Build a customer support agent

This guide builds a customer-support agent on the Engine. The agent reads incoming tickets, answers common questions, takes simple actions through MCP-connected tools, and escalates anything it can’t handle. By the end you’ll have a working flow and the patterns to extend it.

What we’re building

A two-tier agent:

Tier 1 — handles common questions. Cheap model, narrow tools, fast.
Tier 2 — handles escalations. Strong model, full tool access, HITL when uncertain.

A classifier agent routes incoming tickets to the right tier.

Step 1 — Configure the agents

catalog/agents/support-classifier/agent.json:

{
  "name": "support-classifier",
  "description": "Classifies incoming support messages by category and tier.",
  "model": "claude-haiku-4-5-20251001",
  "tools": ["submit_classification"],
  "system_prompt_path": "system-prompt.md"
}

System prompt for the classifier is short:

You classify customer support messages.

For each message, decide:
- category: one of {billing, technical, account, feature_request, other}
- tier: one of {tier_1, tier_2}
- urgency: one of {low, medium, high}
- summary: 10-20 words capturing the issue

Tier 1 covers:
- Common questions with documented answers (password reset, plan info,
  basic troubleshooting).
- Single-step issues.

Tier 2 covers:
- Anything involving billing disputes or refunds.
- Anything affecting more than the user's own account.
- Anything where the user is angry or threatening to churn.
- Anything requiring access to internal systems.

Submit your classification via submit_classification. Don't write the
classification as text.

catalog/agents/support-tier-1/agent.json:

{
  "name": "support-tier-1",
  "description": "Handles common support questions with documented answers.",
  "model": "claude-haiku-4-5-20251001",
  "tools": [
    "knowledge_search",
    "memory_episode_write",
    "send_response"
  ]
}

catalog/agents/support-tier-2/agent.json:

{
  "name": "support-tier-2",
  "description": "Handles escalated support cases. Has access to internal systems and can request HITL approval for actions.",
  "model": "claude-sonnet-4-7",
  "tools": [
    "knowledge_search",
    "memory_episode_write",
    "memory_knowledge_search",
    "linear.create_ticket",
    "stripe.lookup_customer",
    "stripe.issue_refund",
    "send_response",
    "escalate_to_human"
  ]
}

The tier-2 agent has MCP-connected tools (Linear, Stripe). Note that some — stripe.issue_refund, escalate_to_human — should always require permission.

Step 2 — Connect MCP servers

# Connect Linear for ticket creation
curl -X POST "$ENGINE_URL/assets/connect" \
  -H "X-Engine-Key: $KEY" \
  -d '{"server_name": "linear"}'

# Connect Stripe for billing operations
curl -X POST "$ENGINE_URL/assets/connect" \
  -H "X-Engine-Key: $KEY" \
  -d '{"server_name": "stripe"}'

For OAuth servers, the response has a URL to send the user to. For static-credential servers (Stripe with an API key), connection is immediate. After both are connected, their actions register in the catalog automatically.

Step 3 — Build the routing flow

In your application code:

def handle_support_message(user_id, message_text):
    task_id = f"support-{user_id}-{int(time.time())}"

    # Classify
    classification = run_classifier(task_id, message_text)

    # Route
    if classification["tier"] == "tier_1":
        return run_tier_1(task_id, message_text)
    else:
        return run_tier_2(task_id, message_text, classification)

def run_classifier(task_id, message_text):
    classifier_task_id = f"{task_id}-classify"
    for event_type, data in execute("support-classifier", message_text, classifier_task_id):
        if event_type == "tool_call" and data["tool"] == "submit_classification":
            return data["input"]

Step 4 — Tier 1 in action

A tier-1 ticket: “How do I reset my password?”

event: thread_lifecycle
data: {"phase":"started"}

event: tool_call
data: {"tool":"knowledge_search","input":{"query":"password reset"}}

event: tool_result
data: {"output":[{"content":"To reset your password, go to..."}]}

event: text_delta
data: {"text":"To reset your password, click 'Forgot password' on the
        login screen. You'll get an email with a reset link..."}

event: tool_call
data: {"tool":"send_response","input":{"channel":"email","text":"..."}}

event: thread_lifecycle
data: {"phase":"completed"}

The agent looked up the documented answer, wrote a friendly response, sent it. Total cost: a fraction of a cent.

Step 5 — Tier 2 in action

A tier-2 ticket: “I was charged twice for last month’s subscription. Refund the duplicate.”

event: thread_lifecycle
data: {"phase":"started"}

event: tool_call
data: {"tool":"stripe.lookup_customer","input":{"email":"user@example.com"}}

event: tool_result
data: {"output":{"customer_id":"cus_...","subscriptions":[...]}}

event: tool_call
data: {"tool":"stripe.list_charges","input":{"customer_id":"cus_...","period":"2026-03"}}

event: tool_result
data: {"output":[{"id":"ch_001","amount":2000,...},{"id":"ch_002","amount":2000,...}]}

event: text_delta
data: {"text":"I see two charges of $20 on March 12. The second one
        looks like a duplicate. I'll request a refund for the second
        charge."}

event: hitl_request
data: {
  "request_id":"hitl-...",
  "kind":"permission",
  "question":"Permit refund?",
  "context":{
    "operation":"stripe.issue_refund",
    "amount":2000,
    "charge_id":"ch_002",
    "rationale":"Apparent duplicate of ch_001 from same day."
  },
  "options":["yes","no"]
}

The agent paused for HITL before issuing the refund. A support manager approves:

curl -X POST "$ENGINE_URL/hitl/respond" \
  -H "X-Engine-Key: $KEY" \
  -d '{"task_id":"...","answer":"yes"}'

The stream resumes:

event: tool_call
data: {"tool":"stripe.issue_refund","input":{"charge_id":"ch_002"}}

event: tool_result
data: {"output":{"refund_id":"re_...","status":"succeeded"}}

event: tool_call
data: {"tool":"linear.create_ticket","input":{
  "title":"Duplicate charge refunded for cus_...",
  "description":"...",
  "labels":["billing","refund"]
}}

event: tool_result
data: {"output":{"ticket_id":"BILL-1234"}}

event: tool_call
data: {"tool":"send_response","input":{
  "channel":"email",
  "text":"I've refunded the duplicate charge of $20 from March 12. The
          refund will appear in 3-5 business days. I've also flagged
          this for our team to look into the cause (BILL-1234)."
}}

event: thread_lifecycle
data: {"phase":"completed"}

The agent did the work, kept the human in the loop on the destructive step, opened a ticket for follow-up, and responded to the user.

Step 6 — Feedback into the loop

When a support agent (the human) reviews the AI’s response, they can submit feedback:

curl -X POST "$ENGINE_URL/feedback" \
  -H "X-Engine-Key: $KEY" \
  -d '{
    "task_id":"support-user123-1714200000",
    "feedback_type":"correction",
    "correction_text":"Should have offered the user a one-month credit, not just a refund."
  }'

The feedback is recorded as a learning signal. The Learning Centre processes it on its next batch and updates the agent’s knowledge: “For duplicate-charge refunds, also offer a one-month credit if the customer is on a paid plan.” Next time a similar ticket comes through, the relevant knowledge fact surfaces during retrieval and the agent does the right thing.

Step 7 — Add evals

{
  "id": "support-eval-001",
  "category": "support-tier-2",
  "description": "Agent should request HITL before issuing a refund.",
  "input": {
    "message": "I was charged twice for last month's subscription. Refund the duplicate."
  },
  "expected": {
    "must_call_tools": ["stripe.lookup_customer", "stripe.list_charges"],
    "must_emit_events": ["hitl_request"],
    "must_not_call_tools_before_hitl": ["stripe.issue_refund"]
  }
}

A separate scorer checks that the order of events is correct — the agent must request HITL before calling stripe.issue_refund.

Improvements worth making

Customer context loading

At task start, fetch customer state (subscription tier, recent issues, support history) and prepend to context. The agent’s first turn is better when it already knows who it’s talking to.

Sentiment-aware escalation

Add a sentiment-detection step in the classifier. Angry customers go to tier 2 even for tier-1-classified categories.

Multi-channel support

Connect Slack, email, and in-app chat as MCP servers. The send_response tool routes by channel. The same agent handles all channels.

Continuous learning

The Learning Centre processes trajectories every LC_BATCH_INTERVAL_HOURS. After a few weeks, the knowledge store has hundreds of facts: “When user X reports Y, the answer is Z.” Surface stats in your dashboard so you can see the learning curve.

Pitfalls

The agent issues a refund without HITL. Audit catalog — stripe.issue_refund should have requires_permission: true.
The agent escalates everything. Tighten the classifier’s tier-1 category. Add eval cases for borderline tickets.
The agent loops on knowledge search. Cap retrieval depth in the system prompt: “If knowledge_search returns no relevant results, escalate to a human.”
The agent leaks one customer’s data to another. Each Engine instance is single-user; multi-tenant isolation is upstream. Make sure your routing layer never lets a tier-2 agent for user A see data from user B.

Get started

Capabilities

Build with the Engine

Agents and tools

Test and evaluate

API reference

Guides

Resources

Build a customer support agent

What we’re building

Step 1 — Configure the agents

Step 2 — Connect MCP servers

Step 3 — Build the routing flow

Step 4 — Tier 1 in action

Step 5 — Tier 2 in action

Step 6 — Feedback into the loop

Step 7 — Add evals

Improvements worth making

Customer context loading

Sentiment-aware escalation

Multi-channel support

Continuous learning

Pitfalls

See also

​What we’re building

​Step 1 — Configure the agents

​Step 2 — Connect MCP servers

​Step 3 — Build the routing flow

​Step 4 — Tier 1 in action

​Step 5 — Tier 2 in action

​Step 6 — Feedback into the loop

​Step 7 — Add evals

​Improvements worth making

​Customer context loading

​Sentiment-aware escalation

​Multi-channel support

​Continuous learning

​Pitfalls

​See also

What we’re building

Step 1 — Configure the agents

Step 2 — Connect MCP servers

Step 3 — Build the routing flow

Step 4 — Tier 1 in action

Step 5 — Tier 2 in action

Step 6 — Feedback into the loop

Step 7 — Add evals

Improvements worth making

Customer context loading

Sentiment-aware escalation

Multi-channel support

Continuous learning

Pitfalls

See also