--- title: "AI Agent for Employee Surveys: Distribution, Collection, and Analysis" description: "Build an AI agent that designs employee surveys, distributes them to targeted groups, collects responses with anonymity controls, and performs sentiment analysis to surface actionable insights for leadership." canonical: https://callsphere.ai/blog/ai-agent-employee-surveys-distribution-collection-analysis category: "Learn Agentic AI" tags: ["Employee Surveys", "Sentiment Analysis", "Employee Engagement", "HR Analytics", "Agentic AI"] author: "CallSphere Team" published: 2026-03-17T00:00:00.000Z updated: 2026-05-30T21:55:44.046Z --- # AI Agent for Employee Surveys: Distribution, Collection, and Analysis > Build an AI agent that designs employee surveys, distributes them to targeted groups, collects responses with anonymity controls, and performs sentiment analysis to surface actionable insights for leadership. ## Why Survey Management Needs AI Employee engagement surveys are only valuable if they are well-designed, widely completed, and thoroughly analyzed. Most organizations struggle on all three fronts: surveys ask vague questions, response rates hover around 30-40%, and the results sit in spreadsheets for weeks before anyone acts on them. An AI survey agent solves each problem — it helps craft targeted questions, sends intelligent reminders, and analyzes responses in real time so leaders can act while the feedback is still fresh. ## Survey Data Model ```python from dataclasses import dataclass, field from datetime import date, datetime from typing import Optional from enum import Enum from agents import Agent, Runner, function_tool import json class QuestionType(Enum): LIKERT = "likert" # 1-5 scale MULTIPLE_CHOICE = "multiple_choice" FREE_TEXT = "free_text" NPS = "nps" # 0-10 Net Promoter Score @dataclass class SurveyQuestion: question_id: str text: str question_type: QuestionType options: list[str] = field(default_factory=list) required: bool = True @dataclass class Survey: survey_id: str title: str description: str questions: list[SurveyQuestion] target_audience: str # "all", "engineering", "managers", etc. anonymous: bool = True start_date: date = field(default_factory=date.today) end_date: Optional[date] = None responses: list[dict] = field(default_factory=list) SURVEY_DB: dict[str, Survey] = {} ``` ## Survey Design Tool The design tool helps HR create effective surveys by suggesting evidence-based question structures and preventing common pitfalls like double-barreled questions or leading phrasing. ```mermaid flowchart LR INPUT(["User intent"]) PARSE["Parse plus classify"] PLAN["Plan and tool selection"] AGENT["Agent loop LLM plus tools"] GUARD{"Guardrails and policy"} EXEC["Execute and verify result"] OBS[("Trace and metrics")] OUT(["Outcome plus next action"]) INPUT --> PARSE --> PLAN --> AGENT --> GUARD GUARD -->|Pass| EXEC --> OUT GUARD -->|Fail| AGENT AGENT --> OBS style AGENT fill:#4f46e5,stroke:#4338ca,color:#fff style GUARD fill:#f59e0b,stroke:#d97706,color:#1f2937 style OBS fill:#ede9fe,stroke:#7c3aed,color:#1e1b4b style OUT fill:#059669,stroke:#047857,color:#fff ``` ```python @function_tool def create_survey( title: str, description: str, target_audience: str, topics: list[str], anonymous: bool = True, ) -> str: """Create a survey with auto-generated questions for specified topics.""" topic_templates = { "engagement": [ SurveyQuestion("q1", "I feel motivated to go above and beyond at work.", QuestionType.LIKERT), SurveyQuestion("q2", "I would recommend this company as a great place to work.", QuestionType.NPS), SurveyQuestion("q3", "What would make your work experience better?", QuestionType.FREE_TEXT, required=False), ], "management": [ SurveyQuestion("q4", "My manager provides clear expectations.", QuestionType.LIKERT), SurveyQuestion("q5", "I receive regular, helpful feedback.", QuestionType.LIKERT), SurveyQuestion("q6", "How could your manager better support you?", QuestionType.FREE_TEXT, required=False), ], "work_life_balance": [ SurveyQuestion("q7", "I can maintain a healthy work-life balance.", QuestionType.LIKERT), SurveyQuestion("q8", "What is the biggest barrier to work-life balance?", QuestionType.MULTIPLE_CHOICE, options=["Meeting overload", "Unclear priorities", "After-hours messages", "Workload volume", "Other"]), ], } questions = [] for topic in topics: qs = topic_templates.get(topic.lower(), []) questions.extend(qs) if not questions: return json.dumps({"error": f"Unknown topics: {topics}. " "Available: engagement, management, work_life_balance"}) survey_id = f"SRV-{len(SURVEY_DB) + 1:04d}" survey = Survey( survey_id=survey_id, title=title, description=description, questions=questions, target_audience=target_audience, anonymous=anonymous, ) SURVEY_DB[survey_id] = survey return json.dumps({ "survey_id": survey_id, "title": title, "question_count": len(questions), "target": target_audience, "anonymous": anonymous, }) ``` ## Response Collection and Tracking ```python @function_tool def submit_survey_response( survey_id: str, respondent_id: str, answers: str, ) -> str: """Submit a survey response. Answers is a JSON string mapping question_id to answer.""" survey = SURVEY_DB.get(survey_id) if not survey: return json.dumps({"error": "Survey not found"}) parsed_answers = json.loads(answers) # Validate required questions are answered required_ids = {q.question_id for q in survey.questions if q.required} answered_ids = set(parsed_answers.keys()) missing = required_ids - answered_ids if missing: return json.dumps({"error": f"Missing required answers: {list(missing)}"}) response_record = { "respondent": "anonymous" if survey.anonymous else respondent_id, "submitted_at": datetime.now().isoformat(), "answers": parsed_answers, } survey.responses.append(response_record) return json.dumps({"status": "submitted", "survey_id": survey_id}) @function_tool def get_survey_participation(survey_id: str) -> str: """Get participation statistics for a survey.""" survey = SURVEY_DB.get(survey_id) if not survey: return json.dumps({"error": "Survey not found"}) # Simulated total target count target_counts = {"all": 500, "engineering": 80, "managers": 45} total_target = target_counts.get(survey.target_audience, 100) response_count = len(survey.responses) rate = round(response_count / total_target * 100, 1) if total_target else 0 return json.dumps({ "survey": survey.title, "responses": response_count, "target_population": total_target, "participation_rate": f"{rate}%", "status": "healthy" if rate >= 70 else "needs_nudge" if rate >= 40 else "low", }) ``` ## Sentiment Analysis Tool ```python @function_tool def analyze_survey_results(survey_id: str) -> str: """Analyze survey responses with aggregated scores and sentiment breakdown.""" survey = SURVEY_DB.get(survey_id) if not survey: return json.dumps({"error": "Survey not found"}) if not survey.responses: return json.dumps({"message": "No responses to analyze yet"}) analysis = {"survey": survey.title, "total_responses": len(survey.responses)} question_results = [] for question in survey.questions: answers = [ r["answers"].get(question.question_id) for r in survey.responses if question.question_id in r["answers"] ] if question.question_type == QuestionType.LIKERT: numeric = [a for a in answers if isinstance(a, (int, float))] if numeric: avg = sum(numeric) / len(numeric) question_results.append({ "question": question.text, "type": "likert", "average": round(avg, 2), "sentiment": "positive" if avg >= 4 else "neutral" if avg >= 3 else "negative", "response_count": len(numeric), }) elif question.question_type == QuestionType.NPS: numeric = [a for a in answers if isinstance(a, (int, float))] if numeric: promoters = sum(1 for a in numeric if a >= 9) / len(numeric) * 100 detractors = sum(1 for a in numeric if a <= 6) / len(numeric) * 100 nps = round(promoters - detractors) question_results.append({ "question": question.text, "type": "nps", "nps_score": nps, "promoters_pct": round(promoters), "detractors_pct": round(detractors), }) analysis["questions"] = question_results return json.dumps(analysis) survey_agent = Agent( name="SurveyBot", instructions="""You are SurveyBot, an employee survey assistant. Help HR teams design surveys, track participation, and analyze results. When creating surveys, suggest evidence-based question formats. Always maintain respondent anonymity when surveys are marked anonymous. Present results with actionable insights, not just raw numbers.""", tools=[create_survey, submit_survey_response, get_survey_participation, analyze_survey_results], ) ``` ## FAQ ### How do you maintain anonymity while still tracking participation? Use a two-table approach: one table records which employees have submitted (for participation tracking and reminders), and a separate table stores the actual responses without any employee identifier. The agent never joins these tables, so individual responses cannot be traced back to specific employees. ### What response rate should an organization target? A response rate of 70% or higher is considered strong. Below 40%, results may not be representative. The agent monitors participation in real time and can send targeted reminders to departments with low completion rates without revealing who specifically has not responded. ### How do you handle free-text responses at scale? The agent uses natural language processing to cluster free-text responses by theme and sentiment. Rather than reading 500 individual comments, leadership sees aggregated themes like "meeting overload mentioned 47 times with negative sentiment" alongside representative anonymized quotes. --- #EmployeeSurveys #SentimentAnalysis #EmployeeEngagement #HRAnalytics #AgenticAI #LearnAI #AIEngineering --- Source: https://callsphere.ai/blog/ai-agent-employee-surveys-distribution-collection-analysis