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.

Can AI really reduce technical debt, or does it just create more?

Both. 93% of developers report positive impacts (better docs, automated review), but 88% also report negative ones — AI generates code that "looked correct but was unreliable." AI reduces technical debt only when paired with human architectural oversight and automated quality gates. See our [AI agent guardrails guide](/blog/ai-agent-guardrails/) for how to enforce those gates.

What types of technical debt can AI agents fix autonomously?

AI agents safely handle well-scoped bounded tasks: dependency upgrades, adding missing test coverage, removing code duplication, fixing linting violations, and generating documentation. They are not safe to deploy autonomously on architectural refactoring, business-logic-critical paths, or multi-system integration changes — those need human sign-off at every step.

How do you modernize legacy code with AI agents without downtime?

Use the Strangler Fig pattern augmented with AI agents. AI identifies legacy modules, generates a migration target, and incrementally routes traffic behind feature flags. Shadow testing runs both old and new implementations in parallel before any cutover. Rollback is automated — if error rates spike, traffic reverts to the legacy path instantly.

How much does technical debt cost companies?

McKinsey research shows technical debt accounts for 40% of IT balance sheets, with companies spending 30–40% of IT budgets in reactive legacy maintenance mode. Gartner puts the operational cost at 25% of engineering time and budget consumed by managing technical debt annually.

What is the biggest risk of using AI agents for code refactoring?

Two risks dominate. First, context blindness — AI agents cannot see internal business constraints, launch deadlines, or undocumented system behaviors a senior engineer would protect. Second, comprehension debt — code gets refactored but nobody on the team understands it anymore, making future incidents harder to diagnose.

AI Technical Debt: Fix It With Agents (Without Breaking Things)

AI technical debt costs 25% of eng budgets. Learn how to use AI agents to ELIMINATE it safely — with a phased playbook, no production incidents. Start today.

Frequently Asked Questions

Can AI really reduce technical debt, or does it just create more?: Both. 93% of developers report positive impacts (better docs, automated review), but 88% also report negative ones — AI generates code that "looked correct but was unreliable." AI reduces technical debt only when paired with human architectural oversight and automated quality gates. See our [AI agent guardrails guide](/blog/ai-agent-guardrails/) for how to enforce those gates.
What types of technical debt can AI agents fix autonomously?: AI agents safely handle well-scoped bounded tasks: dependency upgrades, adding missing test coverage, removing code duplication, fixing linting violations, and generating documentation. They are not safe to deploy autonomously on architectural refactoring, business-logic-critical paths, or multi-system integration changes — those need human sign-off at every step.
How do you modernize legacy code with AI agents without downtime?: Use the Strangler Fig pattern augmented with AI agents. AI identifies legacy modules, generates a migration target, and incrementally routes traffic behind feature flags. Shadow testing runs both old and new implementations in parallel before any cutover. Rollback is automated — if error rates spike, traffic reverts to the legacy path instantly.
How much does technical debt cost companies?: McKinsey research shows technical debt accounts for 40% of IT balance sheets, with companies spending 30–40% of IT budgets in reactive legacy maintenance mode. Gartner puts the operational cost at 25% of engineering time and budget consumed by managing technical debt annually.
What is the biggest risk of using AI agents for code refactoring?: Two risks dominate. First, context blindness — AI agents cannot see internal business constraints, launch deadlines, or undocumented system behaviors a senior engineer would protect. Second, comprehension debt — code gets refactored but nobody on the team understands it anymore, making future incidents harder to diagnose.