AgentSky

About AgentSky

AgentSky is a managed service for running long-horizon AI agents in the cloud. Users pick a harness - Claude Code, Codex, Hermes, or OpenClaw - and a large language model, then launch an agent with one click or a CLI command. The agent operates in its own sandbox with history, state snapshots, and recovery, and it's reachable through WhatsApp, iMessage, Telegram, Slack, web, API, and CLI.

Review

AgentSky handles the infrastructure that production agents typically require, from sandboxing to cross-channel delivery. The platform was built after the team encountered these same challenges in their own product, and it already powers over 10,000 agent sessions on tycoon.us. The service keeps an agent always-on, restores state after crashes, and unifies conversation memory across every supported messaging channel.

Key Features

• One-click or CLI launch for agents using Claude Code, Codex, Hermes, or OpenClaw harnesses with any LLM.
• Access through eight channels: WhatsApp, iMessage, Telegram, Slack, web, CLI, and developer APIs, with a single agent able to converse across all of them while keeping shared context.
• State snapshots, backup, and restore - agents recover to their last state after a crash without restarting tasks from scratch.
• Health checks monitor agent processes; failed agents are detected, though the maker notes that health checks alone don't confirm task progress.
• Free parking: you pay only when the agent is actively working, not while it's idle.

Pricing and Value

Pricing specifics beyond the free-parking model are not yet defined on the public page. Parking an agent costs nothing, and charges apply only during active work periods. The maker hasn't published per-minute or per-session rates, so potential users should inquire directly for cost details.

Pros

• Deployment removes the need to manage sandboxes, harness versions, and channel integrations separately.
• Cross-channel memory means a user can start on Telegram and continue on Slack without losing context; the agent sees a unified conversation history.
• Crash recovery pulls from the latest state backup, so long-running tasks don't reset entirely when something goes wrong.
• Multi-harness support lets teams test different agent frameworks without building their own orchestration layer.
• Idle agents incur no cost, which can reduce expenses for workloads with intermittent activity.

Cons

• Harness or model routing - using a cheaper model for simple tasks and a stronger one for complex reasoning - is on the roadmap but not available yet.
• Dead agents and idle agents look the same on an invoice; the platform doesn't yet surface last-progress signals to distinguish a stuck agent from one that's simply waiting.
• Users who need granular control over the agent runtime, detailed progress observability, or custom idempotency handling for side effects may find the abstraction layer too opaque.

AgentSky fits teams that want to ship a multi-channel AI agent without building the surrounding infrastructure themselves. It's most practical for products where cross-platform conversation continuity matters and where the agent's work pattern includes periods of inactivity that make the free-parking model cost-effective. Developers who prefer to tune every aspect of state management or routing logic will likely want more visibility than the current service provides.

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