When you send a query to an AI platform, how does it decide which model to use? This seemingly simple question is actually the foundation of AI trust. If you can’t see the selection logic, you’re operating on faith alone.

Why Routing Exists at All

Once organizations deploy multiple specialized AI models, they face a critical decision: which model should handle which request? This routing problem emerges because different tasks have fundamentally different optimal solutions.

The economics are compelling: research on RouteLLM showed 85% cost reduction while maintaining 95% of GPT-4’s quality – simply by routing simple queries to cheaper models. Without intelligent routing, teams default to expensive models for everything.

But the problem goes deeper than cost. Single requests often contain multiple, distinct intents. “Review this auth code for security vulnerabilities and generate fixes” actually needs two different optimal model choices: vulnerability scanning (pattern matching) and code generation (specialized knowledge).

Traditional hard-coded rules quickly become unmaintainable: conflicting conditions, ambiguous categorizations, constant updates for new use cases. Intelligent routing shifts from keyword-matching to understanding actual user intent.

For a deeper exploration of intent-aware routing and compound AI systems, see Intent-Aware Routing for Compound AI.

Why Routing Matters for Trust

Every enterprise AI platform makes routing decisions. Your message might go to GPT-4 for complex reasoning, Claude for nuanced analysis, or a local model for privacy-sensitive data. But most platforms treat this as a black box.

Building a local routing layer isn’t about replacing cloud services. It’s about understanding the decision-making process so you can:

• Verify that sensitive queries stay on privacy-compliant models
• Understand cost implications of model selection
• Validate that quality requirements are being met
• Debug when responses don’t match expectations

The 6-Dimensional Selection Model

Rather than simple rules like “use GPT-4 for everything,” a proper routing system evaluates multiple dimensions simultaneously. Each dimension answers a different trust question:

Why These Six?

Each dimension maps to a real-world concern:

Dynamic Weight Adjustment

Static weights don’t work in the real world. Different routes and security classifications need different priorities. Here’s the key insight:

# Base weights for general requests
weights = {
    "performance": 0.20,
    "speed": 0.15,
    "quality": 0.25,
    "cost": 0.10,
    "privacy": 0.20,
    "availability": 0.10
}

# For CONFIDENTIAL or SECRET data
if security_classification in ["CONFIDENTIAL", "SECRET"]:
    weights["privacy"] = 0.50  # Override: privacy becomes dominant
    # Normalize other weights proportionally

The Model Registry

Routing decisions require knowing what each model can do. A local registry lets you see exactly what capabilities you’re working with:

Each model has capability scores across the six dimensions. When a request comes in, the router multiplies capability scores by current weights to get a final ranking.

Beyond Scoring: Intent-Based Routing

Simple scoring works for straightforward requests. But complex agentic workflows need something smarter. Consider a request like:

This isn’t one task – it’s four interconnected tasks with dependencies. Intent-based routing breaks this down:

Intent Parsing

The intent parser identifies what the user actually wants to accomplish:

# Parsed intents from the sales query
{
    "intents": [
        {"type": "DATA_ANALYSIS", "target": "sales data"},
        {"type": "TREND_IDENTIFICATION"},
        {"type": "REPORT_GENERATION"},
        {"type": "VISUALIZATION", "count": 3}
    ],
    "complexity": "COMPLEX",
    "dependencies": [
        ["DATA_ANALYSIS", "TREND_IDENTIFICATION"],
        ["TREND_IDENTIFICATION", "REPORT_GENERATION"]
    ]
}

Execution Strategy Selection

Based on complexity and dependencies, the system chooses how to execute:

6D vs Intent: When to Use Each

Both approaches have their place:

What You Learn By Building This

Running routing logic locally teaches you things that API documentation can’t:

• Cost patterns: You’ll see exactly when expensive models get selected and can tune thresholds
• Privacy boundaries: You can verify that sensitive data never leaves certain models
• Performance tradeoffs: You’ll understand the real latency cost of quality improvements
• Failure modes: You can test what happens when primary models are unavailable

This understanding transfers directly to evaluating enterprise AI platforms. When a vendor says “we route to the optimal model,” you’ll know the right questions to ask.

Coming Up Next

Routing gets queries to the right model, but many queries need context. In the next post, we’ll explore the Knowledge Service – how RAG (Retrieval Augmented Generation) provides grounding and context to improve response quality.