AGENTC — Agentic Job Discovery Automation

Businesses running outbound sales need a steady stream of decision-maker contacts at companies that are actively hiring — hiring activity signals budget and growth — but manually scanning job postings, identifying the right decision-makers, and verifying their emails and phone numbers before the data goes stale is slow, labor-intensive work that delays the start of every outreach cycle.

AGENTC — Agentic Job Discovery Automation was built with n8n, Apify, Hunter.io, ZeroBounce, Clearout Phone, Nocodb, OpenAI, Google Sheets, Gmail.

This was built as commercial/client work, so the source code isn't publicly available. Get in touch to discuss the implementation.

It scans recent job postings — typically from LinkedIn and company career sites — to identify which companies are actively hiring in a target industry, then looks up and verifies the contact details of that company's key decision-makers, such as the CEO, CTO, or COO, turning hiring activity into a qualified, timely sales lead list.

Apify actors are pre-built, hosted scrapers triggered directly from n8n's native Apify node, returning structured data via Apify's dataset API — removing the need to write, host, and maintain custom Selenium browser-automation scripts, which typically require separate infrastructure, proxy management, and anti-bot handling that Apify manages on the scraper provider's side instead.

Hunter.io discovers decision-maker contact details — name, email, sometimes phone number — associated with a company domain. ZeroBounce is an email-verification API that checks whether a discovered email address is valid and deliverable. Clearout Phone is a phone-number verification API that checks whether a discovered phone number is valid and reachable — together forming a discover-then-verify pipeline rather than one all-in-one tool.

Deduplicating against existing records before calling a paid API — such as Hunter.io for enrichment or ZeroBounce for verification — prevents spending API credits on companies or contacts that have already been processed, which directly controls the ongoing cost of an automation that relies on metered third-party APIs.

A resilient n8n workflow inspects the HTTP status codes returned from a batch of API requests, isolates any that returned a 429 rate-limited response, and routes those specifically to a notification or retry path — while requests that succeeded in the same batch continue through the pipeline — rather than letting one rate-limited call halt the entire execution.

It's a scheduled workflow that periodically checks the remaining usage credits or quota across every metered third-party service a pipeline depends on — here, Apify, Hunter.io, ZeroBounce, and Clearout Phone — consolidates the results, and reports them to the team, giving advance warning of a depleting balance before it causes a silent pipeline failure.

An AI agent powered by an OpenAI model can be given the day's raw pipeline metrics — jobs scraped, companies enriched, contacts verified — and prompted to generate a plain-language summary of that run, which is then combined with a generated CSV export and sent by email, turning raw automation logs into a readable daily report without manual write-up.

It means replacing a more expensive combination of tools and infrastructure — here, Selenium-based scraping plus a broader tool stack — with a leaner set of purpose-built, usage-metered services chosen and configured through deduplication, batching, and rate-aware requests to minimize redundant API usage while preserving the same daily lead output.