degradation. Traditional retry logic assumes identical inputs yield identical results, but LLM-based agents need semantic validation and quality-aware error handling instead.

What is the circuit breaker pattern for AI agents?

A circuit breaker tracks consecutive failures to an external tool or API. After a threshold (e.g. 5 failures in 60 seconds), it "opens" and blocks further calls, returning a fallback response instead. After a cooldown period, it enters a half-open state to test if the service has recovered. Learn more in our [agent architecture guide](/blog/ai-agent-architecture/).

How do you prevent cascading failures in multi-agent systems?

Isolate agents with independent error boundaries, use circuit breakers between agent-to-agent calls, implement timeout limits per agent step, and design downstream agents to operate in degraded mode when upstream agents return partial results.

What is graceful degradation in AI agent systems?

Graceful degradation means an agent continues providing value at reduced capability when components fail — for example, falling back from GPT-4 to a smaller model, returning cached results, or escalating to a human rather than crashing silently.

How many retries should an AI agent attempt before failing?

Most production agents use 2-3 retries with exponential backoff for transient errors like rate limits or network timeouts. For LLM quality failures (bad output format, hallucinations), limit retries to 1-2 attempts with a modified prompt before falling back to an alternative strategy.

AI Agent Error Handling: Retries, Fallbacks & Recovery

Master AI agent error handling with PROVEN retry, fallback, and recovery patterns. Build resilient agents that recover gracefully. Start now.

Frequently Asked Questions

How do AI agents handle errors differently than traditional software?: AI agents fail non-deterministically — the same input can produce different outputs, hallucinations, or silent quality degradation. Traditional retry logic assumes identical inputs yield identical results, but LLM-based agents need semantic validation and quality-aware error handling instead.
What is the circuit breaker pattern for AI agents?: A circuit breaker tracks consecutive failures to an external tool or API. After a threshold (e.g. 5 failures in 60 seconds), it "opens" and blocks further calls, returning a fallback response instead. After a cooldown period, it enters a half-open state to test if the service has recovered. Learn more in our [agent architecture guide](/blog/ai-agent-architecture/).
How do you prevent cascading failures in multi-agent systems?: Isolate agents with independent error boundaries, use circuit breakers between agent-to-agent calls, implement timeout limits per agent step, and design downstream agents to operate in degraded mode when upstream agents return partial results.
What is graceful degradation in AI agent systems?: Graceful degradation means an agent continues providing value at reduced capability when components fail — for example, falling back from GPT-4 to a smaller model, returning cached results, or escalating to a human rather than crashing silently.
How many retries should an AI agent attempt before failing?: Most production agents use 2-3 retries with exponential backoff for transient errors like rate limits or network timeouts. For LLM quality failures (bad output format, hallucinations), limit retries to 1-2 attempts with a modified prompt before falling back to an alternative strategy.