AI Automation: Deploying an Autonomous Career & Recruitment Orchestration System

Automation Stack & Architecture

Agent Framework: enoughsifgetting_started with Claude Code / Neuro-agent logic
Integration Layers: GitHub open source repository ai-job-search + HeadHunter API (`HH.ru`) or Tool #141` + Telegram/WhatsApp interface
Target Outcome: Fully autonomous end-to-end vacancy monitoring, resume adaptation, and professional headhunting without manual scrolling.

Job Seeker Agent (Claude Code based)

Persona: Professional career assistant focused on finding suitable positions and tailoring applications.
Goal: Monitor job sites, evaluate position fit, adapt resumes to specific requirements, and generate cover letters.
Tools assigned: Job site monitoring tools; Resume/Cover letter generation engine.

Recruiter AI Agent (ProTalk enabled)

Persona: Intelligent HR Recruiter capable of understanding natural language context for sourcing candidates.
Goal: Find ideal candidates using filters like experience level, salary range, and relocation readiness으로 any given requirement properly decoded from chat commands.
Tools assigned: `Function №141`: Search resume on HH.ru; Contact collection tool; Brief analysis generator.

Triggering event or user commandtnoted in inputstget_started enoughsifgetting_startedset via Telegram or WhatsApp dialogue.
If searching for jobs: The agent monitors vacancy websites $ ightarrow$ evaluates suitability $ ightarrow$ adapts resume prepared against certainmrequirementsments ($ ext{input data} $) $ ightarrow$ output contactable materials.
If recruiting: User provides a text prompt e.g., "Find sales staff with >2 years exp / $k>$ etc." $ ightarrow$ trigger function №141` (`Search resume on HH.ru`) $ ightarrow$ collect contacts/resumes per filterssensationsings sentser'swon rskersrerrrslye (list of bestest possibleqf).
Final Output: A ready list containing short brief analyses and direct access to candidates, moving straight from search to negotiation phase without manual scrolling.

Contextual Refinement: Use natural language context-understandingto allow users to refine requests

! DYOR (Do Your Own Research)

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