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 the core components of an AI agent architecture?

Every AI agent has five core components: a perception layer (how it receives input), a reasoning engine (the LLM that decides what to do), a memory system (short-term and long-term state), a tool/action layer (what the agent can do), and an orchestration layer (how it coordinates steps and other agents). See our [full breakdown below](/blog/ai-agent-architecture/).

What is the ReAct pattern in AI agent architecture?

ReAct (Reasoning + Acting) is the dominant agent loop pattern. The agent alternates between Thought (reasoning about the next step), Action (calling a tool or API), and Observation (processing the result) in a loop until the task is complete. It's the foundation of most production agent frameworks including LangGraph, OpenAI Agents SDK, and Claude Agent SDK.

When should I use a single-agent vs. multi-agent architecture?

Start with a single agent. Add multiple agents when a task requires genuinely different expertise (a researcher + a writer + a fact-checker), when parallelism would meaningfully speed up the work, or when the context window of a single agent would be overwhelmed. Multi-agent systems add coordination overhead — only introduce them when the single-agent ceiling is clearly hit.

What are the main AI agent orchestration patterns?

The four main patterns are: (1) Single-agent loop — one agent handles everything; (2) Sequential pipeline — agents run in a fixed order, output to input; (3) Hierarchical — an orchestrator agent delegates to specialized worker agents; (4) Collaborative swarm — agents share context and vote or negotiate. Most production systems are hierarchical at the top with sequential pipelines within each specialist agent.

What is the difference between short-term and long-term memory in AI agents?

Short-term (working) memory is the agent's in-context scratchpad for the current task — it exists for the duration of one session. Long-term memory persists across sessions and is stored in a vector database or key-value store. The agent retrieves relevant long-term memories at the start of each session based on the current task. Learn more about [context engineering for AI agents](/blog/context-engineering-ai-agents/).

AI Agent Architecture: Components, Patterns & Design Decisions

COMPLETE guide to AI agent architecture in 2026. The 5 core components, 4 orchestration patterns & key design decisions for production.

Frequently Asked Questions

What are the core components of an AI agent architecture?: Every AI agent has five core components: a perception layer (how it receives input), a reasoning engine (the LLM that decides what to do), a memory system (short-term and long-term state), a tool/action layer (what the agent can do), and an orchestration layer (how it coordinates steps and other agents). See our [full breakdown below](/blog/ai-agent-architecture/).
What is the ReAct pattern in AI agent architecture?: ReAct (Reasoning + Acting) is the dominant agent loop pattern. The agent alternates between Thought (reasoning about the next step), Action (calling a tool or API), and Observation (processing the result) in a loop until the task is complete. It's the foundation of most production agent frameworks including LangGraph, OpenAI Agents SDK, and Claude Agent SDK.
When should I use a single-agent vs. multi-agent architecture?: Start with a single agent. Add multiple agents when a task requires genuinely different expertise (a researcher + a writer + a fact-checker), when parallelism would meaningfully speed up the work, or when the context window of a single agent would be overwhelmed. Multi-agent systems add coordination overhead — only introduce them when the single-agent ceiling is clearly hit.
What are the main AI agent orchestration patterns?: The four main patterns are: (1) Single-agent loop — one agent handles everything; (2) Sequential pipeline — agents run in a fixed order, output to input; (3) Hierarchical — an orchestrator agent delegates to specialized worker agents; (4) Collaborative swarm — agents share context and vote or negotiate. Most production systems are hierarchical at the top with sequential pipelines within each specialist agent.
What is the difference between short-term and long-term memory in AI agents?: Short-term (working) memory is the agent's in-context scratchpad for the current task — it exists for the duration of one session. Long-term memory persists across sessions and is stored in a vector database or key-value store. The agent retrieves relevant long-term memories at the start of each session based on the current task. Learn more about [context engineering for AI agents](/blog/context-engineering-ai-agents/).