Problem
Recruitment conversations are multi-step and stateful. The agent must answer questions, collect candidate details, schedule next steps, and call backend tools without hallucinating job facts or skipping required fields.
Professional Case Study · Agent Systems
AI Recruitment Agent
A company-owned agent system for recruitment workflows that manages conversation state, detects intent, invokes tools, retrieves grounded context, returns structured outputs, and avoids unsupported job or scheduling claims.
Confidentiality: Source code and internal implementation details are private. This case study describes my contribution and general engineering decisions without disclosing proprietary information.
Candidate message
↓
Intent detection + conversation state
↓
RAG context + deterministic tools
↓
Structured response + next action
Key Decisions
- Represent agent output as typed structured responses instead of free-form text.
- Keep scheduling, profile updates, and search operations behind explicit tools.
- Use retrieval only for approved job and FAQ context.
- Track conversation state so follow-up messages can complete previous intents.
Failure Handling
- Unsupported or low-confidence intents fall back to clarification.
- Tool failures return recoverable messages and preserve the conversation state.
- Missing required fields are requested before any scheduling or submission action.
- Guardrails prevent invented job details when retrieval has no supporting context.
Trade-offs
- Structured outputs add schema design work but make downstream automation safer.
- Tool calling is slower than direct text responses, but keeps side effects auditable.
- Strict grounding can make the agent less chatty, but much more reliable for recruiting workflows.
Results
The system brings state, tools, retrieval, schemas, fallback behavior, and model boundaries into an operational recruitment workflow.