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 tool calling in AI agents?

Tool calling is the mechanism that lets an LLM trigger external functions, APIs, or services during a conversation. Instead of just generating text, the model outputs a structured request (name + arguments) to invoke a specific tool — your application executes it and returns the result. This is the core mechanism that turns a chatbot into an AI agent.

What is the difference between function calling and tool calling?

They're the same thing with different branding. OpenAI originally called it "function calling." Anthropic calls it "tool use." Google uses both terms. In 2026 the industry has converged on "tool calling" as the broader term, since tools can be much more than simple functions — they include APIs, code interpreters, browser actions, and entire MCP servers.

Can AI agents call multiple tools at once?

Yes. Modern LLMs support parallel tool calling — the model can decide to call several tools in a single response when their results are independent of each other. This dramatically reduces latency for multi-step tasks (e.g., fetching weather and calendar data simultaneously). OpenAI, Anthropic, and Google all support parallel tool calls.

How do you define a tool for an AI agent?

A tool is defined as a JSON schema with three required elements: a name (snake_case, no spaces), a description (this is what the model reads to decide whether to use the tool), and a parameters object describing the expected inputs and their types. The description is the most important part — a poor description is the #1 cause of tool selection mistakes.

What happens when a tool call fails?

When a tool call fails, the agent receives the error response as an observation in its loop. A well-designed agent will interpret the error, decide whether to retry with different parameters, try a fallback tool, or surface the issue to the user. The key is to return structured, descriptive error messages — not raw stack traces — so the model can reason about what went wrong.

AI Agent Tool Calling: How Agents Use APIs, Functions & External Tools

COMPLETE guide to AI agent tool calling in 2026. How agents call APIs, define tools, handle errors & use parallel tools. OpenAI, Anthropic & Google.

Frequently Asked Questions

What is tool calling in AI agents?: Tool calling is the mechanism that lets an LLM trigger external functions, APIs, or services during a conversation. Instead of just generating text, the model outputs a structured request (name + arguments) to invoke a specific tool — your application executes it and returns the result. This is the core mechanism that turns a chatbot into an AI agent.
What is the difference between function calling and tool calling?: They're the same thing with different branding. OpenAI originally called it "function calling." Anthropic calls it "tool use." Google uses both terms. In 2026 the industry has converged on "tool calling" as the broader term, since tools can be much more than simple functions — they include APIs, code interpreters, browser actions, and entire MCP servers.
Can AI agents call multiple tools at once?: Yes. Modern LLMs support parallel tool calling — the model can decide to call several tools in a single response when their results are independent of each other. This dramatically reduces latency for multi-step tasks (e.g., fetching weather and calendar data simultaneously). OpenAI, Anthropic, and Google all support parallel tool calls.
How do you define a tool for an AI agent?: A tool is defined as a JSON schema with three required elements: a name (snake_case, no spaces), a description (this is what the model reads to decide whether to use the tool), and a parameters object describing the expected inputs and their types. The description is the most important part — a poor description is the #1 cause of tool selection mistakes.
What happens when a tool call fails?: When a tool call fails, the agent receives the error response as an observation in its loop. A well-designed agent will interpret the error, decide whether to retry with different parameters, try a fallback tool, or surface the issue to the user. The key is to return structured, descriptive error messages — not raw stack traces — so the model can reason about what went wrong.