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 you scale AI agents from prototype to production?

Scale AI agents in four phases: (1) externalize all state from in-memory to Redis or a database, (2) add a task queue for long-running agent jobs, (3) implement load balancing with sticky routing to preserve prompt caching, and (4) set up distributed tracing to monitor every agent hop. See our [guide to AI agent orchestration](/blog/ai-agent-orchestration/) for architecture patterns.

What are the main bottlenecks when scaling AI agents?

The four most common bottlenecks are: stateful in-memory context that breaks horizontal scaling, synchronous HTTP that times out on long agent tasks, uncontrolled token spend (which grows super-linearly with users), and lack of observability that makes failures impossible to diagnose.

How many concurrent users can an AI agent handle?

A single agent instance typically handles 10–50 concurrent users before latency degrades. Beyond that, you need horizontal scaling with a shared task queue, stateless agent workers, and external state storage. At 10,000 users you need a full production infrastructure stack with load balancing, caching, and multi-model routing.

What is the difference between centralized and decentralized multi-agent orchestration?

Centralized orchestration uses one coordinator that assigns tasks to sub-agents — 80.8% faster on parallelizable tasks but a single point of failure. Decentralized (peer-to-peer) agents self-coordinate — more resilient but harder to debug. Most production systems use a hybrid. Learn more in our [comparison of hierarchical vs peer-to-peer agents](/blog/hierarchical-vs-peer-to-peer-agents/).

How do you reduce the cost of running AI agents at scale?

Three levers: (1) prompt caching — can cut API costs 50–90% with sticky routing, (2) multi-model routing — direct simple tasks to cheap models and reserve large models for complex reasoning, and (3) context compression — summarize conversation history instead of sending full transcripts. See our [AI agent cost optimization guide](/blog/ai-agent-cost-optimization/) for a full breakdown.

AI Agent Scaling: From Prototype to 10,000 Users

COMPLETE guide to scaling AI agents in production. State management, load balancing, cost optimization, and orchestration patterns. Start scaling TODAY with cowork.ink.

Frequently Asked Questions

How do you scale AI agents from prototype to production?: Scale AI agents in four phases: (1) externalize all state from in-memory to Redis or a database, (2) add a task queue for long-running agent jobs, (3) implement load balancing with sticky routing to preserve prompt caching, and (4) set up distributed tracing to monitor every agent hop. See our [guide to AI agent orchestration](/blog/ai-agent-orchestration/) for architecture patterns.
What are the main bottlenecks when scaling AI agents?: The four most common bottlenecks are: stateful in-memory context that breaks horizontal scaling, synchronous HTTP that times out on long agent tasks, uncontrolled token spend (which grows super-linearly with users), and lack of observability that makes failures impossible to diagnose.
How many concurrent users can an AI agent handle?: A single agent instance typically handles 10–50 concurrent users before latency degrades. Beyond that, you need horizontal scaling with a shared task queue, stateless agent workers, and external state storage. At 10,000 users you need a full production infrastructure stack with load balancing, caching, and multi-model routing.
What is the difference between centralized and decentralized multi-agent orchestration?: Centralized orchestration uses one coordinator that assigns tasks to sub-agents — 80.8% faster on parallelizable tasks but a single point of failure. Decentralized (peer-to-peer) agents self-coordinate — more resilient but harder to debug. Most production systems use a hybrid. Learn more in our [comparison of hierarchical vs peer-to-peer agents](/blog/hierarchical-vs-peer-to-peer-agents/).
How do you reduce the cost of running AI agents at scale?: Three levers: (1) prompt caching — can cut API costs 50–90% with sticky routing, (2) multi-model routing — direct simple tasks to cheap models and reserve large models for complex reasoning, and (3) context compression — summarize conversation history instead of sending full transcripts. See our [AI agent cost optimization guide](/blog/ai-agent-cost-optimization/) for a full breakdown.