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 AI agent observability?

AI agent observability is the practice of collecting and analyzing telemetry data — traces, logs, metrics, and evaluations — from AI agent systems in production. Unlike traditional monitoring that asks "is the system up?", agent observability asks "is the system making good decisions?" by tracking reasoning chains, tool calls, and output quality.

How is AI observability different from traditional APM?

Traditional APM tracks request latency, error rates, and throughput. AI observability adds LLM-specific signals like token usage, prompt-response pairs, reasoning chain analysis, tool selection patterns, hallucination rates, and cost per session. You need both layers for production AI agents.

What are the best AI agent observability tools in 2026?

The leading AI agent observability tools in 2026 include Langfuse (open-source, self-hostable), Arize Phoenix (drift detection), Braintrust (evaluation-first), LangSmith (LangChain native), and Helicone (proxy-based cost tracking). The right choice depends on your framework, scale, and whether you need self-hosting.

What metrics should I track for AI agents in production?

Track latency per step (LLM calls, tool calls, total), token usage and cost per session, error and retry rates, tool call success rates, output quality scores (via automated evaluators), and hallucination rates. See our guide to [AI agent testing](/blog/ai-agent-testing/) for evaluation strategies.

Does OpenTelemetry support AI agent tracing?

Yes. The OpenTelemetry GenAI SIG has finalized semantic conventions for agent applications and is developing conventions for agent frameworks like CrewAI, AutoGen, and LangGraph. Using OTel prevents vendor lock-in and lets you export traces to any compatible backend.

AI Agent Observability: Logging, Tracing & Debugging

Learn how to implement AI agent observability with logging, tracing, and debugging. PRACTICAL guide with tool comparisons. Start monitoring now.

Frequently Asked Questions

What is AI agent observability?: AI agent observability is the practice of collecting and analyzing telemetry data — traces, logs, metrics, and evaluations — from AI agent systems in production. Unlike traditional monitoring that asks "is the system up?", agent observability asks "is the system making good decisions?" by tracking reasoning chains, tool calls, and output quality.
How is AI observability different from traditional APM?: Traditional APM tracks request latency, error rates, and throughput. AI observability adds LLM-specific signals like token usage, prompt-response pairs, reasoning chain analysis, tool selection patterns, hallucination rates, and cost per session. You need both layers for production AI agents.
What are the best AI agent observability tools in 2026?: The leading AI agent observability tools in 2026 include Langfuse (open-source, self-hostable), Arize Phoenix (drift detection), Braintrust (evaluation-first), LangSmith (LangChain native), and Helicone (proxy-based cost tracking). The right choice depends on your framework, scale, and whether you need self-hosting.
What metrics should I track for AI agents in production?: Track latency per step (LLM calls, tool calls, total), token usage and cost per session, error and retry rates, tool call success rates, output quality scores (via automated evaluators), and hallucination rates. See our guide to [AI agent testing](/blog/ai-agent-testing/) for evaluation strategies.
Does OpenTelemetry support AI agent tracing?: Yes. The OpenTelemetry GenAI SIG has finalized semantic conventions for agent applications and is developing conventions for agent frameworks like CrewAI, AutoGen, and LangGraph. Using OTel prevents vendor lock-in and lets you export traces to any compatible backend.