PromptFork

Write a brand-aligned support-agent system prompt with escalation rules

Produces a complete system prompt that defines a support agent's persona, voice, knowledge boundaries, and exact escalation rules — brand-aligned and safe to ship, not a generic 'be helpful'.

Open in Studio
Prompt
You are a senior conversational-AI designer who writes production support-agent system prompts that are safe, on-brand, and know their limits.

Write a complete system prompt for a customer-support AI agent. This will be pasted into a model as its operating instructions.

Brand & product context:
- Company & product: [NAME + ONE-LINE]
- Brand voice: [e.g. 'WARM, PLAIN-SPOKEN, A LITTLE DRY — NEVER CHEERLEADER' / 'CALM, FORMAL, ENTERPRISE']
- What the agent can fully resolve: [LIST — e.g. 'PASSWORD RESETS, BILLING FAQ, STATUS CHECKS']
- What it must NOT do: [e.g. 'ISSUE REFUNDS OVER $50, MODIFY CONTRACTS, GIVE MEDICAL OR LEGAL ADVICE']
- Escalation triggers: [WHEN IT MUST HAND TO A HUMAN — e.g. 'ANY THREAT TO SAFETY, CHARGEBACK THREATS, OUT-OF-SCOPE REQUESTS']
- Channels: [CHAT / EMAIL / TICKET]

The system prompt must cover, in this order:
1. Role & scope — who the agent is, who it serves, and the firm boundary that it is a support agent, not a source of truth on anything out of scope.
2. Voice & persona — concrete rules, not adjectives. Give 2 examples of on-brand and 2 of off-brand phrasing for the same question.
3. Resolution protocol — how it solves in-scope issues: confirm the problem, pull from the provided knowledge, give numbered steps, verify it worked. Never invent policy.
4. Knowledge boundary — it answers only from provided help-center material; on anything else it says it does not know and routes. It must say 'I am not sure' rather than guess.
5. Escalation rules — the explicit, enumerated triggers for handing to a human, and the exact handoff it performs (capture context, set priority, tell the customer what happens next and when).
6. Safety & privacy — never collect or echo full card numbers, government IDs, or passwords; never make commitments the company has not authorized (refunds, SLAs, legal statements).
7. Closing discipline — every reply ends by confirming resolution or stating the next step. Never leave a thread ambiguous.

Rules:
- The agent must never fabricate an answer. Unknown -> escalate, stated plainly.
- No unescalated high-risk actions. Refunds, account deletion, contract changes, and anything involving a vulnerable customer must route to a human.
- The system prompt is instructions to the AI, written in second person to the agent ('You are…', 'You must…').
- Keep it tight and directive — a model should follow it without ambiguity.

Output: the complete system prompt, ready to paste, followed by a short 'Launch checklist' of what to wire up before going live (knowledge source, escalation queue, eval set).

Success signal: the output is good only if the voice rules are concrete (with on-brand and off-brand examples), the escalation triggers are enumerated, and the agent is explicitly instructed to never fabricate and to route all high-risk actions to a human.

Use case

Use when launching an AI support agent and you need a system prompt that sounds on-brand and knows when to hand off to a human.

When to use this

Before deploying a support agent to real tickets. Pair with your help-center docs and a human-review loop.

Follow-up prompts

  • Write the escalation-handoff message the agent sends when it routes to a human.
  • Build the evaluation set of 20 tickets to test this persona before launch.
  • Create the brand-voice one-pager the agent references for tone.
#customer-support#system-prompt#ai-agents#automation#cx
Source
promptfork seed
License
CC-BY-4.0
Published
6/22/2026

More prompts you might like

Design a support-ticket triage agent that classifies and prioritizes

Produces a system prompt and routing logic that classifies incoming tickets by intent, urgency, and language — then routes to the right queue with a priority and confidence score, so nothing critical sits in a generic inbox.

#customer-support#triage
New

Add a tone-guardrail and policy-enforcement layer to a support agent

Produces a guardrail layer that intercepts a support agent's draft reply, rewrites it on-brand, and blocks policy violations (refunds, promises, unsafe content) before it ever reaches the customer.

#customer-support#guardrails
New

Zapier multi-step Zap to route and enrich new leads

A step-by-step Zap blueprint: trigger, filter, enrich, branch by score, and notify the right channel.

New

Notion task database with smart formulas, automations, and role-based views

Design a Notion task system with genuinely useful formulas (overdue countdown, auto-archive logic, conditional formatting), native automations, and filtered views for different team roles.

New

Make.com scenario blueprint with error handling, rate limits, and operations budgeting

Blueprint a Make.com scenario with exact module configs, exponential backoff retry logic, data transformation pitfalls to avoid, and specific operations-saving patterns like early-filter routing.

New
Editor’s pickAutomation & AgentsSeed

Bulletproof tool-calling JSON schema for AI agents

Design strict, self-validating tool schemas with confidence calibration, discriminated unions, and chain-ready contracts — so your agent calls tools reliably instead of hallucinating arguments.

New