Deploying Agents

Once you have created an agent, written its instructions, selected a model, configured memory, and assigned skills, the next step is deployment. Deployment makes the agent available for conversations.

Agent lifecycle

Every agent in Horizon follows a lifecycle with four states:

State	Description
Draft	The agent is being configured. It is not available for conversations.
Active	The agent is live and accepting conversations from users and integrations.
Paused	The agent is temporarily suspended. Existing conversations are frozen; new conversations are rejected.
Archived	The agent is permanently deactivated. Conversation history is retained per your retention policy, but the agent cannot be reactivated.

Activating an agent

1. Open the agent from the Apps section in the left sidebar.
2. Verify that all required configuration is complete:
   • Instructions are written.
   • At least one skill is assigned.
   • A model is selected.
   • The agent is assigned to a department.
3. Click Activate in the top-right corner of the agent page.
4. Confirm the activation in the dialog. The agent’s state changes from Draft to Active.

Caution

Activation requires an active connection for every external service referenced by the agent’s skills. If you have assigned QuickBooks skills but have not connected a QuickBooks account, activation will fail with a connection error.

Testing before going live

Horizon provides a built-in testing environment so you can validate agent behavior before exposing it to your team.

Test Console

The Test Console is available for agents in any state (including Draft). Open it by clicking the Test button on the agent page.

• Send messages as if you were a user.
• See which skills the agent invokes and what parameters it sends.
• Inspect raw skill responses before the agent formats them.
• Test edge cases: malformed inputs, missing data, ambiguous requests.

The Test Console does not count against your department’s conversation limits, but it does consume tokens.

Dry Run Mode

For skills that modify data (e.g., Customer Upsert, Create Invoice), enable Dry Run Mode in the Test Console. In this mode:

• Skills are invoked normally, but write operations are simulated.
• The agent receives a realistic response without actually creating or modifying records.
• This lets you test the full conversation flow without affecting production data.

Monitoring conversations

Once the agent is active and handling real conversations, monitor its performance from several vantage points:

Conversation logs

Navigate to Conversations in the left sidebar to see all agent conversations. Each log entry shows:

• The user who initiated the conversation.
• Timestamps for each message.
• Skills invoked, including parameters and responses.
• Token consumption for the conversation.
• Any errors or retries.

Agent metrics

The agent’s Metrics tab provides aggregate statistics:

• Conversations per day — volume trends over time.
• Average turns per conversation — indicates how many back-and-forth exchanges users need.
• Skill success rate — percentage of skill invocations that succeeded without errors.
• Average response time — how quickly the agent replies (includes model inference and skill execution).
• Token usage — daily and cumulative token consumption.

Alerts

Configure alerts on the agent’s Settings tab to be notified when:

• The error rate exceeds a threshold.
• Token usage approaches a budget limit.
• A skill fails repeatedly.
• Response time degrades beyond a threshold.

Alerts can be sent via email, Slack, or webhook.

Pausing and resuming

If you need to temporarily take an agent offline — for maintenance, instruction updates, or issue investigation — use Pause rather than archiving:

• Click Pause on the agent page.
• Active conversations are frozen. Users see a message indicating the agent is temporarily unavailable.
• Make your changes (update instructions, fix skill configuration, etc.).
• Click Resume to bring the agent back online. Frozen conversations continue from where they left off.

Archiving

Archive an agent when it is permanently retired. Archiving:

• Stops all conversations.
• Retains conversation history and memory per your retention policy.
• Frees the agent name for reuse.
• Cannot be undone — create a new agent if you need the functionality again.

Deployment checklist

Before activating any agent for production use, walk through this checklist:

• Instructions cover persona, scope, constraints, and examples.
• The correct model tier is selected for the agent’s complexity level.
• All assigned skills have active connections.
• Memory retention settings comply with your organization’s data policies.
• The agent has been tested in the Test Console with representative queries.
• Write operations have been validated in Dry Run Mode.
• Alerts are configured for error rate and token budget thresholds.
• The agent is assigned to the correct department with appropriate access controls.