What should the fallback agent prioritize: speed or accuracy?

[Isaac24Karat]

I'm building an agentic RAG workflow where queries are routed through expert agents. If the primary agent fails, a fallback agent takes over to ensure the user still gets a response.

But now I’m at a crossroads:

💡 Should the fallback agent prioritize speed (to ensure continuity), or accuracy (to maintain trust in the output)?

🔍 Here's the context:
The fallback agent is triggered when the main AI fails due to:

timeouts,

API overload,

or confidence score drops below a threshold.

Options I'm exploring:

Speed-first fallback: Use a smaller LLM like Phi-3 or Gemini Nano to give “good enough” answers fast.

Accuracy-first fallback: Route to Claude 3 Opus or a multi-agent chain, which takes longer but provides richer responses.

🧠 Why it matters:
Fallback behavior impacts:

🕒 User experience

🤖 Agent trust and credibility

📊 Performance KPIs like response latency and resolution accuracy

✅ What I’d love input on:
If you’ve built multi-agent workflows, how do you balance these trade-offs?

Should fallback mode degrade gracefully, or preserve depth at all costs?

What metrics do you use to guide this choice?

🧩 Open to feedback, ideas, or examples from your own builds.
(And feel free to check out the repo here: agentic-rag-system)