NightAgent

AI-powered after-hours service intake, triage, ticket lifecycle, customer follow-up, opportunity handoff, and agent evaluation.

NightAgent is a portfolio demonstration of how an AI voice agent can support the complete after-hours service lifecycle for a physical-security integrator. Instead of stopping at a chatbot or voice demo, NightAgent connects the customer conversation to operational workflow: capture the problem, create and prioritize a service ticket, simulate dispatch and repair progression, call the customer back, verify the outcome, identify follow-up sales or service needs, and regression-test agent behavior against problems found during real demo calls.

Live demo: https://nightshift-dispatch.vercel.app/demo
Evaluation Lab: https://nightshift-dispatch.vercel.app/lab
On your phone: open the live demo and tap "Add NightAgent to your home screen" at the bottom. It then opens from its own icon, like an app (Chrome installs it in one tap; Firefox, DuckDuckGo, and Safari show the menu steps). Calls still need signal.
How it's built: Architecture · Run it locally · Jargon Bench

Demo note: The customer/AI interaction demonstrates the conversational experience. Dispatch, technician assignment, repair timing, and lifecycle progression are intentionally accelerated/simulated so a recruiter or reviewer can experience an hours-long service workflow in minutes. The UI labels simulated events accordingly.

Why I Built It

After-hours service is more than answering a phone call. A useful system has to understand what happened, determine urgency, capture enough information for service, keep the customer informed, and make sure the issue is actually resolved.

NightAgent demonstrates how AI can sit inside that workflow rather than exist as a standalone assistant. The Evaluation Lab extends that idea by showing that a production-style agent also needs repeatable evaluation: when a problem is discovered in a live conversation, it can become a regression test that protects the behavior going forward.

Two Ways to Explore the Demo

Simple Mode

Simple Mode is designed for a recruiter, hiring manager, customer, or business stakeholder. It keeps the experience focused on the customer journey: talk to Sam, create a ticket, follow the service lifecycle, and see the post-service callback and downstream actions.

Engineering Mode

Engineering Mode exposes what is happening behind each conversational turn. It shows the path from caller audio through speech-to-text, agent reasoning, tool calls, deterministic business rules, persistence, voice response, and grading.

The goal is to make the implementation inspectable rather than presenting the voice agent as a black box.

Caller audio
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Speech to text
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Agent decision
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Tool call
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Rules in Python/FastAPI
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Record saved in Supabase
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Voice response
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Evaluation / grading

The Agents

Four ElevenLabs agents share the work. The backend decides what happens; the agents talk and call tools.

Agent	Role	Tools
Sam	After-hours front desk and dispatcher: safety first, finds the account, confirms name and number, opens and prioritizes tickets, pages the on-call technician for emergencies, checks or adds to a ticket the caller already has, takes messages, and hands off to a specialist	lookup_customer, create_ticket, page_on_call_tech, check_ticket_status, add_ticket_note, take_message, transfer_to_agent
Jordan	Billing assistant (own voice): looks up invoices and flags possible duplicate charges; never promises a refund or credit. Can also check or add to a ticket the caller already has	billing_lookup, request_billing_review, check_ticket_status, add_ticket_note, take_message
Riley	Sales assistant (own voice): gathers what the caller wants; never quotes a price, the account executive does	record_sales_interest, take_message
Follow-up call	Sam, in the same voice, calls the customer back after the repair and reports one structured outcome; routing rules in code decide what happens next	record_follow_up_outcome

The staff behind the agents (billing contact, account executive, service manager, technicians) are fictional and labeled that way.

Sam on Deepgram

/deepgram runs the same Sam on the Deepgram Voice Agent API: Flux (flux-general-en) listens and decides when the caller has finished a turn, Claude (Deepgram-managed) thinks, and Aura-2 speaks. Sam's tools, business rules, ticket board, and Zendesk logging are shared with the ElevenLabs version, so only the voice layer changes.

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