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 KPIs should I track for AI agents in production?

Track five categories: reliability (task success rate, error rate), performance (latency per step, throughput), cost (tokens per session, cost per goal completion), quality (hallucination rate, tool selection accuracy), and user impact (CSAT, task completion rate). See our full [AI agent testing guide](/blog/ai-agent-testing/) for evaluation strategies.

How is AI agent monitoring different from traditional APM?

Traditional APM tracks request latency, error rates, and throughput. AI agent monitoring adds LLM-specific signals like token costs, reasoning chain traces, hallucination rates, tool call patterns, and output quality scores. You need both layers for production agents.

What tools are best for monitoring AI agents in 2026?

Leading tools include Langfuse (open-source tracing), Arize Phoenix (drift detection), Datadog LLM Observability (infrastructure integration), Helicone (cost tracking), and Braintrust (evaluation-first). The right choice depends on your stack and whether you need self-hosting. See our [observability deep-dive](/blog/ai-agent-observability/) for tool comparisons.

How do I set up alerts for AI agent failures?

Start with three alert tiers. Critical alerts (PagerDuty) for task success rate below 80% or error rate spikes. Warning alerts (Slack) for latency P95 exceeding 2x baseline or cost anomalies. Info alerts (email digest) for quality score drift or token usage trends.

How often should I review AI agent dashboards?

Review real-time dashboards during incidents and deployments. Check daily summary dashboards every morning for overnight anomalies. Run weekly KPI reviews to track trends and cost drift. Monthly deep-dives should cover model performance, quality evaluations, and capacity planning.

AI Agent Monitoring: Dashboards, Alerts & KPIs

Build PRODUCTION dashboards, alerts, and KPI tracking for AI agents. Metrics that matter, tools that work. Start monitoring now.

Frequently Asked Questions

What KPIs should I track for AI agents in production?: Track five categories: reliability (task success rate, error rate), performance (latency per step, throughput), cost (tokens per session, cost per goal completion), quality (hallucination rate, tool selection accuracy), and user impact (CSAT, task completion rate). See our full [AI agent testing guide](/blog/ai-agent-testing/) for evaluation strategies.
How is AI agent monitoring different from traditional APM?: Traditional APM tracks request latency, error rates, and throughput. AI agent monitoring adds LLM-specific signals like token costs, reasoning chain traces, hallucination rates, tool call patterns, and output quality scores. You need both layers for production agents.
What tools are best for monitoring AI agents in 2026?: Leading tools include Langfuse (open-source tracing), Arize Phoenix (drift detection), Datadog LLM Observability (infrastructure integration), Helicone (cost tracking), and Braintrust (evaluation-first). The right choice depends on your stack and whether you need self-hosting. See our [observability deep-dive](/blog/ai-agent-observability/) for tool comparisons.
How do I set up alerts for AI agent failures?: Start with three alert tiers. Critical alerts (PagerDuty) for task success rate below 80% or error rate spikes. Warning alerts (Slack) for latency P95 exceeding 2x baseline or cost anomalies. Info alerts (email digest) for quality score drift or token usage trends.
How often should I review AI agent dashboards?: Review real-time dashboards during incidents and deployments. Check daily summary dashboards every morning for overnight anomalies. Run weekly KPI reviews to track trends and cost drift. Monthly deep-dives should cover model performance, quality evaluations, and capacity planning.