Interviewer Case Study

Case Study 3: Interviewer

Headline: Agentic Design & Cognitive Architectures

The Challenge: Traditional chatbots are passive; they wait for a user to ask a question. However, many business processes—like technical recruiting, intake protocols, or diagnostic interviews—require the AI to drive the conversation. The system needs to maintain state, remember context across a long session, and make decisions about what to ask next based on the user’s previous answers.

The Solution: “Interviewer” is a demonstration of Agentic AI using LangGraph on Google Cloud. Unlike a stateless RAG chain, this system uses a Finite State Machine (FSM) to model the cognitive loop of a technical recruiter. It does not just generate text; it evaluates inputs, updates its internal state, and decides on the next optimal move.

Key Technical Innovations:

• Cyclic Cognitive Graph: Instead of a linear script, I implemented a cyclic graph architecture where an “Evaluator” node critiques the candidate’s response silently before the “Interviewer” node generates the next question. This creates a feedback loop that mimics human reasoning.
• Serverless State Management: To maintain conversation history without managing heavy infrastructure, I integrated Firestore as a serverless, persistent memory store. This allows the agent to “remember” details from the beginning of the interview and reference them later.
• Structured Outputs: The system enforces structured JSON outputs for internal reasoning, ensuring that the transition between interview stages (e.g., Introduction -> Technical Deep Dive -> Coding Challenge) is deterministic and reliable.

The Outcome: A stateful, goal-oriented agent that can autonomously execute a complex business process. This architecture is the foundation for building “digital employees” that can handle intake, triage, and structured data collection tasks without human intervention.