Architecture Advice: Scalable Multi-Source Inbound Pipeline for Enterprise Resume Screening Agents

Hi everyone,

I’m building an enterprise-grade AI Resume Screening Agent and could use some advice on streamlining the architecture.

I’m looking to solve three specific challenges:

1. Unified Trigger Strategy:

Enterprises receive resumes from fragmented sources: Google Drive uploads, LinkedIn/hiring platforms (API), website career forms (Webhooks), and direct inbound emails (IMAP).

The Challenge: Instead of having 5 different starting points, what is the best practice for "normalizing" these inputs into a single trigger for the AI Agent?

2. Idempotency & De-duplication:

How do you best ensure a resume isn't screened multiple times if it’s submitted through different channels?

The Challenge: I’m considering hashing the file or using a "processed" flag in a vector database. Does anyone have experience with a robust way to handle this at scale without increasing latency?

3. Multi-JD Matching Logic:

The enterprise has multiple active job postings. The agent needs to pick the correct Job Description (JD) to screen against.

The Challenge: Should the agent first perform a "Classification" step to assign the resume to a Job ID, or should I be looking at a multi-agent setup where a "Router" sends the resume to a specific sub-agent dedicated to a specific JD?

I'd love to hear how you guys are handling the orchestration of these "pre-screening" steps.

Thanks!

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