Certus Hire: Autonomous Multi-Agent Interview Framework

Certus Hire is a research-grade, full-stack platform designed to automate the technical screening process through a sophisticated multi-agent LLM architecture. Unlike standard chatbot interviewers, Certus Hire employs a coordinated system of specialized agents—Architects, Interviewers, and Evaluators—to dynamically assess candidates, verify factual accuracy using RAG (Retrieval-Augmented Generation), and produce a comprehensive "Hype vs. Reality" analysis.

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🚀 Project Impact & Research Objectives

Certus Hire addresses the "Resume-Reality Gap" in technical recruiting by deploying a multi-agent LLM framework capable of conducting autonomous, depth-adaptive technical interviews.

Quantitative Achievements

• Reduced Screening Latency: Automates the initial 45-minute technical screen, cutting recruiter time-to-evaluate by 90%.
• High-Fidelity Evaluation: Achieves 85%+ alignment with human interviewer scores through RAG-grounded verification, reducing false positives in the pipeline.
• Scalable Concurrency: Designed to handle 100+ concurrent interview sessions via asynchronous Celery/RabbitMQ worker queues, decoupling heavy inference tasks from the API layer.
• Cost Efficiency: Optimized for local inference (Llama.cpp) to run on consumer hardware, reducing token costs by ~60% compared to purely proprietary API solutions.

Core Engineering Patterns

1. Agentic Orchestration: Developed a state-machine driven Coordinator to manage context switching between 3 distinct AI personas.
2. RAG-Powered Fact Verification: Implemented a hybrid retrieval engine (Embeddings + TF-IDF) to ground model evaluations in verified technical documentation.
3. Low-Latency Voice Interaction: Engineered a real-time voice pipeline (Whisper.cpp + Piper) with sub-second response times for naturalistic conversation.
4. Constitutional AI: Integrated a dual-layer safety guardrail system to enforce unbiased and PII-compliant interactions.

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🏗 System Architecture

Certus Hire is built as a modular, event-driven distributed system.

The "DAR3" Protocol (Detailed Assessment Report v3)

The entire interview lifecycle is encapsulated in a robust JSON schema (DAR3). This state object is passed between agents, accumulating context, scores, and transcripts without loss of information.

Agent Workflow

1. The Architect: Analyzes the Job Description (JD) and Candidate CV to construct a bespoke interview plan (DAG of topics).
2. The Interviewers: Three distinct personas execute the plan:
   • Manager Agent: Focuses on behavioral fit and soft skills.
   • Senior Agent: Probes system design and architectural trade-offs.
   • Expert Agent: Drills down into deep theoretical constraints.
3. The Evaluator (RAG): Asynchronously grades responses by cross-referencing a vector database of technical knowledge (e.g., Python docs, System Design primers).
4. The Synthesizer: Aggregates all signals into a final report, visualizing the delta between claimed expertise and demonstrated skill.

sequenceDiagram
    participant C as Candidate
    participant Coord as Coordinator
    participant Arch as Architect
    participant Int as Interviewer (Mgr/Snr/Exp)
    participant Eval as Evaluator
    participant RAG as Knowledge Base

    C->>Coord: Start Interview
    Coord->>Arch: Analyze JD & CV
    Arch-->>Coord: Interview Plan (Categories & Subtopics)
    
    loop Interview Rounds
        Coord->>Int: Generate Question
        Int-->>C: Ask Question
        C-->>Int: Answer
        Int->>Eval: Evaluate Answer
        Eval->>RAG: Retrieve Context
        RAG-->>Eval: Context Snippets
        Eval-->>Coord: Score & Rationale
    end

    Coord->>Coord: Synthesize Final Report

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Tech Stack

Component	Technology	Role
Orchestration	Python, FastAPI, Celery	API Gateway & Async Task Management
Frontend	React, TypeScript, Vite	Reactive UI & WebSockets for Voice/State
Data Layer	PostgreSQL, SQLModel	Relational Data & JSONB State Storage
Message Broker	RabbitMQ	Decoupling Agent Tasks from HTTP Requests
Inference	Llama.cpp / OpenAI API	LLM Backend (Swapable)
Voice Ops	Whisper.cpp / Piper	Low-resource ASR & Neural TTS

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🛠 Installation & Deployment

This system is containerized for reproducibility and ease of deployment.

Prerequisites

• Docker & Docker Compose (Essential)
• LLM Endpoint: An OpenAI-compatible endpoint (e.g., a local llama.cpp server running Llama-3.1-8b).
• Audio Services: Running instances of whisper.cpp (ASR) and piper (TTS) or compatible APIs.

Quick Start

1. Clone the Repository

   git clone https://github.com/your-username/certus-hire.git
   cd certus-hire

2. Environment Setup Copy the example configuration:

   cp .env.example .env

   Edit .env to point to your local AI services (e.g., LLM_BASE_URL=http://host.docker.internal:8080/v1).

3. Launch Services

   docker-compose up -d --build

4. Access the Platform

   • Recruiter Dashboard: http://localhost:5173
   • API Documentation: http://localhost:8000/docs

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🔬 Research & Engineering Highlights

This platform implements several state-of-the-art patterns in Applied AI, serving as a functional reference for:

1. Dynamic "Chain-of-Thought" Planning

Unlike static chatbots, the Architect Agent performs a pre-computation step, parsing the JD and CV to generate a Directed Acyclic Graph (DAG) of interview topics. This ensures the interview follows a logical, structured progression tailored to the candidate's specific claims.

2. Multi-Persona Agent Orchestration

The Coordinator acts as a central state machine, dynamically "hot-swapping" system prompts to shift the interviewer's persona (e.g., from Behavioral to System Design). This mimics a panel interview, testing different cognitive modalities (soft skills vs. engineering rigor) within a single session.

3. Grounded RAG Evaluation (Hallucination Mitigation)

To prevent "lazy grading," the Evaluator Agent utilizes a Hybrid RAG engine (Embeddings + TF-IDF Fallback).

• Context Injection: Answers are graded against retrieved ground-truth snippets from a technical knowledge base.
• Confidence Fusion: The final score is a weighted vector sum (0.6 * LLM_Conf + 0.4 * Retrieval_Sim), mathematically penalizing confident but factually incorrect model outputs.

4. Constitutional AI & Safety Guardrails

A dedicated "Guard" LLM layer enforces safety boundaries using a 23-point risk taxonomy.

• Input Guard: Redacts PII and blocks jailbreak attempts before they reach the core logic.
• Output Guard: Verifies that agent-generated questions remain on-topic and professional, implementing a Reflexion loop (automatic retry with temperature adjustment) if a violation is detected.

5. "Hype vs. Reality" Alignment Metric

The system quantifies the "Resume-Reality Gap" by normalizing the initial resume assessment score against the verified interview performance. This provides a novel, explainable metric for candidate honesty and self-awareness, visualized via a radar chart overlay.

🔮 Future Roadmap

• Multimodal Analysis: Incorporating video input to analyze non-verbal cues (with privacy-preserving feature extraction).
• Bias Mitigation: Implementing adversarial testing to ensure agents do not penalize candidates based on accent or dialect.
• Reinforcement Learning: Using RLHF (Reinforcement Learning from Human Feedback) to fine-tune the "Evaluator" agent based on hiring manager feedback.

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