Build an AI Recruitment Screening Agent
An intelligent agent that automates resume parsing, candidate evaluation, and interview scheduling by combining document processing, structured data extraction, and multi-step workflow orchestration.
Document Processing
Parse resumes and cover letters from various formats (PDF, DOCX, HTML) into structured text for downstream analysis
Converts PDFs, DOCX, and other document formats into clean structured text, ideal for extracting resume content regardless of formatting
Handles a wide variety of document types and converts them to structured data, useful when resumes come in diverse formats
Lightweight option for converting office documents to markdown, suitable for simpler resume parsing pipelines
Candidate Analysis & Scoring
Use LLMs with structured output to extract skills, experience, and qualifications, then score candidates against job requirements
Enforces structured outputs from LLMs via Pydantic schemas — perfect for extracting typed candidate profiles (skills, years of experience, education) with validation
Programmatic LLM pipelines with optimizable prompts — useful for building reliable scoring modules that improve over time with feedback
Type-safe agent framework that ensures candidate evaluation outputs conform to strict schemas, reducing hallucinated scores
Agent Orchestration
Coordinate the multi-step screening workflow: intake → parse → evaluate → rank → notify, with human-in-the-loop approval for final decisions
Graph-based agent workflow enables branching logic (auto-reject, shortlist, request more info) with checkpointing and human approval nodes
Role-based multi-agent orchestration lets you assign specialized agents for technical screening, culture fit assessment, and scheduling
Visual workflow automation with native AI nodes — great for teams that want a no-code interface to manage the screening pipeline and integrate with HR tools
Knowledge & Memory
Store candidate profiles, job descriptions, and historical hiring decisions for semantic search and context-aware evaluation
Lightweight vector database for embedding and retrieving candidate profiles, job descriptions, and past successful hires for similarity matching
Persistent memory layer that remembers candidate interactions across sessions, useful for ongoing recruitment pipelines with returning applicants
High-performance vector search for large-scale candidate pools, with advanced filtering by metadata like location, seniority, and skills
Observability & Evaluation
Monitor screening accuracy, detect bias in evaluations, and track agent performance to ensure fair and effective hiring decisions
Traces every screening decision back to its LLM calls — essential for auditing hiring fairness, debugging false rejections, and optimizing prompt quality
AI observability with built-in evaluation tools to measure screening accuracy against human reviewer decisions
LLM evaluation framework to unit-test screening criteria — ensures the agent consistently applies job requirements without bias drift