AI Agent for Employee Surveys: Distribution, Collection, and Analysis

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

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.

@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

@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

@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.

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#EmployeeSurveys #SentimentAnalysis #EmployeeEngagement #HRAnalytics #AgenticAI #LearnAI #AIEngineering