July 27th, 2026

Customer Support Agent

Support

Docs

Escalation

Policy

6 min read

The problem

Support volume grows faster than headcount. Teams want an agent that can answer common questions from product docs — without inventing policies or approving refunds it should not approve. A Customer Support Agent is a grounded reply drafter with an escalation path, not a freeform chatbot.

Success looks like: a draft customer reply, citations to the docs used, and an explicit escalate flag when the case is billing-sensitive, ambiguous, or outside the knowledge base.

Paste into Claude, Codex, Cursor, or another coding agent to scaffold this harness.

# Context: Vidbyte SDK

Vidbyte SDK is a Python package (import as `vidbyte`) for building agent workflows yourself.

Use it when you want to own the agent loop: system prompts, model/provider choice, tools, managed context, middleware, pipelines, tracing, evals, and multi-agent composition.

Core mental model:
1. Create an Agent or BaseAgent.
2. Attach a system prompt, provider/model, optional tools, context, middleware, and runtime choices.
3. Call run() or arun().
4. Let the SDK handle message assembly, tool schemas/calls, iteration limits, and optional pipelines.

Public package boundary: reusable local agent infrastructure. It does not ship private Vidbyte learning models or proprietary platform internals.

GitHub: https://github.com/cerredz/Vidbyte-SDK
Install: pip install vidbyte-sdk
Verify: python -c "from vidbyte import Agent, BaseAgent, tool; print(Agent, BaseAgent, callable(tool))"

# Install

Primary:
```bash
pip install vidbyte-sdk
```

From a checkout (pre-release / local development):
```bash
git clone https://github.com/cerredz/Vidbyte-SDK.git
cd Vidbyte-SDK
pip install -e .
```

# Task: Build a Customer Support Agent

Build a Vidbyte SDK support agent that answers customer questions using product documentation tools.

Scenario: A user emails "I was charged twice this month. How do I get a refund?" The agent should look up the relevant docs, answer helpfully, and escalate when policy requires a human.

The harness should:
- Accept a support ticket (subject + body)
- Use tools to look up FAQ / policy / product docs
- Answer only from retrieved material when possible
- Escalate billing, legal, or safety issues instead of guessing
- Return a reply draft plus a short internal note (sources + confidence)

Constraints:
- Use Agent + tools for doc lookup
- Never invent refunds, credits, or account state
- Escalate when docs do not cover the case
- Keep the customer-facing tone calm and clear

# Deliverable

Produce a small, runnable Python harness that uses the Vidbyte SDK (`vidbyte` package) to implement this agent. Prefer clear modules, a main entrypoint, and short comments that explain the control flow. Do not invent private Vidbyte backend APIs.

Build it step by step

Create the support agent

Write a system prompt that prioritizes helpfulness and honesty. The agent should never invent account-specific facts.

from vidbyte import Agent

agent = Agent(
    name="support-agent",
    system_prompt=(
        "You draft customer support replies. "
        "Use lookup tools for product docs. "
        "If the answer is not in the docs, say so and escalate. "
        "Never invent refunds, credits, or account status."
    ),
    provider="openai",
    model_name="gpt-4.1",
)

Add a documentation lookup tool

Start with a simple FAQ map or local markdown search. Replace the stub with your real help center later.

from vidbyte import tool

DOCS = {
    "billing": "Refunds take 5–10 business days after approval. Double charges need human review.",
    "login": "Reset password from Settings → Security. Clear cookies if the page loops.",
}

@tool
def lookup_docs(topic: str) -> str:
    """Look up product documentation by topic keyword."""
    key = topic.lower()
    for name, body in DOCS.items():
        if name in key or key in name:
            return body
    return "No matching doc found."

Encode escalation policy in the prompt

Policy belongs in the system prompt and tool results. The agent should return a structured decision: reply vs escalate.

system_prompt = """
Draft a support reply using lookup_docs.

Always output:
1) customer_reply — the message to send
2) sources — docs topics used
3) escalate — true/false with reason

Escalate when: billing disputes, legal threats, safety issues,
or docs do not cover the question.
"""

agent = Agent(
    name="support-agent",
    system_prompt=system_prompt,
    provider="openai",
    model_name="gpt-4.1",
    tools=[lookup_docs],
)

Run a sample ticket

Feed a realistic ticket body. Inspect whether the agent looked up docs and whether escalate is set correctly for billing.

ticket = (
    "Subject: Double charge\n"
    "Body: I was charged twice this month. How do I get a refund?"
)
reply = await agent.arun(ticket)
print(reply.content)

Harden with limits and review

Production support agents need iteration limits, logging, and human review queues. The SDK loop is the core; your app owns ticket state and audit trails.

# Next steps in your app (outside the minimal SDK demo):
# - store ticket id + model output
# - route escalate=true to a human queue
# - log which docs topics were retrieved
# - add middleware for rate limits / audit if using SDK middleware