Cost-Aware Agent Routing: Optimize Your RAG Workflow with Pre-Check Steps¶

In many AI pipelines, especially those that use retrieval-augmented generation (RAG), not every user input requires a full context fetch. Blindly triggering expensive vector queries and similarity search can drastically inflate cost without improving output.

Inspired by real-world field practices, this use case explores how a simple pre-check step using a lightweight LLM can act as a router — deciding whether to invoke the full RAG retrieval flow or skip it.

What this solves¶

• Reduces unnecessary embedding + vector search calls
• Enables smarter usage of both local (e.g., Gemma, Mistral) and hosted models (e.g., OpenAI, Claude) as front-line routers
• Adds an auditable decision step to your trace (e.g., "RAG skipped due to low context need")
• Validated in real-world usage: one OpenAI-based RAG system reported 70% cost reduction using this method (Reddit post)

How to implement this in Dokugent¶

Create a plan with a routing step as the first item:

{
  "id": "route_query",
  "goal": "Decide if query needs RAG context",
  "tools": ["cheap-llm-router"],
  "constraints": [
    "Avoid unnecessary RAG calls",
    "Log decision with explanation",
    "Allow override by human reviewer"
  ]
}

// Example trace step: { "step": "route_query", "decision": "Skip RAG", "reason": "Query matches FAQ with high confidence", "model_used": "Gemma-2b", "cost_saving_estimate": "Saved ~1,000 tokens" }

If the router deems context unnecessary, the rest of the plan may short-circuit or fall back to a fast local response step.

Why this matters¶

The decision not to act is just as important as acting. This isn't just theoretical — recent public benchmarks show pre-check steps can dramatically reduce cost without sacrificing quality. In cost-sensitive agent deployments, the ability to skip a step — with traceable reasoning — is part of responsible design.

Dokugent makes it easy to scaffold, test, and certify these kinds of cost-aware decisions. They're visible in previews, dryruns, and certs — helping you validate not just what your agent did, but what it wisely chose not to do.