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.

How do AI agents find bugs automatically?

AI agents combine static analysis, dynamic runtime observation, and LLM-based reasoning trained on large codebases. They trace logs, stack traces, and execution paths to locate the root cause, then generate context-aware fix suggestions — all without waiting for a human to trigger each step. Learn more in our [guide to AI agent error handling](/blog/ai-agent-error-handling/).

Can AI fix bugs without human review?

Partially. GitHub Copilot Autofix resolves over two-thirds of found security vulnerabilities with little or no editing. However, 66% of developers report that AI-generated fixes appear correct but fail during real-world testing — so human review remains essential for complex or security-critical code.

What is the difference between an AI debugger and a traditional static analyzer?

Traditional static analyzers apply fixed rule sets and cannot reason about developer intent. AI debuggers use machine learning and NLP to understand why code was written a certain way and generate context-specific suggestions for novel patterns not covered by static rules.

How do you debug AI agents themselves?

Debugging AI agents requires specialized observability tools because agents are non-deterministic — the same input can produce different outputs across runs. Tools like LangSmith and Arize provide distributed tracing (recording every tool call and LLM prompt) and span-level evaluation. See our [AI agent observability guide](/blog/ai-agent-observability/) for a full breakdown.

How accurate are AI agents at solving real bugs?

As of 2026, top AI agents on the SWE-bench benchmark resolve up to 75.2% of real GitHub issues. GitHub Copilot Autofix reduces median fix time from 1.5 hours to 28 minutes. Performance varies significantly by bug complexity — well-isolated, reproducible bugs see the highest accuracy.

AI Agent Debugging: How Agents Find & Fix Bugs Automatically

COMPLETE guide to AI agent debugging — how autonomous agents detect and repair code defects. Benchmarks, limitations, and real-world results. Learn more!

Frequently Asked Questions

How do AI agents find bugs automatically?: AI agents combine static analysis, dynamic runtime observation, and LLM-based reasoning trained on large codebases. They trace logs, stack traces, and execution paths to locate the root cause, then generate context-aware fix suggestions — all without waiting for a human to trigger each step. Learn more in our [guide to AI agent error handling](/blog/ai-agent-error-handling/).
Can AI fix bugs without human review?: Partially. GitHub Copilot Autofix resolves over two-thirds of found security vulnerabilities with little or no editing. However, 66% of developers report that AI-generated fixes appear correct but fail during real-world testing — so human review remains essential for complex or security-critical code.
What is the difference between an AI debugger and a traditional static analyzer?: Traditional static analyzers apply fixed rule sets and cannot reason about developer intent. AI debuggers use machine learning and NLP to understand why code was written a certain way and generate context-specific suggestions for novel patterns not covered by static rules.
How do you debug AI agents themselves?: Debugging AI agents requires specialized observability tools because agents are non-deterministic — the same input can produce different outputs across runs. Tools like LangSmith and Arize provide distributed tracing (recording every tool call and LLM prompt) and span-level evaluation. See our [AI agent observability guide](/blog/ai-agent-observability/) for a full breakdown.
How accurate are AI agents at solving real bugs?: As of 2026, top AI agents on the SWE-bench benchmark resolve up to 75.2% of real GitHub issues. GitHub Copilot Autofix reduces median fix time from 1.5 hours to 28 minutes. Performance varies significantly by bug complexity — well-isolated, reproducible bugs see the highest accuracy.