How to Build a Productivity-Grade Automated Customer Support Pipeline with Griptape Using Deterministic Tools and Agentic Thinking
In this lesson, we build advanced Griptape-a customer support automation system that integrates decision-making tools and agency thinking to process real-world support tickets in the end. We build custom tools to clean sensitive information, categorize issues, prioritize with clear SLA targets, and generate structured incremental payments, all before engaging the language model. We then use Griptape Agent to synthesize these tool outputs into professional customer feedback and internal support notes, demonstrating how Griptape enables a controlled, readable, and production-ready AI workflow without relying on retrieval or external knowledge support.
!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: ")Set up the workstation by installing all the necessary Griptape requirements and support libraries. We securely load the OpenAI API key using Colab secrets or a runtime command to keep credentials out of the code. We ensure that the notebook is ready for the agent to use 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 functionality by defining a custom Griptape tool within a standalone Python module. We code prescriptive rules for PII redirection, ticket classification, priority points, SLA allocation, and incremental payload generation. We then import and certify this tool for safe testing and use 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 an original stream of customer support tickets that serve as our input load. We create each ticket with metadata such as channel, timestamp, and free text to display the actual performance data. We use this dataset to regularly test and show a 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 implement the Griptape Agent with a custom tool and fast driver to enable controlled logic. We define a deterministic processing task that includes tool calls before invoking the agent, ensuring that critical handling and configuration are completed first. We then ask the agent to generate customer responses and internal notes based solely on the output of the tools.
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 perform a pipeline on all tickets and collect organized results. We print incremental payments and Markdown results generated by the agent to ensure fairness and clarity. We use this last step to ensure that the workflow works end-to-end without hidden dependencies or retrieval comprehensions.
In conclusion, we have shown how Griptape can be used to organize complex workflows where logic, policy, and AI reasoning come together cleanly. We rely on deterministic tools for classification, risk management, and scaling, using agents only where natural language judgment is needed to keep the system reliable and interpretable. This pattern shows 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 key shortcuts.
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