AI System Prompt for a Support Agent That Resolves Tickets via Intercom's MCP Server

0 views Updated Oct 11, 2026

This AI system prompt for an Intercom support agent defines how a model should behave when it's connected to Intercom's MCP server and can actually read conversations, pull customer data, and update or close tickets instead of just drafting replies for a human to copy. It's written for support leads and ops teams building an AI agent that sits inside Intercom's workflow rather than a side chatbot that never touches the real ticket.

The core problem with giving a model direct MCP access to a live support inbox is scope: without firm boundaries, it will happily close tickets it shouldn't, promise refunds it can't authorize, or escalate every edge case to a human and defeat the point of automating anything. This system prompt handles that by defining exactly which Intercom actions the agent may take on its own (tagging, drafting replies, looking up order status) versus which require a human to approve first (refunds, account deletions, anything the customer is clearly upset about).

Because this is a system prompt rather than a one-off task prompt, it's meant to persist across an entire support session, which is what the existing Slack MCP agent system prompt on this site does for internal team messages - this one applies the same discipline to a customer-facing Intercom agent instead.

Prompt template

prompt-template

327 tokens

SYSTEM PROMPT ROLE: You are a customer support agent operating inside Intercom with MCP access to live conversations, customer profiles, and ticket data. SCOPE OF AUTONOMOUS ACTIONS (you may perform these without approval): - [LIST ACTIONS, e.g. tag conversation, look up order status, reply to FAQ-type questions] SCOPE REQUIRING HUMAN APPROVAL (draft only, do not execute): - [LIST ACTIONS, e.g. issue refund, delete account, change billing plan] TONE: [DESCRIBE TONE WITH A CONCRETE EXAMPLE REPLY, e.g. "Hey [name], thanks for flagging this - here's what's going on and how we'll fix it."] ESCALATION RULES: - Escalate to a human if: [LIST TRIGGERS, e.g. customer expresses anger, request involves money over $X, same issue reported 3+ times] - When escalating, summarize the issue and your attempted resolution in [NUMBER] sentences for the human agent CONSTRAINTS: - Never state something as fact if the Intercom data is missing or contradictory - say so and ask the customer or flag for review - State your confidence level before closing any ticket - Do not promise outcomes you cannot execute through your available actions OUTPUT FORMAT: For each conversation, respond with: Action taken: [action or "escalated"] Reply sent: [exact customer-facing text, if any] Confidence: [high/medium/low] Notes for human reviewer: [only if escalated or confidence is not high]

Want it sharper? Optimize this prompt with Prompt Optimizer, check it with the Prompt Debugger or shorten it with the Token Optimizer.

Example input

example-input

75 tokens

Incoming Intercom conversation: "I was charged twice for my subscription this month and I need this fixed today, this is the second time this has happened." Customer profile (via MCP): Sarah Chen, Pro plan, $49/mo, prior duplicate-charge ticket closed 6 weeks ago. Scope: autonomous actions = tag conversation, look up billing history; approval required = refunds.

When to use it

You're deploying an AI agent inside Intercom that has MCP access to real conversations and ticket data
You need the agent to triage and tag incoming tickets automatically but escalate anything involving money or account deletion
Your support team wants consistent tone and escalation rules across every AI-handled conversation, not just the first one
You're piloting AI-assisted support and need a clear boundary between what the agent can resolve alone and what it must hand off

Best practices

List the exact Intercom actions the agent is allowed to take (tag, snooze, reply, close) separately from actions that always require human approval
Give the agent a specific tone example, not just an adjective like "friendly," since models interpret tone words inconsistently
Require the agent to state its confidence or uncertainty before closing a ticket, so low-confidence resolutions get flagged instead of silently closed
Test the system prompt against a handful of your actual worst-case tickets (angry customers, ambiguous bugs) before deploying it live

Common mistakes

Giving the agent blanket permission to "resolve tickets" without separating safe actions from ones that need human sign-off
Omitting a clear escalation trigger, so the agent either escalates everything or nothing
Writing the tone instruction as a single adjective instead of a concrete example reply
Forgetting to specify what the agent should do when Intercom data is missing or contradictory, which leads to confidently wrong replies

FAQs

Can an AI agent actually resolve Intercom tickets on its own, or only draft replies?

With MCP access and the right permissions, the agent can take real actions like tagging, replying, and closing tickets - but which actions it's allowed to take without approval is something you define explicitly in the system prompt, not something the model decides on its own.

How do I stop the AI agent from issuing refunds without approval?

List refunds and other sensitive actions under a "requires human approval" section in the system prompt, and instruct the agent to draft the action rather than execute it whenever a ticket falls into that category.

What's the difference between this and a regular customer support chatbot?

A typical chatbot only talks to the customer. An MCP-connected agent can read and write directly to your actual support platform - looking up real order data and updating real tickets - which is why the permission boundaries matter more here.

What tool pairs well with this system prompt?

Prompt Debugger - run your finished system prompt through it to catch vague escalation triggers or missing edge cases before you let the agent touch live customer conversations.

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