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Once your agent passes testing and you’re confident in its behavior, it’s time to ship it. Hive supports three deployment models depending on your needs.

Deployment Models

Local (Docker)

Full control. Run agents on your own infrastructure with Docker.

Aden Cloud

Zero-ops. Managed deployment with built-in logging, scaling, and credential management.

Autonomous

Self-running. Agents that operate continuously with event-driven triggers and self-recovery.

Local Deployment

By default, Hive supports deployment through Docker. This gives you full control over your runtime environment, networking, and credentials.
1

Pre-flight validation

Validate your agent before deploying. This checks structure, credentials, and dependencies.
hive validate <agent_name>
2

Deploy with one command

hive deploy local <agent_name>
This builds a container with your agent, its dependencies, and the Hive runtime.
3

Configure credentials

Your agent uses local credentials for APIs and services. For OAuth-based integrations, Aden Cloud handles token refresh automatically.
4

Verify the deployment

Check that the agent is running and accessible:
hive status <agent_name>
Prefer isolated environments (container, VM, or dedicated host). Keep credentials in secure secret stores. Use environment-scoped configs for staging vs. production.

Cloud Deployment

If you want managed infrastructure with easier credential management and built-in observability, Aden Cloud is the simpler path. You get secure defaults, scaling, and logging out of the box — at the cost of less low-level control. What you get with Aden Cloud:
  • Managed compute and scaling
  • Built-in credential sync and OAuth refresh
  • Real-time logging and monitoring dashboard
  • Automatic restarts and failure recovery
  • Cost tracking and budget enforcement
See Create an Account to get started with Aden Cloud, or Aden Credential Sync for how cloud credentials work.

Autonomous Agent Deployment

For agents that need to run continuously without human intervention, Hive provides built-in support for autonomous operation:
  • Memory sustainability — Decide what memory to keep and what to discard across sessions, so your agent doesn’t grow unbounded or forget critical context
  • Event source management — Trigger agent runs from external events like incoming emails, webhooks, or scheduled cron jobs
  • Recoverability — Automatically recover from crashes and resume from the last checkpoint
  • Repeatability — Ensure consistent behavior across identical inputs
  • Volume handling — Support batch operations when you need to process many items at once

Deployment Strategy

Interactive and autonomous modes look different from the outside, but the core remains the same. Your deployment strategy should be consistent across both:
1

Start interactive

Run your agent with hive tui to validate behavior with real inputs.
2

Move to scheduled

Set up cron-based or event-triggered runs. Monitor logs and costs.
3

Go autonomous

Remove the human-in-the-loop gates for low-risk decisions. Keep alerts for failures and escalations.

Runtime Guardrails

Hive provides built-in safety mechanisms to keep your agents within bounds in production:
  • Cost limits — Set maximum spend per run, per day, or per month
  • Timeout policies — Prevent runaway execution with configurable timeouts
  • Model degradation — Automatically fall back to cheaper models when budget runs low
  • Human-in-the-loop gates — Require human approval for high-impact actions
  • Escalation policies — Route failures to the right person instead of silently breaking

Developer Trust

To deploy agents for production use with confidence, Hive provides:
  • Transparency — Full visibility into what the agent did and why through three-level logging
  • Control — Configurable guardrails, intervention points, and approval flows
  • Reliability — Self-healing mechanisms and failure recovery built into the runtime

Next Steps