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 the difference between prompt engineering for chatbots vs. AI agents?

Chatbot prompts optimize a single response. Agent prompts must orchestrate multi-step behavior across tool calls, memory reads, and branching logic. An agent's system prompt is less like a question and more like a job description, a rulebook, and a tool manual combined — all in one context window that persists for the entire session.

What should a system prompt for an AI agent include?

A well-structured agent system prompt has six sections: Role (who the agent is), Objective (what it's trying to achieve), Tool Usage (how and when to call each tool), Constraints (what it must never do), Output Format (how to structure responses), and Examples (few-shot demonstrations of correct behavior). Omitting any section is a common cause of inconsistent agent behavior.

What is prompt chaining in AI agents?

Prompt chaining decomposes a complex task into a sequence of discrete LLM invocations, where each step's output feeds into the next. Instead of asking one prompt to research, analyze, and write a report simultaneously, you chain three prompts: one that researches, one that analyzes the research, and one that writes the report from the analysis. Each step is smaller, easier to debug, and more reliable.

How do few-shot examples improve AI agent reliability?

Few-shot examples show the agent the exact format, tone, and reasoning pattern you expect — rather than just describing it. For tool-calling agents, a single worked example (thought → tool call → observation → answer) can dramatically reduce malformed tool calls and off-format outputs. Three to five examples cover the most common cases; more than ten usually wastes tokens without further improvement.

Should I put dynamic state (like the current time) in the system prompt?

No. Dynamic state in the system prompt invalidates your prompt cache on every request, multiplying costs. Put static identity, rules, and examples in the system prompt. Pass dynamic state (current time, user context, session variables) in the first user message or a dedicated context injection block. This preserves cache hits and keeps the system prompt stable.

Prompt Engineering for AI Agents: System Prompts, Chains & Best Practices

COMPLETE guide to AI agent prompt engineering in 2026. System prompts, chaining, few-shot, tool calling & common mistakes. Copy-paste templates.

Frequently Asked Questions

What is the difference between prompt engineering for chatbots vs. AI agents?: Chatbot prompts optimize a single response. Agent prompts must orchestrate multi-step behavior across tool calls, memory reads, and branching logic. An agent's system prompt is less like a question and more like a job description, a rulebook, and a tool manual combined — all in one context window that persists for the entire session.
What should a system prompt for an AI agent include?: A well-structured agent system prompt has six sections: Role (who the agent is), Objective (what it's trying to achieve), Tool Usage (how and when to call each tool), Constraints (what it must never do), Output Format (how to structure responses), and Examples (few-shot demonstrations of correct behavior). Omitting any section is a common cause of inconsistent agent behavior.
What is prompt chaining in AI agents?: Prompt chaining decomposes a complex task into a sequence of discrete LLM invocations, where each step's output feeds into the next. Instead of asking one prompt to research, analyze, and write a report simultaneously, you chain three prompts: one that researches, one that analyzes the research, and one that writes the report from the analysis. Each step is smaller, easier to debug, and more reliable.
How do few-shot examples improve AI agent reliability?: Few-shot examples show the agent the exact format, tone, and reasoning pattern you expect — rather than just describing it. For tool-calling agents, a single worked example (thought → tool call → observation → answer) can dramatically reduce malformed tool calls and off-format outputs. Three to five examples cover the most common cases; more than ten usually wastes tokens without further improvement.
Should I put dynamic state (like the current time) in the system prompt?: No. Dynamic state in the system prompt invalidates your prompt cache on every request, multiplying costs. Put static identity, rules, and examples in the system prompt. Pass dynamic state (current time, user context, session variables) in the first user message or a dedicated context injection block. This preserves cache hits and keeps the system prompt stable.