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 event-driven AI agent?

An event-driven AI agent stays dormant until triggered by a specific event — a message, a file upload, a CI/CD alert, or a state change — rather than polling on a schedule. Once the event fires, the agent perceives the payload, reasons over it, and acts. This makes responses nearly instantaneous and eliminates wasted compute between events.

How does event-driven architecture differ from polling in AI agents?

With polling, an agent repeatedly queries a data source at fixed intervals (every 30 seconds, every minute), burning compute even when nothing has changed. With event-driven architecture, the system pushes a notification the instant something happens. Production systems show this reduces agent response latency by 70–90% and computational resource usage by roughly 45%.

Why do multi-agent systems need event-driven architecture to scale?

In a point-to-point system, each agent must maintain a direct connection to every other agent, creating O(n²) connection complexity. With a central event bus, every agent connects only to the broker — reducing complexity to O(n). See our [multi-agent systems guide](/blog/multi-agent-systems/) for how this plays out in practice.

What events typically trigger AI agents?

Common triggers include user events (chat messages, form submissions), system events (CI/CD pipeline results, monitoring alerts), data events (new database records, S3 file uploads), time-based events (cron schedules), and inter-agent events (one agent emitting "research.complete" to trigger the next agent in a chain).

What infrastructure do event-driven AI agents use?

Common choices range from lightweight to enterprise-grade: webhooks (simplest), Redis Streams and RabbitMQ (moderate scale), and Apache Kafka or AWS EventBridge for high-throughput production systems. Agent frameworks like LangGraph and LlamaIndex Workflows have native event-driven primitives built in.

Event-Driven AI Agents: Real-Time Reactions Without Polling

Event-driven AI agents react the INSTANT a trigger fires—no polling loop needed. Cut response latency 70%+. See how engineering teams build real-time agent pipelines.

Frequently Asked Questions

What is an event-driven AI agent?: An event-driven AI agent stays dormant until triggered by a specific event — a message, a file upload, a CI/CD alert, or a state change — rather than polling on a schedule. Once the event fires, the agent perceives the payload, reasons over it, and acts. This makes responses nearly instantaneous and eliminates wasted compute between events.
How does event-driven architecture differ from polling in AI agents?: With polling, an agent repeatedly queries a data source at fixed intervals (every 30 seconds, every minute), burning compute even when nothing has changed. With event-driven architecture, the system pushes a notification the instant something happens. Production systems show this reduces agent response latency by 70–90% and computational resource usage by roughly 45%.
Why do multi-agent systems need event-driven architecture to scale?: In a point-to-point system, each agent must maintain a direct connection to every other agent, creating O(n²) connection complexity. With a central event bus, every agent connects only to the broker — reducing complexity to O(n). See our [multi-agent systems guide](/blog/multi-agent-systems/) for how this plays out in practice.
What events typically trigger AI agents?: Common triggers include user events (chat messages, form submissions), system events (CI/CD pipeline results, monitoring alerts), data events (new database records, S3 file uploads), time-based events (cron schedules), and inter-agent events (one agent emitting "research.complete" to trigger the next agent in a chain).
What infrastructure do event-driven AI agents use?: Common choices range from lightweight to enterprise-grade: webhooks (simplest), Redis Streams and RabbitMQ (moderate scale), and Apache Kafka or AWS EventBridge for high-throughput production systems. Agent frameworks like LangGraph and LlamaIndex Workflows have native event-driven primitives built in.