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About Agentic Jobs

From one job seeker to another.

I built this during a frustrating job search. The problem was not a lack of listings. The problem was figuring out which ones were worth my time.

I would open a role that looked perfect, spend 45 minutes tailoring my resume to it, write a cover letter, and submit. Then I would hear nothing. Later I would dig into it and realize the listing was 73 days old, reposted verbatim from months earlier, showing up on six platforms with conflicting locations and no salary range. The company had not hired in that function in over a year. It was never a real opening. I had just spent a morning on nothing.

That happened enough times that I stopped trusting any listing at face value. Once that trust is gone, a job search gets exhausting in a different way. You spend more energy evaluating than actually applying. I started keeping notes on which sources were reliable, which companies were really hiring, and which description patterns seemed to correlate with actual interviews. It started as a spreadsheet, then became a script, and eventually turned into this.

Agentic Jobs is the product I wished I had while I was searching. I wanted one place where the noise had already been reduced before I opened the first tab. I wanted to see whether a listing came from a direct ATS source or a third party mirror, how old it really was, whether the description had enough detail to be worth my time, and which skills were most important to lead with on my resume. The evaluation work I was doing by hand in a spreadsheet is now automated and applied before a listing shows up on the page.

I am not trying to replace the job search. I am trying to make the hour you spend on it go further.

My name is Krishna. I built Agentic Jobs to solve a problem I lived through, and I keep improving it based on what I hear from people using it. If something is not working, the contact page goes directly to me.

What Agentic Jobs does

Agentic Jobs is a full stack aggregation and enrichment pipeline. It pulls listings from primary sources such as ATS systems, company career portals, and job APIs. It then normalizes them into a unified schema, enriches them with extracted metadata, and ranks them with a trust score so cleaner listings show up earlier.

The main difference from most aggregators is where the data comes from. This platform targets ATS endpoints directly, including Greenhouse, Workday, Lever, Ashby, iCIMS, SmartRecruiters, and Oracle Cloud. Direct ATS access keeps the data closer to what the employer actually published before resyndication introduces stale metadata or missing fields. That is where much of the trust score advantage comes from.

The pipeline, step by step

01

Multi-Source Aggregation

The crawl pulls from more than 15 source types. ATS platforms such as Greenhouse, Workday, Lever, Ashby, iCIMS, SmartRecruiters, and Oracle Cloud are queried directly by company. Broad APIs like LinkedIn, Indeed, JSearch, Adzuna, Jooble, and Remotive fill in the gaps. Each source runs in parallel to keep searches fast, and a raw cap protects the system on high volume runs.

02

Description Enrichment from Detail Pages

Many aggregator listings contain truncated previews, sometimes only 2 or 3 sentences. The pipeline identifies those cases and fetches the fuller description from the linked detail page with a concurrent thread pool and configurable timeout. This step alone improves the quality of everything that follows.

03

Schema Normalization

Every listing is normalized into a unified schema regardless of source. Fields like title, company, location, posted_date, work_mode, and salary are extracted, standardized, and validated. Raw HTML is parsed into plain text and Markdown. Excessive whitespace, repeated legal blocks, and formatting artifacts are cleaned out.

04

NLP Enrichment: Skills, Level, Work Mode, and Salary

The enrichment stage extracts structured metadata from the normalized text. Skills are matched against a curated taxonomy weighted by title context. Experience level is inferred from title signals and description language. Work mode is parsed from both the location field and the description body. Salary ranges are extracted from unstructured text when not present as a structured field.

05

Deduplication

The same role routinely appears across five or six sources. The deduplication step groups postings by normalized company name, then applies fuzzy title matching with a configurable similarity threshold. Matched postings are linked by a shared group key. The trust scorer uses that relationship to reward the canonical ATS record and discount the aggregator mirrors.

06

Trust Scoring

Every listing receives a trust score from 0.0 to 1.0, computed from source quality, posting freshness, metadata completeness, and deduplication signals. The score maps to three labels: High, Medium, and Low. Listings streamed to the UI during an active crawl receive a preview trust score so results are usable before the full pipeline completes.

07

Summary Generation

Every listing with a sufficient description gets a rule-based summary that extracts role scope, top required skills, and a resume angle. For listings that meet a minimum quality threshold, an AI assisted summary adds a shorter explanation that helps you decide faster. Both summary types are available in the listing detail panel.

A few limits

A few things are worth stating clearly:

  • *Not a job application system. The platform surfaces and enriches listings. Applications happen on the employer's original page. We never intercept that process.
  • *Does not guarantee listing accuracy. The enrichment pipeline improves quality significantly but cannot verify every field. Salary ranges are extracted heuristically. Always verify details on the source ATS page before applying.
  • *The trust score is a signal, not a guarantee. High trust means the observable quality signals are clean. It does not mean the company is actively interviewing this week. Some high trust listings are still on hiring freezes.
  • *No access to internal hiring data. The pipeline uses publicly available listings only. Internal requisition status, recruiter activity, and offer volume are not visible to this system.

Why the guides are here

The guides come from the same problem that led to the product. Job search advice is often broad, repetitive, and disconnected from what a candidate is actually looking at on the screen. These guides are meant to be more practical. They focus on things like spotting ghost jobs, reading descriptions faster, and turning a posting into interview prep without starting from zero.

If something in a guide is wrong, a listing has an obviously broken trust score, or the enrichment missed something important, I want to know. The contact form goes directly to me.

Ready to look at listings differently?

The dashboard surfaces listings across roles and locations with trust scores already applied. Filter to High trust only and start from the cleaner end of the pile.

Read the Guides