Exploring Routing Design Pattern: Agentic AI Systems

Why Routing Matters

When I first began building multi-model AI systems, I quickly discovered that routing was not merely a technical detail, it was the backbone of the system's efficiency and scalability. Routing dictates how queries are processed, balancing cost, accuracy, and latency to optimize the system's "thinking budget." Without effective routing, you risk overloading expensive models with trivial tasks or missing the mark with lightweight models that lack the capability for complex reasoning. Through trial and error, experimenting with various routing mechanisms, and deploying these architectures in production, I learned valuable lessons about what works, what doesn’t, and where routing can truly excel. In this post, I aim to share those insights, along with the technical strategies that guided my journey.

The Three Primary Routing Mechanisms

One of my key realizations was that routing is not a one-size-fits-all solution. Different tasks require tailored approaches, and often, the best results come from combining multiple mechanisms. Here’s a breakdown of the three routing mechanisms I’ve worked with:

Rule-Based Routing (Tier 1): The Deterministic Workhorse

Rule-based routing is the simplest and fastest mechanism. It relies on deterministic logic such as keywords, regular expressions, or hardcoded rules to direct queries. This tier is invaluable for routine tasks or compliance-critical workflows. Here’s an example from my implementation:

def rule_based_router(query):
    # Simple keyword-based routing
    if "weather" in query.lower():
        return "weather_service"
    elif "math" in query.lower():
        return "math_tutor"
    else:
        return "fallback_service"

This approach handled about 60% of the traffic in my tiered system. It was nearly instantaneous (microseconds) and incurred no operational cost. However, it has limitations: it’s rigid and struggles with synonyms, language variations, or ambiguous queries.

Semantic/Embedding-Based Routing (Tier 2): Mini RAG Compass

Semantic routing is a step up in complexity. Instead of relying on exact matches, it uses vector embeddings to compare the semantic meaning of queries against predefined intents. This enables the system to handle synonyms and language variations more effectively. Embedding-based routing transformed how I processed user queries. Here’s how I implemented it using sentence embeddings:

from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity

model = SentenceTransformer('all-MiniLM-L6-v2')

def semantic_router(query, intents):
    query_embedding = model.encode([query])
    intent_embeddings = model.encode(intents)
    similarities = cosine_similarity(query_embedding, intent_embeddings)
    best_match_idx = similarities.argmax()
    return intents[best_match_idx]

With latency between 50-150ms, it was slower than rule-based routing but still fast enough for most use cases. More importantly, it could route queries like “forecast tomorrow” to the weather service even if “weather” wasn’t explicitly mentioned. Semantic routing became my go-to for tasks requiring nuanced understanding.

LLM-Based Routing (Tier 3): The Universal Translator

The final tier is the most flexible and capable, large language models (LLMs) that reason about intent. These models can handle ambiguity, multi-turn context, and edge cases that other mechanisms might fail to address. However, they are both slow and expensive. Here’s an example of how I integrated GPT-based routing for complex queries:

import openai

def llm_router(query):
    response = openai.ChatCompletion.create(
        model="gpt-4",
        messages=[{"role": "system", "content": "You are a routing assistant. And all the intent logics are defined here"},
                  {"role": "user", "content": query}]
    )
    return response.choices[0].message['content']

This tier was reserved for the ~10% of traffic that couldn’t be resolved by simpler methods. While its cost and latency (1-3 seconds) were significant, it was indispensable for tasks like handling ambiguous user requests or breaking down complex goals.

Production Strategies

After experimenting with these mechanisms individually, I realized that their true power lies in combining them into a tiered architecture. Here’s the pattern I adopted:

Tiered Routing Funnel

The concept is straightforward: traffic flows through a funnel, starting with the cheapest and fastest mechanism (rule-based) and escalating to more expensive methods (semantic, LLM) only when necessary. This approach optimizes costs while maintaining high accuracy:

def tiered_router(query, intents):
    # Tier 1: Rule-based routing
    route = rule_based_router(query)
    if route != "fallback_service":
        return route

    # Tier 2: Semantic routing
    route = semantic_router(query, intents)
    if route != "fallback_service":
        return route

    # Tier 3: LLM-based routing
    return llm_router(query)

Supervisor Pattern

For more complex workflows, I implemented the Supervisor Pattern, a stateful orchestrator agent that breaks down tasks into subtasks and delegates them to worker agents. Think of it as a traffic controller and project manager rolled into one. This pattern improved modularity and workflow management, especially for multi-agent systems.

Router Performance and Handling Follow-Up Queries

One of my key insights was that a model’s ability to generate good answers doesn’t necessarily correlate with its routing effectiveness. For instance, LLaMA 4 consistently outperformed other models in routing accuracy, even though it wasn’t the best generative model.

Stateful vs. Stateless Routers

Follow-up queries presented another challenge. Stateless routers, which treat every query as an independent event, struggled to maintain context. For example:

```python
# Stateless router example
query = "Tell me more about that"
route = rule_based_router(query)  # Fails to resolve "that"
```

In contrast, stateful routers (like orchestrators) retain conversation history, enabling them to resolve follow-up queries effectively.

Key Takeaways and What’s Next

Optimizing AI routing is both an art and a science. Here are my main takeaways:

1. Balance is essential: Effective routing minimizes latency, cost, and accuracy trade-offs by integrating multiple mechanisms.

2. Modularity matters: Decoupling routing logic from agent logic enhances system maintainability and scalability.

3. Advanced patterns hold promise: Emerging strategies like topology routing and confidence-aware routing can expand the horizons of what’s achievable.

Looking ahead, I plan to delve deeper into advanced routing techniques like MasRouter and RopMura, experimenting with dynamic topologies and knowledge-boundary systems.

Routing is not just a technical detail, it’s a critical component of building efficient, scalable AI systems. If you're designing multi-model architectures, I hope my experiences and insights assist you in optimizing your own routing strategies.