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 orchestration?

AI agent orchestration is the systematic coordination of multiple specialized AI agents so they collaborate to achieve a shared goal. An orchestration layer decomposes tasks, routes them to the right agents, manages shared state, and aggregates results — turning individual agents into a reliable, scalable system.

When should I use multi-agent orchestration instead of a single agent?

Add orchestration when a single agent hits a real ceiling: the task requires genuinely different expertise domains, the context window would be overwhelmed, or parallel execution would meaningfully cut latency. Don't add agents for the sake of it — multi-agent coordination is harder to debug and test than a well-designed single-agent loop.

What is the best framework for AI agent orchestration in 2026?

LangGraph for complex stateful production workflows; CrewAI for fast role-based prototyping; OpenAI Agents SDK for GPT-native deployment. For enterprise durability — especially long-running workflows lasting hours or days — Temporal provides the durable execution layer that runs underneath any framework. The right answer depends on your stack, team size, and use case.

How do you prevent AI agents from getting stuck in infinite loops?

Set hard caps on maximum steps, total cost, and handoff depth (typically 4–5 hops max). Define explicit termination criteria for every agent. Log every invocation so circular patterns are detectable before they spiral. Add periodic human checkpoints for long-running workflows.

What is the difference between centralized and decentralized agent orchestration?

Centralized orchestration uses a single orchestrator agent that directs all others — consistent and controllable, but a single point of failure. Decentralized agents communicate peer-to-peer and reach consensus independently — more resilient, but significantly harder to debug, audit, and reason about. Most production systems use centralized hierarchical orchestration with event-driven triggers for real-time signals.

AI Agent Orchestration: How to Coordinate Multiple Agents

COMPLETE guide to AI agent orchestration in 2026. 5 coordination patterns, top frameworks (LangGraph, CrewAI, OpenAI SDK) & proven best practices.

Frequently Asked Questions

What is AI agent orchestration?: AI agent orchestration is the systematic coordination of multiple specialized AI agents so they collaborate to achieve a shared goal. An orchestration layer decomposes tasks, routes them to the right agents, manages shared state, and aggregates results — turning individual agents into a reliable, scalable system.
When should I use multi-agent orchestration instead of a single agent?: Add orchestration when a single agent hits a real ceiling: the task requires genuinely different expertise domains, the context window would be overwhelmed, or parallel execution would meaningfully cut latency. Don't add agents for the sake of it — multi-agent coordination is harder to debug and test than a well-designed single-agent loop.
What is the best framework for AI agent orchestration in 2026?: LangGraph for complex stateful production workflows; CrewAI for fast role-based prototyping; OpenAI Agents SDK for GPT-native deployment. For enterprise durability — especially long-running workflows lasting hours or days — Temporal provides the durable execution layer that runs underneath any framework. The right answer depends on your stack, team size, and use case.
How do you prevent AI agents from getting stuck in infinite loops?: Set hard caps on maximum steps, total cost, and handoff depth (typically 4–5 hops max). Define explicit termination criteria for every agent. Log every invocation so circular patterns are detectable before they spiral. Add periodic human checkpoints for long-running workflows.
What is the difference between centralized and decentralized agent orchestration?: Centralized orchestration uses a single orchestrator agent that directs all others — consistent and controllable, but a single point of failure. Decentralized agents communicate peer-to-peer and reach consensus independently — more resilient, but significantly harder to debug, audit, and reason about. Most production systems use centralized hierarchical orchestration with event-driven triggers for real-time signals.