In this tutorial, we create an advance grip tape-based customer support automation system that combines deterministic tooling with agentic logic to process real-world support tickets from start to finish. We design custom tools to clean sensitive information, classify issues, assign priorities with clear SLA targets, and generate structured escalation payloads before incorporating the language model. We then use the GripTape Agent to synthesize these tool outputs into professional customer replies and internal support notes, demonstrating how GripTape enables controlled, auditable, and production-ready AI workflows without relying on retrieval or external knowledge bases.
!pip -q install "griptape(all)" rich schema pandas
import os, re, json
from getpass import getpass
try:
from google.colab import userdata
os.environ("OPENAI_API_KEY") = userdata.get("OPENAI_API_KEY")
except Exception:
pass
if not os.environ.get("OPENAI_API_KEY"):
os.environ("OPENAI_API_KEY") = getpass("Enter OPENAI_API_KEY: ")We set up the execution environment by installing all the necessary GripTap dependencies and supporting libraries. We load OpenAI API keys securely using Colab Secrets or runtime prompts to keep credentials away from code. We ensure that the Notebook Agent is ready for execution before defining any logic.
tool_code = r'''
import re, json
from schema import Schema, Literal, Optional
from griptape.tools import BaseTool
from griptape.utils.decorators import activity
from griptape.artifacts import TextArtifact, ErrorArtifact
def _redact(text: str) -> str:
text = re.sub(r"(\\w\\.-)+@(\\w\\.-)+\\.\\w+", "(REDACTED_EMAIL)", text)
text = re.sub(r"\\+?\\d(\\d\\-\\s\\(\\)){7,}\\d", "(REDACTED_PHONE)", text)
text = re.sub(r"\\b(\\d{4}(\\s-)?){3}\\d{4}\\b", "(REDACTED_CARD)", text)
return text
class TicketOpsTool(BaseTool):
@activity(config={"description": "Redact PII", "schema": Schema({Literal("text"): str})})
def redact_pii(self, params: dict):
try:
return TextArtifact(_redact(params("values")("text")))
except Exception as e:
return ErrorArtifact(str(e))
@activity(config={"description": "Categorize ticket", "schema": Schema({Literal("text"): str})})
def categorize(self, params: dict):
try:
t = params("values")("text").lower()
if any(k in t for k in ("charged", "refund", "invoice", "billing", "payment")):
cat = "billing"
elif any(k in t for k in ("crash", "error", "bug", "export", "0x")):
cat = "bug"
elif any(k in t for k in ("locked", "password", "login attempts", "unauthorized", "security")):
cat = "security"
elif any(k in t for k in ("account", "profile", "access")):
cat = "account"
else:
cat = "other"
return TextArtifact(cat)
except Exception as e:
return ErrorArtifact(str(e))
@activity(config={"description": "Priority and SLA", "schema": Schema({Literal("category"): str, Literal("text"): str, Optional(Literal("channel"), default="web"): str})})
def priority_and_sla(self, params: dict):
try:
cat = params("values")("category").lower()
t = params("values")("text").lower()
channel = params("values").get("channel", "web")
if cat == "security" or "urgent" in t or "asap" in t:
p, sla = 1, "15 minutes"
elif cat in ("billing", "account"):
p, sla = 2, "2 hours"
elif cat == "bug":
p, sla = 3, "1 business day"
else:
p, sla = 4, "3 business days"
if channel == "chat" and p > 1:
p = max(2, p - 1)
return TextArtifact(json.dumps({"priority": p, "sla_target": sla}))
except Exception as e:
return ErrorArtifact(str(e))
@activity(config={"description": "Escalation payload", "schema": Schema({Literal("ticket_id"): str, Literal("customer"): str, Literal("category"): str, Literal("priority"): int, Literal("sanitized_text"): str})})
def build_escalation_json(self, params: dict):
try:
v = params("values")
payload = {
"summary": f"({v('category').upper()})(P{v('priority')}) Ticket {v('ticket_id')} - {v('customer')}",
"labels": (v("category"), f"p{v('priority')}"),
"description": v("sanitized_text"),
"customer": v("customer"),
"source_ticket": v("ticket_id")
}
return TextArtifact(json.dumps(payload, indent=2))
except Exception as e:
return ErrorArtifact(str(e))
'''
with open("/content/ticket_tools.py", "w", encoding="utf-8") as f:
f.write(tool_code)
import importlib, sys
sys.path.append("/content")
ticket_tools = importlib.import_module("ticket_tools")
TicketOpsTool = ticket_tools.TicketOpsTool
tool = TicketOpsTool()We implement the main operational logic by defining a custom GripTap tool inside a standalone Python module. We encode deterministic rules for PII reduction, ticket classification, priority scoring, SLA assignment, and generation of escalation payloads. We then import and instantiate this device so that it can be safely inspected and used by GripTape.
TICKETS = (
{"ticket_id": "TCK-1001", "customer": "Leila", "text": "I was charged twice on my card ending 4432. Please refund ASAP. email: (email protected)", "channel": "email", "created_at": "2026-02-01T10:14:00Z"},
{"ticket_id": "TCK-1002", "customer": "Rohan", "text": "App crashes every time I try to export. Screenshot shows error code 0x7f. My phone: +1 514-555-0188", "channel": "chat", "created_at": "2026-02-01T10:20:00Z"},
{"ticket_id": "TCK-1003", "customer": "Mina", "text": "Need invoice for January. Also update billing address to 21 King St, Montreal.", "channel": "email", "created_at": "2026-02-01T10:33:00Z"},
{"ticket_id": "TCK-1004", "customer": "Sam", "text": "My account got locked after password reset. I’m seeing login attempts I don't recognize. Please help urgently.", "channel": "web", "created_at": "2026-02-01T10:45:00Z"}
)
We create a realistic stream of customer support tickets that serves as our input workload. We structure each ticket with metadata such as channel, timestamp, and free-form text to reflect real operational data. We use this dataset to continuously test and perform the entire pipeline.
from griptape.structures import Agent
from griptape.drivers.prompt.openai import OpenAiChatPromptDriver
prompt_driver = OpenAiChatPromptDriver(model="gpt-4.1")
agent = Agent(prompt_driver=prompt_driver, tools=(tool))
def run_ticket(ticket: dict) -> dict:
sanitized = tool.redact_pii({"values": {"text": ticket("text")}}).to_text()
category = tool.categorize({"values": {"text": sanitized}}).to_text().strip()
pr_sla = json.loads(tool.priority_and_sla({"values": {"category": category, "text": sanitized, "channel": ticket("channel")}}).to_text())
escalation = tool.build_escalation_json({"values": {"ticket_id": ticket("ticket_id"), "customer": ticket("customer"), "category": category, "priority": int(pr_sla("priority")), "sanitized_text": sanitized}}).to_text()
prompt = f"""
You are a senior support lead. Produce:
1) A customer-facing reply
2) Internal notes
3) Escalation decision
Ticket:
- id: {ticket('ticket_id')}
- customer: {ticket('customer')}
- channel: {ticket('channel')}
- category: {category}
- priority: {pr_sla('priority')}
- SLA target: {pr_sla('sla_target')}
- sanitized_text: {sanitized}
Output in Markdown.
"""
out = agent.run(prompt).to_text()
return {"ticket_id": ticket("ticket_id"), "category": category, "priority": pr_sla("priority"), "sla_target": pr_sla("sla_target"), "escalation_payload_json": escalation, "agent_output_markdown": out}We initialize a GripTap agent with custom tools and a prompt driver to enable controlled logic. We define a deterministic processing function that chains tool calls before invoking the agent, ensuring that all sensitivity handling and classification is completed first. We then ask the agent to prepare customer responses and internal notes based only on the tool output.
results = (run_ticket
for r in results:
print("\n" + "=" * 88)
print(f"{r('ticket_id')} | category={r('category')} | P{r('priority')} | SLA={r('sla_target')}")
print(r("escalation_payload_json"))
print(r("agent_output_markdown"))We execute the pipeline on all tickets and collect structured results. We print the escalation payload and agent-generated Markdown output to verify correctness and clarity. We use this last step to verify that the workflow runs end-to-end without hidden dependencies or recovery logic.
In conclusion, we demonstrated how GripTape can be used to orchestrate complex operational workflows in which logic, policy, and AI logic clearly co-exist. We relied on deterministic tools for classification, risk management, and escalation, using agents only where natural-language judgment was required to keep the system reliable and explainable. This pattern demonstrates how we can safely scale AI-assisted operations, integrate them into existing support systems, and maintain tight control over behavior, output, and service guarantees using GripTape’s core abstractions.
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