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 a multi-agent system in AI?

A multi-agent system (MAS) is a network of specialized AI agents, each with a distinct role, that coordinate to solve tasks too complex for a single agent. One agent might research, another might write, and a third might fact-check — together they outperform any single model on long-horizon tasks.

How do multi-agent systems differ from single-agent systems?

A single agent handles a task end-to-end with one context window and one set of skills. A multi-agent system distributes the task across specialized agents — increasing parallelism, fault tolerance, and the ability to handle tasks that exceed a single context window. The trade-off is added coordination overhead and harder observability.

When should you use a multi-agent system instead of a single agent?

Use multi-agent systems when a task requires more context than one agent can hold, when sub-tasks can run in parallel to save time, or when specialized expertise (coding vs. research vs. critique) is meaningfully different. Start with a single agent and only add agents when you hit a concrete ceiling. See our [guide to AI agent architecture](/blog/ai-agent-architecture/).

What are the main architecture patterns for multi-agent systems?

The four core patterns are sequential (pipeline), parallel, hierarchical (orchestrator-worker), and loop (generator-critic). Most production systems combine two or more. For a deeper dive, read our [AI agent orchestration guide](/blog/ai-agent-orchestration/).

What frameworks exist for building multi-agent systems?

Popular frameworks include LangGraph, CrewAI, AutoGen (AG2), and OpenAI Agents SDK. For teams that want a managed layer on top, [cowork.ink](https://app.cowork.ink) provides shared workspaces, orchestration, and observability without framework setup. See our [framework comparison](/blog/ag2-vs-crewai-vs-langgraph-openai-agents-sdk/).

Multi-Agent Systems: Architecture, Patterns & Best Practices (2026)

COMPLETE guide to multi-agent systems — architecture patterns, building blocks, failure modes, and frameworks. Build production-ready MAS today.

Frequently Asked Questions

What is a multi-agent system in AI?: A multi-agent system (MAS) is a network of specialized AI agents, each with a distinct role, that coordinate to solve tasks too complex for a single agent. One agent might research, another might write, and a third might fact-check — together they outperform any single model on long-horizon tasks.
How do multi-agent systems differ from single-agent systems?: A single agent handles a task end-to-end with one context window and one set of skills. A multi-agent system distributes the task across specialized agents — increasing parallelism, fault tolerance, and the ability to handle tasks that exceed a single context window. The trade-off is added coordination overhead and harder observability.
When should you use a multi-agent system instead of a single agent?: Use multi-agent systems when a task requires more context than one agent can hold, when sub-tasks can run in parallel to save time, or when specialized expertise (coding vs. research vs. critique) is meaningfully different. Start with a single agent and only add agents when you hit a concrete ceiling. See our [guide to AI agent architecture](/blog/ai-agent-architecture/).
What are the main architecture patterns for multi-agent systems?: The four core patterns are sequential (pipeline), parallel, hierarchical (orchestrator-worker), and loop (generator-critic). Most production systems combine two or more. For a deeper dive, read our [AI agent orchestration guide](/blog/ai-agent-orchestration/).
What frameworks exist for building multi-agent systems?: Popular frameworks include LangGraph, CrewAI, AutoGen (AG2), and OpenAI Agents SDK. For teams that want a managed layer on top, [cowork.ink](https://app.cowork.ink) provides shared workspaces, orchestration, and observability without framework setup. See our [framework comparison](/blog/ag2-vs-crewai-vs-langgraph-openai-agents-sdk/).