In this tutorial, we build an advanced Griptape-based customer support automation system that combines deterministic tooling with agentic reasoning to process real-world support tickets end-to-end. We design custom tools to sanitize sensitive information, categorize issues, assign priorities with clear SLA targets, and generate structured escalation payloads, all before involving the language model. We then use a 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 required Griptape dependencies and supporting libraries. We securely load the OpenAI API key using Colab secrets or a runtime prompt to keep credentials out of the code. We ensure the notebook is ready for agent execution before any logic is defined.
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 core operational logic by defining a custom Griptape tool inside a standalone Python module. We encode deterministic rules for PII redaction, ticket categorization, priority scoring, SLA assignment, and the generation of escalation payloads. We then import and instantiate this tool so 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 acts 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 consistently test and demonstrate the full 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 Griptape Agent with the custom tool and a prompt driver to enable controlled reasoning. We define a deterministic processing function that chains tool calls before invoking the agent, ensuring all sensitive handling and classification are completed first. We then ask the agent to generate customer responses and internal notes based solely on tool outputs.
results = [run_ticket(t) for t in TICKETS]
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 across all tickets and collect the structured results. We print escalation payloads and agent-generated Markdown outputs to verify correctness and clarity. We use this final step to validate that the workflow runs end-to-end without hidden dependencies or retrieval logic.
In conclusion, we demonstrated how Griptape can be used to orchestrate complex operational workflows in which logic, policy, and AI reasoning coexist cleanly. We relied on deterministic tools for classification, risk handling, and escalation, using the agent only where natural-language judgment is required to keep the system reliable and explainable. This pattern illustrates how we can scale AI-assisted operations safely, integrate them into existing support systems, and maintain strict control over behavior, outputs, and service guarantees using Griptape’s core abstractions.
Check out the Full Codes here. Also, feel free to follow us on Twitter and don’t forget to join our 100k+ ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well.
