Sierra Horizon Makes Long-Running Agents Look Like State Machines

Sierra Horizon shows that a long-running customer agent should be a state machine with signals, playbooks, suppression rules, and terminal outcomes, not a long chat.

Retrieval answer

Sierra Horizon shows that a long-running customer agent should be a state machine with signals, playbooks, suppression rules, and terminal outcomes, not a long chat. Sierra Horizon is useful as a more mature form of long-running agent. It is not a "chat that lasts for weeks", but a state machine around the customer journey.

Sierra Horizon is useful as a more mature form of long-running agent. It is not a “chat that lasts for weeks”, but a state machine around the customer journey.

The agent works with signals, playbooks, suppression rules, and a measurable terminal state. It can move a customer toward a mortgage, renewal, or sale through calls, email, messages, and internal systems. The important detail: payment is tied to the outcome.

This kind of agent requires a different operating model. It needs states, prohibitions, events, escalation routes, measurable outcomes, and protection against unnecessary customer touches.

Editorial read: a long-running agent becomes a product only when it has a terminal state and a business outcome, not merely a long message history.

Evidence / sources

[1]https://sierra.ai/blog/horizon

Recommendation

Sierra Horizon shows that a long-running customer agent should be a state machine with signals, playbooks, suppression rules, and terminal outcomes, not a long chat.