--- title: "Building a Triage Agent: Intelligent Request Routing to the Right Specialist" description: "Build a triage agent that classifies incoming requests, routes them to the correct specialist agent with confidence scoring, and handles ambiguous or unclassifiable inputs with fallback strategies." canonical: https://callsphere.ai/blog/building-triage-agent-intelligent-request-routing-specialist category: "Learn Agentic AI" tags: ["Triage Agent", "Request Routing", "Multi-Agent Systems", "OpenAI Agents SDK", "Classification"] author: "CallSphere Team" published: 2026-03-17T00:00:00.000Z updated: 2026-05-06T05:24:12.125Z --- # Building a Triage Agent: Intelligent Request Routing to the Right Specialist > Build a triage agent that classifies incoming requests, routes them to the correct specialist agent with confidence scoring, and handles ambiguous or unclassifiable inputs with fallback strategies. ## The Triage Agent's Role Every multi-agent system needs a front door. The triage agent is that front door — it receives the raw user input, determines what kind of request it is, and routes it to the specialist best equipped to handle it. A well-built triage agent is the difference between a multi-agent system that feels intelligent and one that feels like a phone tree. The triage agent has three core responsibilities: classify the request, select the right specialist, and transfer cleanly. It should not attempt to answer the question itself. Its job is routing, not resolution. ## A Simple Triage Agent Here is a basic triage agent that routes between three specialists: ```mermaid flowchart TD INPUT(["Task input"]) SUPER["Supervisor agent plans plus monitors"] W1["Worker 1 research"] W2["Worker 2 code"] W3["Worker 3 writing"] CRITIC{"Output meets rubric?"} REWORK["Rework or retry path"] SHARED[("Shared scratchpad and memory")] OUT(["Final result"]) INPUT --> SUPER SUPER --> W1 --> CRITIC SUPER --> W2 --> CRITIC SUPER --> W3 --> CRITIC W1 --> SHARED W2 --> SHARED W3 --> SHARED SHARED --> SUPER CRITIC -->|Pass| OUT CRITIC -->|Fail| REWORK --> SUPER style SUPER fill:#4f46e5,stroke:#4338ca,color:#fff style CRITIC fill:#f59e0b,stroke:#d97706,color:#1f2937 style OUT fill:#059669,stroke:#047857,color:#fff style SHARED fill:#ede9fe,stroke:#7c3aed,color:#1e1b4b ``` ```python from agents import Agent, Runner, handoff sales_agent = Agent( name="Sales Agent", instructions="""You handle pricing questions, product comparisons, and purchase decisions. Be helpful and consultative.""", ) support_agent = Agent( name="Support Agent", instructions="""You handle technical issues, bug reports, and troubleshooting. Be patient and methodical.""", ) general_agent = Agent( name="General Agent", instructions="""You handle general inquiries that do not fit sales or support. Answer questions about the company, careers, and partnerships.""", ) triage = Agent( name="Triage Agent", instructions="""You are a request router. Analyze the user's message and hand off to the correct specialist: - Sales Agent: pricing, plans, discounts, purchases, upgrades - Support Agent: bugs, errors, outages, how-to questions, config - General Agent: company info, careers, partnerships, everything else Hand off immediately. Do not answer questions yourself.""", handoffs=[ handoff(sales_agent), handoff(support_agent), handoff(general_agent), ], ) result = Runner.run_sync(triage, "How much does the enterprise plan cost?") print(result.final_output) # Handled by sales_agent ``` This works for straightforward requests. But production traffic is messy. Users say things like "I think there is a bug with the billing page" — is that a support issue or a billing issue? The basic triage agent guesses, and sometimes guesses wrong. ## Adding Structured Classification To make routing more reliable, give the triage agent a classification tool that forces structured reasoning: ```python from agents import Agent, Runner, function_tool, handoff from pydantic import BaseModel from enum import Enum class RequestCategory(str, Enum): SALES = "sales" SUPPORT = "support" GENERAL = "general" @function_tool def classify_request( category: str, confidence: float, reasoning: str, ) -> str: """Classify a user request into a category with confidence score. Args: category: One of 'sales', 'support', or 'general' confidence: Float between 0.0 and 1.0 reasoning: Brief explanation of classification decision """ return f"Category: {category} | Confidence: {confidence} | Reason: {reasoning}" triage = Agent( name="Triage Agent", instructions="""You are a request classifier. For every user message: 1. Call classify_request with your assessment 2. If confidence >= 0.8, hand off to the matching specialist 3. If confidence str: """Detect multiple intents in a single user message. Args: primary_intent: The main request category secondary_intent: A secondary category, or 'none' primary_confidence: Confidence for primary intent secondary_confidence: Confidence for secondary intent """ if secondary_intent != "none": return f"Multi-intent: {primary_intent} ({primary_confidence}) + {secondary_intent} ({secondary_confidence})" return f"Single intent: {primary_intent} ({primary_confidence})" ``` When two intents are detected, the triage agent can address the primary intent first and note the secondary intent for follow-up. ## Fallback Strategies Every triage system needs a fallback for requests that do not fit any category. There are three common fallback strategies: **1. Default specialist.** Route unclassifiable requests to a general-purpose agent that can handle anything, even if less expertly. **2. Human escalation.** If the triage agent cannot classify with sufficient confidence after one clarifying question, escalate to a human operator. **3. Graceful decline.** Acknowledge that the request is outside the system's capabilities and suggest alternative channels. ```python fallback_agent = Agent( name="Fallback Agent", instructions="""You handle requests that could not be confidently routed to a specialist. Do your best to help, but proactively offer to connect the user with a human agent if you cannot fully resolve their issue. Say: 'I want to make sure you get the best help. Would you like me to connect you with a team member who specializes in this?'""", ) triage = Agent( name="Triage Agent", instructions="""Classify and route requests. If confidence is below 0.5 even after asking a clarifying question, hand off to the Fallback Agent.""", handoffs=[ handoff(sales_agent), handoff(support_agent), handoff(general_agent), handoff(fallback_agent), ], ) ``` ## Optimizing the Triage Agent for Speed The triage agent sits in the critical path of every conversation. Every millisecond it spends reasoning is latency the user feels before getting a real answer. Two techniques help: **Use a fast model.** The triage agent performs classification, not complex reasoning. GPT-4o-mini handles this task well and responds significantly faster than GPT-4o. **Minimize instructions.** Keep the triage agent's system prompt short. It does not need background context about the company or detailed product knowledge — it only needs routing criteria. ```python triage = Agent( name="Triage Agent", model="gpt-4o-mini", instructions="Route to sales (pricing/plans), support (bugs/errors), or general (other). Hand off immediately.", handoffs=[handoff(sales_agent), handoff(support_agent), handoff(general_agent)], ) ``` ## FAQ ### Should the triage agent maintain conversation state? No. The triage agent should be stateless. It classifies the current message and routes. It does not need to remember previous conversations. If a returning user sends a follow-up message, the triage agent classifies it fresh and routes accordingly. State management belongs in the specialist agents or in an external session store. ### What if the user changes topics mid-conversation? If the user starts with a billing question and then asks a technical question, the currently active specialist agent should detect that the new request is outside its scope and hand off back to the triage agent, or directly to the appropriate specialist if it has that handoff configured. ### How do I measure triage accuracy? Log every classification decision (category, confidence, reasoning) alongside the final outcome. Periodically review cases where the user had to repeat themselves or where the conversation was transferred multiple times — these indicate triage errors. Target 90%+ first-attempt routing accuracy. --- #TriageAgent #RequestRouting #MultiAgentSystems #OpenAIAgentsSDK #Classification #AgenticAI #LearnAI #AIEngineering --- Source: https://callsphere.ai/blog/building-triage-agent-intelligent-request-routing-specialist