Resumes are still stuck in 2010. What if an AI agent handled the entire process?

Every few years, the resume "tool" space gets a facelift. New templates, new editors, maybe a grammar checker. But the core workflow hasn't changed:

1. Open an editor
2. Stare at a blank page
3. Google "strong action verbs for resume"
4. Copy-paste your old resume and tweak it
5. Hope it passes ATS filters you can't see
6. Repeat for every single job application

We've automated deployments, CI/CD, infrastructure — but job applications are still manual, repetitive, and blind.

The current landscape

Tools like Jobscan, Resume Worded, Teal, Rezi, and SkillSyncer have made progress. Most of them do one or two things well — keyword matching, ATS scoring, or template editing. But none of them close the full loop.

You score your resume on one tool, edit it in another, search for jobs on LinkedIn, and tailor manually for each application. It's fragmented.

What if it was an agent instead?

I've been thinking about what a true agentic workflow for job searching would
look like:

Instead of you doing all the work across 5 tabs, the agent:

• Creates your resume from a conversation — like talking to a career coach,not filling out form fields
• Analyzes it against real ATS parsing rules and gives you a score with specific gaps
• Auto-matches jobs from the internet that fit your profile — no more endlessly scrolling job boards Tailors your resume per job — one base resume, custom versions generated for every application
• Tells you exactly what to fix — not generic advice, but "add X keyword to your experience section for this specific role"

The technical challenge that interests me

The matching part is straightforward — embeddings + keyword extraction against job descriptions. What's harder:

1. Job discovery — scraping job boards at scale without getting blocked, keeping listings fresh, deduplicating across sources
2. Quality tailoring — LLMs are good at rewriting, but maintaining authenticity while optimizing for ATS is a fine line. You don't want every resume to sound like ChatGPT wrote it
3. Scoring accuracy — most ATS checkers are keyword matchers dressed up with a percentage. Real ATS systems (Workday, Greenhouse, Lever) parse differently. How close can you get without reverse-engineering proprietary parsers? The trust problem — would someone trust an AI to auto-tailor and submit applications on their behalf? Or do they always want a human-in-the-loop?

I'm building parts of this — and I'm stuck on prioritization

I've already built the score + tailor + export flow. But the "agent" layer — conversational resume building + auto job matching — is a bigger bet.

Before going deeper, I want to hear from this community:

Would you use a conversational AI to build your resume, or do you prefer the control of a traditional editor?

Auto-matching jobs sounds great, but would you worry about missing opportunities the AI didn't surface?

What's your biggest pain point in the current process — writing the
resume, tailoring it per job, or finding the right jobs?

For the devs building in the career/HR space — what's the hardest
technical problem you've hit?

Genuinely looking for signal here, not selling anything. Drop your thoughts below.