Customer Service Agents

Background

AI powered customer service requires contextual intelligence at scale. This guide explains how to use Arfniia Router to dynamically select the most appropriate LLM for each customer interaction, using episodic learning to continuously improve customer experience across the entire support session.

Why Dynamic LLM Routing Matters

Dynamic LLM routing can help AI powered customer service agents achieves the followings:

Efficiency

FAQs and simple clarification questions can be handled by cost-effective models, delivering quick responses while minimizing operational costs without compromising service quality.

Accuracy

Complex troubleshooting or technical issues can be routed to models with advanced reasoning capabilities or models fine-tuned on specific domains, ensuring accurate, relevant responses tailored to each query’s specific needs.

Personalization

Customer preferences for AI support vary significantly based on their demographic, support tier, and urgency level. Effective LLM routing to appropriate language models helps deliver personalized service that matches each customer’s expectations.

Learning-Enhanced Customer Service

Customer service has delayed‑feedback challenges: success often arrives at the end of a session (issue resolved or not), not per message. Arfniia addresses this with reward shaping and episodic credit assignment to optimize routing decisions.

Reward Shaping

Reward Shaping enhances learning efficiency by providing intermediate feedback signals throughout the customer interactions, instead of only providing feedback at the task’s end.

In the context of customer service, you can assign a small positive reward for each message exchanged to reflect healthy engagement, while keeping a larger terminal reward based on the eventual session outcome.

For example, each message could receive a small reward (e.g., +0.1), while a larger final reward (e.g., +1 or −1) is given based on the end result (resolved or unresolved).

Credit Assignment

Credit Assignment distributes end‑of‑session feedback across the interaction chain to identify which decisions contributed to the outcome.

For instance, if you receive binary feedback at session end (resolved vs unresolved), you can distribute that final signal equally across each turn so the system learns which actions contributed positively or negatively to the outcome.

Implementation Guide

A simple demo of a customer service agent powered by episodic routing.

• Event Loop: handle user messages and session lifecycle
• CustomerServiceAgent: send requests with episodic headers and post feedback

# NOTE: defined in another tab
agent = CustomerServiceAgent("cs-agent")
session = agent.start_session()

while True:
    user_message = get_user_message()
    if user_message is None:
        break

    resp = agent.reply(user_message, session=session)

    # optionally break if your UI detects session end
    if is_session_done():
        break

# end-of-session feedback: True if resolved, False if not
resolved = get_user_feedback()
agent.ack(resolved, session=session)
agent.end_session(session=session)

from openai import OpenAI
import requests
import uuid

base_url = "http://ec2-ip-address:5525/v1"

class CustomerServiceAgent:
    def __init__(self, router_name):
        self.router_name = router_name
        self.client = OpenAI(api_key="anything", base_url=base_url)
        self.responses = []  # current session's response ids
        self.accumulated_feedback = 0.0
        self.feedbacks_api = f"{base_url}/feedbacks/{self.router_name}"

    def start_session(self):
        return {"id": str(uuid.uuid4()), "started": False, "ended": False}

    def end_session(self, session):
        session["ended"] = True
        return session

    def _episode_headers(self, session, is_last=False):
        headers = {"X-Arfniia-Episode-Id": session["id"]}
        if not session["started"]:
            headers["X-Arfniia-Episode-Start"] = "1"
            session["started"] = True
        if is_last or session.get("ended"):
            headers["X-Arfniia-Episode-End"] = "1"
        return headers

    def reply(self, msg, session=None, is_last=False):
        headers = self._episode_headers(session, is_last) if session else {}
        resp = self.client.chat.completions.create(
            messages=[
                {
                    "role": "user",
                    "content": msg,
                }
            ],
            model=self.router_name,
            extra_headers=headers,
        )
        self.responses.append(resp.id)
        # small dense reward for engagement
        self.accumulated_feedback += 0.1
        requests.put(f"{self.feedbacks_api}/sparse/{self.accumulated_feedback}")
        return resp

    def ack(self, resolved, session=None):
        final_reward = 1.0 if resolved else -1.0
        # distribute final reward equally across all turns in the session
        num_responses = len(self.responses)
        if num_responses > 0:
            reward_per_message = final_reward / num_responses
            for resp_id in self.responses:
                requests.put(f"{self.feedbacks_api}/{resp_id}/{reward_per_message}")
        # optional: reset per-session state here
        self.responses = []
        self.accumulated_feedback = 0.0

Key Takeaways

Arfniia Router leverages advanced reinforcement learning to dynamically match interactions with optimal LLMs, delivering powerful results across three key dimensions:

• Efficiency: Optimized LLM selection lowers operational costs.
• Accuracy: Context-aware routing reduces average ticket resolution time.
• Personalization: Tailored responses matches customer preferences.

The system continuously improves through reward shaping and episodic credit assignment, creating a feedback loop that refines routing decisions and enhances overall service quality.