How do I build an AI agent for routing inbound requests?

Grab FastAPI, Anthropic SDK, Tavily. Chain four LLM steps: classify message, search company, generate actions, match to JSON team profiles. Full code vibes in the original post.

What AI model is best for inbox triage agents?

Claude 3.5 Sonnet—speedy, structured JSON outputs. Fallback: GPT-4o-mini for cost. Avoid base models; they hallucinate routes.

Will AI routing replace sales teams?

Nah—augments. Handles volume, preps intel; humans seal deals. Expect 2-3x faster responses, fewer lost leads.

🤖 AI & Machine Learning

Inbox Triage Reinvented: The Weekend AI Agent That Routes Leads in Seconds

A sales lead hits the inbox—gone in seconds, auto-routed with company intel and action items. This isn't sci-fi; it's a weekend Python build using Claude that's slashing response times for B2B teams.

theAIcatchup Apr 10, 2026 4 min read

Flowchart of AI agent pipeline classifying, researching, and routing inbound email requests

⚡ Key Takeaways

Break inbox triage into modular LLM steps for reliability and speed. 𝕏
Real-time company research via Tavily turns raw leads into briefed handoffs. 𝕏
JSON team profiles enable dead-simple, semantic routing—scale without recoding. 𝕏

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theAIcatchup

Community-driven. Code-first.

#AI agents #B2B ops #B2B sales automation #B2B triage #Claude AI #ai-agent #inbound routing #inbox automation

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