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 agent-to-agent (A2A) communication?

Agent-to-agent communication is the ability for one AI agent to discover, invoke, and exchange structured data with another agent — without a human in the loop. The A2A protocol, introduced by Google in April 2025, is the leading open standard for this, using Agent Cards for capability discovery and JSON-RPC over HTTPS for message transport.

What is the difference between MCP and A2A?

MCP (Model Context Protocol) connects an agent to tools and data sources — it is a vertical connection between an agent and its environment. A2A connects agents to other agents — it is a horizontal, peer-to-peer connection. They are complementary, not competing. See our [full MCP vs A2A comparison](/blog/mcp-vs-a2a/) for more detail.

How do AI agents communicate with each other in practice?

In A2A, a client agent sends a task to a remote agent's endpoint using JSON-RPC 2.0 over HTTPS. The remote agent processes the task and returns artifacts (text, files, structured data). For long-running work, Server-Sent Events (SSE) stream progress back in real time. The client agent first reads the remote agent's Agent Card — a JSON manifest listing its capabilities, auth requirements, and endpoint URL.

What protocols do AI agents use to communicate?

The four major protocols are: MCP (tool/data access), A2A (peer agent delegation), ACP (async Agent Communication Protocol by IBM/BeeAI), and ANP (Agent Network Protocol for decentralized P2P discovery). Most production deployments use MCP + A2A together — MCP for tools, A2A for cross-agent work.

What are the security risks in agent-to-agent communication?

Key risks include Agent Card spoofing (a malicious agent claiming false capabilities), task injection (crafted payloads hijacking agent behavior), session token theft between agents, and capability drift (agents exceeding their declared scope over time). A2A mitigates these with OAuth 2.0, HTTPS/TLS, and scoped capability declarations. See our [AI agent security guide](/blog/ai-agent-security/) for a full threat model.

Agent-to-Agent Communication: Protocols, Patterns & Standards

COMPLETE guide to agent-to-agent communication — A2A, MCP, ACP & ANP protocols explained, compared, and mapped to real use cases. Learn which protocol to use and when.

Frequently Asked Questions

What is agent-to-agent (A2A) communication?: Agent-to-agent communication is the ability for one AI agent to discover, invoke, and exchange structured data with another agent — without a human in the loop. The A2A protocol, introduced by Google in April 2025, is the leading open standard for this, using Agent Cards for capability discovery and JSON-RPC over HTTPS for message transport.
What is the difference between MCP and A2A?: MCP (Model Context Protocol) connects an agent to tools and data sources — it is a vertical connection between an agent and its environment. A2A connects agents to other agents — it is a horizontal, peer-to-peer connection. They are complementary, not competing. See our [full MCP vs A2A comparison](/blog/mcp-vs-a2a/) for more detail.
How do AI agents communicate with each other in practice?: In A2A, a client agent sends a task to a remote agent's endpoint using JSON-RPC 2.0 over HTTPS. The remote agent processes the task and returns artifacts (text, files, structured data). For long-running work, Server-Sent Events (SSE) stream progress back in real time. The client agent first reads the remote agent's Agent Card — a JSON manifest listing its capabilities, auth requirements, and endpoint URL.
What protocols do AI agents use to communicate?: The four major protocols are: MCP (tool/data access), A2A (peer agent delegation), ACP (async Agent Communication Protocol by IBM/BeeAI), and ANP (Agent Network Protocol for decentralized P2P discovery). Most production deployments use MCP + A2A together — MCP for tools, A2A for cross-agent work.
What are the security risks in agent-to-agent communication?: Key risks include Agent Card spoofing (a malicious agent claiming false capabilities), task injection (crafted payloads hijacking agent behavior), session token theft between agents, and capability drift (agents exceeding their declared scope over time). A2A mitigates these with OAuth 2.0, HTTPS/TLS, and scoped capability declarations. See our [AI agent security guide](/blog/ai-agent-security/) for a full threat model.