1) Hard to find,

2) Hard to get,

3) Hard to pass ATS,

4) Hard to interview, or

5) otherwise full of curveballs? Good — this is where layered AI agents shine.
Below I’ll walk you through a practical, ethical, copy-and-pasteable pipeline that uses multiple specialized agents (discovery, research, ATS-sim, resume-tailor, outreach, interview coach, negotiation, and tracker). Every agent gets: purpose, persona, exact prompt templates, inputs/outputs, and how to chain them together. ✨

Quick overview: discover → match → research → tailor → submit → prep → follow-up → negotiate → track.
Use small, focused agents for each step rather than one “jack-of-all-trades” agent. That makes automation robust, auditable, and easier to improve. 🔗

Safety + ethics note ✋

Always be honest about your experience. Use AI to translate and clarify your real skills — not to fabricate credentials. Respect application channels and anti-spam norms. This guide focuses on ethical, high-signal methods.

Pipeline & agent list (high level) 🧭

1. Role Discovery Agent — finds hidden/rare openings. 🔎

2. Role-Matcher Agent — decides which roles fit you best. 🧩

3. Company Research Agent — builds argument maps & culture fit. 🏢

4. ATS Simulator Agent — extracts keywords and predicts ATS pass/fail. 🤖

5. Resume Tailor Agent — rewrites & formats for ATS + human readers. 📝

6. Cover-Letter/Outreach Agent — crafts outreach, InMails, follow-ups. 💌

7. Application Sender (Automation) Agent — fills forms or prepares attachments. ⚙️

8. Interview Coach Agent — simulates interviews & provides feedback. 🎙️

9. Offer/Negotiation Agent — crafts counteroffers and scripts. 💼

10. Tracker/Analytics Agent — logs activities and metrics. 📊

Agent-by-agent: step-by-step DIY (with prompts you can copy) 🛠️

1) Role Discovery Agent — find the hidden openings 🔎

Purpose: Hunt for niche, unadvertised, or poorly-labeled roles on job boards, company pages, and LinkedIn.

Persona: “Researcher that scrapes and summarizes new postings and signals unusual hiring patterns.”

Inputs: keywords, industries, geo (optional), seniority, unusual title aliases (e.g., “growth engineer” = product + data).

Output: list of 20 prioritized leads with: Title, Company, link, why it’s hard-to-find, signal strength (1–10).

Prompt template (for a general LLM agent):

You are a role discovery agent. Search the specified sources for roles that are rare or poorly advertised matching: [keywords], [locations], [seniority]. Look for signals such as unlisted “careers” pages, product launch teams, VC portfolio hiring, GitHub job posts, niche Slack/Discord job channels, or “we’re hiring” blog posts. Return a JSON array of up to 20 items: {title, company, link, why_hidden, signals, match_score} and prioritize by match_score. Explain the top 3 picks in 1–2 sentences each.

DIY Steps:

1. Start with 5–10 broad keywords + 5 niche synonyms.

2. Run the agent across: LinkedIn search, GitHub Jobs, company careers pages, Twitter/Bluesky/Threads, niche Slack/Discord job channels (if you belong), and specialized job boards.

3. Export results to CSV/Google Sheet. Tag ones with “referral potential” (e.g., company connections).

Tips: use boolean queries and synonyms. Save the source HTML or snapshot link for research agent.

2) Role-Matcher Agent — prioritize the best hard roles 🧩

Purpose: From discovered leads, pick which to pursue based on fit and ROI.

Input: your resume, LinkedIn, 20 discovered leads.

Output: ranked list with suggested priority level, mismatch risks, and suggested angle (skill to emphasize).

Prompt template:

You are a role-matcher. Given this candidate profile: {paste resume + LinkedIn summary} and this list of roles: {paste discovery CSV item list}, score each role 0–100 for fit. For the top 5, provide 3 personalized selling points and one risk to mitigate.

DIY Steps:

1. Paste your resume + LinkedIn into the agent.

2. Feed the discovered leads CSV rows.

3. Get a ranked shortlist (top 5). These are the roles you’ll invest time in tailoring for.

Tip: Aim for a mix: 1 high-likelihood (easy), 2 stretch, 2 moonshot (hard but high reward).

3) Company Research Agent — build the narrative map 🏢

Purpose: Generate a briefing for each target employer: product, org, hiring triggers, pain points, exec bios, funding, and hiring manager signals.

Inputs: job post link(s) + company URL + LinkedIn company profile.

Output: “Briefing pack” (1 page) with bulletized selling points, 3 angles to pitch, 3 questions to ask in interviews, 3 people to connect with.

Prompt template:

You are a company research agent. For {company} and {job_link}, produce: (1) 6-line company summary, (2) 3 business pain points they likely need to solve based on job posting and public info, (3) 3 tailored statements linking candidate’s experience to those pains, (4) 3 recommended people to message and suggested connection message.

DIY Steps:

1. Use the discovery agent’s link and company page.

2. Pull recent news, product pages, and leadership bios.

3. Output goes into your interview prep and outreach drafts.

Tip: Save snippets/quotes to use in outreach & interviews (“I noticed your team just launched X…”).

4) ATS Simulator Agent — get inside the machine 🤖

Purpose: Predict how your resume will fare against ATS, extract relevant keywords from the job description, and produce an “ATS score” and suggestions.

Inputs: Job description text + your resume text.

Output: score (0–100), list of missing keywords, formatting/structure notes, suggested job-specific bullet rewrites.

Prompt template:

You are an ATS-simulator. Parse this job description and identify the top 12 keywords and phrases (exact and near variants). Parse this resume and highlight missing or underweighted keywords, and recommend 5 bullet changes to increase ATS keyword match — keeping truthfulness. Provide a sample optimized resume header and 3 optimized bullet examples.

DIY Steps:

1. Paste job description and resume.

2. Get the top keywords and a short list of bullets to swap.

3. Use those bullets in the Resume Tailor Agent (next).

Ethical tip: Don’t stuff keywords; weave them naturally into real accomplishments.

5) Resume Tailor Agent — rewrite for ATS and humans 📝

Purpose: Produce an ATS-friendly and recruiter-friendly resume variant tailored to the role.

Inputs: original resume, top keywords (from ATS agent), company briefing.

Output: a two-tier resume: (A) clean ATS-friendly plain-text / PDF version, and (B) a visually styled PDF for human reviewer (if applicable), plus a 3-line summary for LinkedIn.

Prompt template:

You are a resume-tailor. Use these keywords: {list}. Rewrite the resume so the top third (headline + summary + top bullets) aligns with the job. Keep accomplishments quantifiable; keep language truthful. Provide:

• 1 candidate summary (3 lines) for LinkedIn

• 10 resume bullet rewrites prioritized for the top role

• A plain-text ATS-friendly resume (no fancy columns, minimal graphics) ready to copy/paste.

DIY Steps:

1. Apply the suggested bullet rewrites.

2. Produce an ATS-friendly plain-text file and a recruiter-friendly PDF.

3. Run the ATS Simulator again to re-score. Iterate until score improves.

Before/After example (realistic):

• Before: “Led backend tasks and improved system.”

• After: “Spearheaded migration to microservices, reducing API latency 42% and improving system uptime from 96.3% to 99.7% (6 months).” ✅

6) Cover-Letter / Outreach Agent — warm, specific, referral-ready 💌

Purpose: Create short, hyper-personalized outreach (LinkedIn InMail, email, gatekeeper message) tailored to people identified by Research Agent.

Inputs: company briefing, role, tailored resume highlights, target person.

Output: 3 variations: (A) Short LinkedIn connection note, (B) Detailed InMail/email, (C) Referral request template for employee.

Prompt template:

You are an outreach agent. Write: (A) 120-char LinkedIn connection message, (B) 250–400 word InMail that highlights 3 relevant accomplishments, ends with a clear ask, and (C) a referral request message the employee can forward. Tone: professional, curious, results-focused.

DIY Steps:

1. Use the Research Agent’s “why hire me” bullets.

2. Send connection request (A). If accepted, wait 24–48 hours and send (B). If you find a mutual connection, use (C).

Tip: Include one company insight (from Research Agent) to prove you did homework.

7) Application Sender (Automation) Agent — reliably apply at scale ⚙️

Purpose: Automate repetitive parts of applying (upload resume, fill standard fields) while preserving personalization.

How to implement: Use Zapier/Make/n8n or a browser macro to paste text, upload files, and store log entries. IMPORTANT: for forms that require CAPTCHA or have anti-bot rules, use human-in-the-loop.

Inputs: tailored resume, cover letter, application form fields.

Output: sent application (or prepared draft) + tracker entry.

DIY Steps:

1. For each high-priority role, prepare attachments and saved form data.

2. Use an automation workflow to pre-fill forms; stop before final submit to review on sites that enforce manual checks.

3. For LinkedIn Easy Apply, prepare a saved file pack and the short answers. Use automation cautiously and respect terms.

Ethical tip: Don’t spam. Personalize the top roles — automate low-value, high-quantity roles.

8) Interview Coach Agent — simulate & score 🎙️

Purpose: Run mock interviews (behavioral, technical, case), give feedback, and produce bullet-level STAR answers.

Inputs: job description, company briefing, tailored resume, voice or text response samples.

Output: graded mock interview (score + feedback), 10 STAR answers, 5 technical whiteboard prompts and model answers.

Prompt template:

You are an interview coach specialized in {role}. Run a mock interview with 10 questions. For each response, score 1–5 on clarity, impact, and evidence, give corrections, and produce an optimized STAR-format answer for the best version.

DIY Steps:

1. Run one mock with text answers; run another with audio (if tool supports).

2. For technical roles, ask for step-by-step solutions and then for an explanation a hiring manager would accept.

3. Record, iterate, and rehearse.

Practical tip: Save the optimized STAR answers and practice them aloud until they feel natural.

9) Offer / Negotiation Agent — script your next move 💼

Purpose: Create negotiation scripts, walk you through priorities, and compute BATNA (best alternative).

Inputs: offer terms, market comps, your bottom line.

Output: negotiation email, talking points, and suggested concessions list.

Prompt template:

You are an offer negotiation agent. Given this offer: {salary, equity, start date, title, benefits} and market comps {if available}, suggest a counteroffer email (polite, data-backed) and three negotiation talking points ordered by likely effectiveness.

DIY Steps:

1. Identify must-haves vs nice-to-haves.

2. Use the agent’s email template to propose a counter.

3. Roleplay with the Interview Coach Agent for negotiation calls.

Tip: Never accept immediately — buy time, ask clarifying ques­tions.

10) Tracker / Analytics Agent — measure what matters 📊

Purpose: Keep an audit trail: applications, responses, interview dates, next actions, and success rate.

Inputs: outcome of each apply, dates, response status.

Output: a dashboard (sheet/CSV) with KPIs: apps sent, replies, interviews scheduled, offers, conversion rates.

DIY Steps:

1. Use a Google Sheet + small agent to append rows after each step.

2. Weekly, ask the agent for insights (e.g., “which subject lines got >20% replies?”).

3. Use insights to refine messaging and target selection.

Metrics to track: time-to-apply, reply rate, interview rate, offer rate, interview->offer conversion.

How to chain them — example flow (real use) 🔗

1. Discovery Agent finds 20 roles → output CSV.

2. Role Matcher takes CSV + your resume → shortlist top 5.

3. For each top role:

   • Research Agent creates company briefing.

   • ATS Agent parses JD + scores your CV.

   • Resume Tailor rewrites top bullets and summary.

   • Outreach Agent writes LinkedIn connection + InMail.

   • Application Sender prepares application and logs to Tracker.

   • Interview Coach preps you once an interview is scheduled.

   • Negotiation Agent readies counteroffer scripts when offers come.

4. Tracker stores everything and produces weekly analytics to tune the pipeline.

This is repeatable: tweak the keywords and the agent prompts as you learn.

Tools & implementation notes (practical)

• You can implement these agents with: a) interactive LLM sessions (ChatGPT/Claude), b) scripted prompts in LangChain, or c) low-code automations (Zapier/Make) that invoke LLM APIs.

• Keep one source-of-truth spreadsheet. Agents should always export to that sheet.

• Use human-in-the-loop for sensitive submits (final application send, signatures, salary negotiation).

• Use version control for resumes (e.g., Resume_V1_GoogleSheetRowID).

Prompt engineering cheat sheet (copy-paste ready) ✂️

Resume Tailor — sample short prompt

Resume Tailor — Role: {role title}. Job description: {paste JD}. Candidate resume: {paste resume}. Return: 1) ATS plain-text resume; 2) Top 6 bullets to replace in current resume; 3) 3-line LinkedIn summary. Keep all facts consistent with the resume.

Interview Coach — sample short prompt

Interview Coach — Role: {role}. Use this resume: {paste}. Ask 8 behavioral + 4 technical questions. For each:

• Provide the question, expected key points the interviewer wants, a model STAR answer, and one sentence improvement for the candidate.

Outreach Agent — sample short prompt

Outreach Agent — Company: {company}. Target person: {name + role}. Provide: 1) 120-char LinkedIn connect message; 2) 300-word InMail (mention 1 company insight); 3) one-line reminder subject for follow-up.

Quick examples (realistic micro-samples) ✍️

LinkedIn connection (120 chars):

Hi [Name], I help teams reduce API latency by 30–50% — saw your cloud rollout at [Company]. Would love to connect! 👋

InMail opener (short):

Hi [Name], congrats on [recent product launch]. In my last role I cut API latency 42% and freed engineers to ship faster — I’d love to share a 15-min idea that may help [company goal]. Would Wednesday work?

STAR Answer model (behavioral):

Situation: Legacy API caused customer timeouts. Task: Reduce latency & improve uptime. Action: Led migration to microservices and introduced observability + runbook. Result: Latency down 42%, uptime from 96.3% → 99.7%; decreased incidents by 60% in 6 months.

Troubleshooting common hard-job problems 🩺

• ATS keeps rejecting: check file type (use .docx or simple .pdf), remove complex headers, run ATS-sim again, prioritize keywords in top third of resume.

• No recruiter replies: change subject lines, shorten outreach, add clear value, ask for 10–15 minutes, mention a mutual or company insight.

• Technical interview flops: run more targeted mock interviews with role-specific problems; get runnable code feedback or whiteboard diagrams from the Interview Coach.

• Hard-to-find roles vanish quickly: increase frequency of discovery scans, set alerts, and use referrals via mutual contacts identified by Research Agent.

Weekly improvement loop (short) 🔁

1. Review tracker weekly.

2. Ask the agents: “Which 3 outreach templates got the best reply rate?” and “Which resume bullets converted to interviews?”

3. Replace low-performing templates and retarget roles.

Final checklist before you send an application ✅

• ATS Agent score improved vs baseline.

• Resume & LinkedIn summary updated.

• Company briefing saved (3 tailored points).

• Outreach message ready for hiring manager + connection attempt.

• Application logged in Tracker.

• Interview Coach scheduled for mock interview (if invited).

TL;DR — The secret sauce ✨

Break the job search into many tiny, repeatable agents. Each agent does one thing really well — discovery, matching, ATS check, tailoring, outreach, coaching, negotiation, and tracking. Chain them, iterate weekly, and keep humans in the loop for judgment calls. Use metrics to optimize what actually wins interviews (not what feels good). 📈