Hiring Agent: E2E AI Evaluation for Fair & Auditable Hiring Decisions
Building, optimizing and evaluating an agentic workflow with guardrails, fine-tuning, and extensive evaluation.
We also use this case study to explore responsible and fair agent development and reporting for the EU AI Act.
Created on April 11|Last edited on June 2
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🎯 Objective🧬 System Architecture🧪 Development Workflow1. Dataset Generation2. System Development3. Fine-tuning of the comparison model📈 Evaluation and Monitoring1. System Evaluation in W&B Weave2. Model Evaluation in W&B Models3. Production Monitoring🧠 Conclusion and Next Steps
🎯 Objective
The Hiring Assistant assists in pre-selecting candidates for an interview round by evaluating candidate applications against job positions using a chain of different LLMs. We focus on performance and transparent, unbiased decision making while maintaining compliance with regulations like the EU AI Act. While there's a lot more techniques and processes one can add to this workflow the objective was to show an E2E fine-tuning, system engineering, evaluation, and production monitoring of an agentic workflow on a real-world use-case.
Check the code here for implementation details and the whitepaper here explaining in detail what risks, mitigation strategies and reporting frameworks we set up for this high-risk use-case, in collaboration with appliedAI.
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Figure 7 in whitepaper - shows the different steps and possible execution worklows depending on the guardrail (see below for more info).
Table 3.1.3 in whitepaper - subset of the analyzed risks and their respective mitigation techniques and proposed testing methods.
🧬 System Architecture
The system consists of the following components. We use Streamlit as simple UI for the HITL view and Langgraph as state machine.
1. Extraction Step
- PDFs for job offers and applications is extracted using PyMuPDF
- Extraction LLM is used to convert the extracted text into a structured format (i.e. as a baseline gpt-4o-mini)
2. Comparison Model
- Both application text and job position text are ingested into a versioned prompt and fed into the comparison LLM
- Any LLM can be used, we tested with different OpenAI, Bedrock, and open-source fine-tuned models hosted on Ollama
3. Reasoning Guardrail
- The reason for the hiring decision is fed into a Guardrail LLM that checks whether it strictly follows from the application, position
- If it detects a hallucination it will go back to the comparison step and force another decision
- If it detects a hallucination for a second time it will involve a human expert to validate the decision and reason
- We use our built-in hallucination scorer from Weave based on gpt-4o-mini but have also tried local models and specialized models such as Selene from Atla.
🧪 Development Workflow
This section focuses on the end-to-end development process.
1. Dataset Generation
2. System Development
3. Fine-tuning of the comparison model
📈 Evaluation and Monitoring
This focuses on the system evaluation of our hiring agent system and the model evaluation during fine-tuning.
1. System Evaluation in W&B Weave
2. Model Evaluation in W&B Models
3. Production Monitoring
🧠 Conclusion and Next Steps
As AI adoption accelerates, regulatory compliance becomes not merely an obligation but a strategic imperative. The EU AI Act demands rigorous processes and comprehensive documentation, making the right choice of supporting technology crucial for organizations navigating these requirements. Weights & Biases stands out by offering robust, integrated tools that simplify compliance tasks—enabling teams to effectively manage risk, maintain transparency, and deliver trustworthy AI solutions. By leveraging the Weights & Biases AI developer platform, organizations can transform regulatory compliance into an advantage, ensuring continuous innovation while upholding the highest standards of safety and ethical responsibility.
- Roll out AI literacy training for your workforce to identify obligations and demonstrate compliance
- Create an inventory of AI systems in your company, classify their risk class, and identify obligations with a service provider such as the appliedAI Initiative.
- Setup your compliant AI operating model to define your regulatory strategy, governance processes, roles & responsibilities with a service provider such as appliedAI Initiative.
- Standardize your compliance processes through the Weights & Biases observability and governance platform.
- Automate reporting and enforce standards using Weights & Biases. Contact us for a demo.
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