Which Prompt Works Best for AI Support Agents Using Decision 3.0 and Clef-omni?

Introduction: Why new decision-making models are revolutionizing customer support

By 2026, AI customer support agents have evolved far beyond simple rule-based bots. Platforms like vLLM Semantic Router with Decision 3.0 and Cloudflare’s multimodal Clef-omni are redefining what an agent can understand and resolve in real time. But the key question for professionals is:which prompt should you use to harness these cutting-edge modelsand deliver a truly conversational user experience?

How Decision 3.0 and Clef-omni are transforming modern agents

Decision 3.0: Semantic routing and choice evaluation

Decision 3.0 offers five open-source decision models (ranging from 0.8B to 27B parameters) that excel at classifying user intentions, answering multiple-choice questions, and providing relevance scores. For agents, this means being able toroutea request to specialized sub-chains with a single API call, reducing latency and improving accuracy.

Clef-omni: Unified multimodal evaluation

Clef-omni (Qwen3-Omni-30B-A3B) handles text, images, audio, and video in a single Apache-2.0 model. An agent that integrates Clef-omni can analyze screenshots of problems, transcribe customer voice notes, and even extract information from short tutorial videos, all while maintaining consistent context.

Prompt engineering for a 2026 AI customer service agent

Designing an effective prompt today means combining routing semantics with multimodal request capabilities. The prompt must guide the agent on three levels:

• Intent identification
• Channel selection
• Action

Building the prompt structure

A modular prompt keeps the system flexible and testable. Below is a template you can copy and adapt to your LangChain or auto-gen stack.

Code example: Agent with LangChain and prompt template

from langchain.prompts import ChatPromptTemplate, HumanMessagePromptTemplate
from langchain.schema import HumanMessage
from vllm_semantic_router import DecisionRouter
from clef_omni_client import ClefOmniAnalyzer

# Initialize models
router = DecisionRouter("decision-3.0")
multimodal = ClefOmniAnalyzer("clef-omni")

# Agent prompt template
prompt = ChatPromptTemplate.from_messages([
    ("system", """
    You are a customer service AI agent.
    Use Decision 3.0 to classify the user's intent.
    If multimodal analysis is needed, send the content to Clef-omni.
    Respond concisely and action-oriented.
    """),
    ("human", """
    Intent: {intent}
    Channel: {channel}
    User message: {message}
    Attachments: {attachments}
    """)
])

# Routing function
def route_query(text: str) -> str:
    intents = router.classify(text)
    return intents[0].name  # e.g. "problem" | "info" | "feedback" | "escalation"

# Multimodal analysis function
def analyze_multimodal(attachments) -> dict:
    return multimodal.score(attachments)

# Run the chain
chain = prompt | llm
user_input = "My order hasn't arrived and I have a video of the sealed package."
intent = route_query(user_input)
channel = "multimodal" if any(a.endswith('.mp4') for a in attachments) else "text"
result = chain.invoke({
    "intent": intent,
    "channel": channel,
    "message": user_input,
    "attachments": attachments
})
print(result)

Practical example: End-to-end implementation

Environment setup

• Install core dependencies:langchain-core,vllm-semantic-router,clef-omni-client,fastapifor the service.
• Set up API keys for vLLM and Cloudflare; enable caching for routing calls.
• Define aKnowledge Graphfor the product (e.g., using Neo4j) so the agent can extract specific details.

Creating the prompt and chains

Once the environment is set up, the prompt becomes a reusableasset. Create two separate chains:

1. Aclassification chainthat uses Decision 3.0 to quickly label requests.
2. Amultimodal processing chainthat sends images/video/audio to Clef-omni for information extraction.

Connect them via anorchestratorthat decides whether to respond automatically or escalate to a human agent.

Best practices and tips for optimal results

• Test routingwith a dataset of over 1,000 real messages to cover edge-cases.
• Use cachingfor Decision 3.0 calls; the models are static for most intent classes.
• Monitor Clef-omni scores
• Implement a feedback loopthat sends correct system decisions to Sakana AI’s LLM revision system for continuous improvement.

Future outlook: Integration with LLM revision systems

Sakana AI has shown that a multi-agent review system can capture over 73% of core-claim errors. Integrating that review system with your customer service agent creates a cycle ofauto-learningwhere every interaction corrects the routing model and the prompt, keeping the agent at the cutting edge.

Key takeaways

The right prompt for a 2026 AI customer service agent leverages the strengths of Decision 3.0 (semantic routing) and Clef-omni (multimodal analysis). Design a modular prompt, test it with real traffic, and connect it to an LLM-based revision system for continuous improvement. By following the steps above, you’ll get an agent that responds quickly, handles screenshots, videos, and voice notes, and reduces escalation times, improving customer satisfaction.

**IMPORTANT:** We are in 2026. All temporal references must be current. Do not mention past years such as 2024, 2023, etc. The content must be fresh and relevant to the present. **Current context (October 2026):** Here are some recent trends and news to inspire you: - OrcaRouter Releases OrcaCyber Zero 1.5 Cybersecurity Model With 1M Context: OrcaRouter released OrcaCyber Zero 1.5, a gated cybersecurity model with a 1M-token context window. It reports 100% on Cybench and 95.8% on an evaluable benchmark... [2026-10-11] - TII Releases Falcon-ASR: 1.6B Arabic Speech Model Hits 20.92% WER: TII has released Falcon-ASR, a 1.6B speech recognition model for Arabic with an Emirati focus. It reports 20.92% average WER on 6 Arabic test sets... [2026-10-12] - Sakana AI’s LLM Peer Review System Catches 73% of Core-Claim Errors: Sakana AI’s TMLR paper introduces Multi-Layered Review, a 3-agent Claude-based reviewer, and a 1,164-error Contradiction Benchmark. MLR caught 73.43% of core-claim errors... [2026-10-11] Use this current information as inspiration to create an original and relevant 2026 prompt.