--- title: "AI Agent for Parks and Recreation: Program Registration, Facility Booking, and Event Info" description: "Build an AI agent for municipal parks and recreation departments that handles program catalog search, class registration, facility reservations, and seasonal event information for community members." canonical: https://callsphere.ai/blog/ai-agent-parks-recreation-program-registration-facility-booking category: "Learn Agentic AI" tags: ["Government AI", "Parks and Recreation", "Program Registration", "Facility Booking", "Community Services"] author: "CallSphere Team" published: 2026-03-17T00:00:00.000Z updated: 2026-05-06T01:02:44.821Z --- # AI Agent for Parks and Recreation: Program Registration, Facility Booking, and Event Info > Build an AI agent for municipal parks and recreation departments that handles program catalog search, class registration, facility reservations, and seasonal event information for community members. ## Parks and Recreation in the Digital Age Municipal parks and recreation departments run hundreds of programs — youth swimming lessons, adult pottery classes, senior fitness programs, summer camps, sports leagues, and community events. They manage facility rentals for pavilions, athletic fields, community rooms, and pools. The catalog changes seasonally, programs fill up fast, and residents want to know what is available, what it costs, and whether there are spots left. Traditional registration systems involve browsing a PDF catalog or navigating a clunky web portal. An AI agent can provide a conversational interface: "What swim classes are available for my 6-year-old on Tuesdays?" gets an immediate, filtered answer instead of a 20-minute search through a 50-page catalog. ## Modeling the Program Catalog Parks and rec programs have rich metadata: age ranges, schedules, locations, instructors, skill levels, fees, and availability. We model this comprehensively so the agent can filter effectively. ```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 from dataclasses import dataclass, field from datetime import date, time from enum import Enum class AgeGroup(Enum): TODDLER = "toddler" # 2-4 YOUTH = "youth" # 5-12 TEEN = "teen" # 13-17 ADULT = "adult" # 18-54 SENIOR = "senior" # 55+ ALL_AGES = "all_ages" class Season(Enum): SPRING = "spring" # Mar-May SUMMER = "summer" # Jun-Aug FALL = "fall" # Sep-Nov WINTER = "winter" # Dec-Feb @dataclass class Program: program_id: str name: str category: str # swimming, arts, fitness, sports, camps description: str age_group: AgeGroup min_age: int max_age: int skill_level: str # beginner, intermediate, advanced, all instructor: str location: str days_of_week: list[str] # ["Tuesday", "Thursday"] start_time: time end_time: time start_date: date end_date: date season: Season fee: float resident_fee: float # discounted rate for city residents max_enrollment: int current_enrollment: int waitlist_count: int = 0 materials_included: bool = True prerequisites: list[str] = field(default_factory=list) PROGRAM_CATALOG: list[Program] = [] # Populated from database ``` ## Program Search and Filtering The search engine is the core of the agent. It must handle natural language queries like "Saturday morning art classes for my 8-year-old" and translate them into structured filters. ```python from openai import OpenAI import json client = OpenAI() SEARCH_EXTRACTION_PROMPT = """Extract search filters from the user's query about parks and recreation programs. Return JSON with any of these fields (omit fields not mentioned): - "category": string (swimming, arts, fitness, sports, camps, dance, music) - "age": integer (child's age) - "days": list of day names - "time_preference": "morning" | "afternoon" | "evening" - "skill_level": "beginner" | "intermediate" | "advanced" - "season": "spring" | "summer" | "fall" | "winter" - "max_fee": float - "keyword": string (free text search term) """ def extract_search_filters(user_query: str) -> dict: """Use LLM to extract structured filters from natural language.""" response = client.chat.completions.create( model="gpt-4o", messages=[ {"role": "system", "content": SEARCH_EXTRACTION_PROMPT}, {"role": "user", "content": user_query}, ], response_format={"type": "json_object"}, temperature=0.0, ) return json.loads(response.choices[0].message.content) def search_programs( filters: dict, catalog: list[Program] = None, ) -> list[Program]: """Search the program catalog using extracted filters.""" results = catalog or PROGRAM_CATALOG if "category" in filters: cat = filters["category"].lower() results = [p for p in results if cat in p.category.lower()] if "age" in filters: age = filters["age"] results = [p for p in results if p.min_age = 17] if "skill_level" in filters: level = filters["skill_level"].lower() results = [ p for p in results if p.skill_level.lower() in (level, "all") ] if "max_fee" in filters: max_fee = filters["max_fee"] results = [p for p in results if p.resident_fee = p.max_enrollment, # full programs last p.start_date, )) return results ``` ## Registration Flow Once a resident finds a program, the agent handles the registration process including eligibility checks, fee calculation, and waitlist management. ```python from datetime import datetime import uuid @dataclass class Registration: registration_id: str program_id: str participant_name: str participant_age: int guardian_name: str | None = None guardian_email: str = "" guardian_phone: str = "" fee_charged: float = 0.0 is_resident: bool = True status: str = "confirmed" # confirmed, waitlisted, cancelled registered_at: datetime = field(default_factory=datetime.utcnow) waitlist_position: int | None = None def register_for_program( program: Program, participant_name: str, participant_age: int, is_resident: bool = True, guardian_name: str | None = None, ) -> Registration: """Register a participant for a program.""" # Age eligibility check if not (program.min_age = program.max_enrollment: # Add to waitlist program.waitlist_count += 1 return Registration( registration_id=str(uuid.uuid4())[:8], program_id=program.program_id, participant_name=participant_name, participant_age=participant_age, guardian_name=guardian_name, fee_charged=0, # no charge until off waitlist is_resident=is_resident, status="waitlisted", waitlist_position=program.waitlist_count, ) # Confirm registration program.current_enrollment += 1 return Registration( registration_id=str(uuid.uuid4())[:8], program_id=program.program_id, participant_name=participant_name, participant_age=participant_age, guardian_name=guardian_name, fee_charged=fee, is_resident=is_resident, status="confirmed", ) ``` ## Facility Booking System Beyond programs, parks departments rent facilities. The agent handles availability checking and reservation creation. ```python @dataclass class Facility: facility_id: str name: str facility_type: str # pavilion, field, pool, room, gym location: str capacity: int hourly_rate: float resident_hourly_rate: float amenities: list[str] = field(default_factory=list) available_hours: dict[str, str] = field(default_factory=dict) @dataclass class Reservation: reservation_id: str facility_id: str date: date start_time: time end_time: time reserved_by: str purpose: str total_cost: float status: str = "confirmed" def check_facility_availability( facility: Facility, requested_date: date, start: time, end: time, existing_reservations: list[Reservation] = None, ) -> dict: """Check if a facility is available for the requested time.""" conflicts = [] for res in existing_reservations or []: if res.facility_id != facility.facility_id: continue if res.date != requested_date: continue if res.status == "cancelled": continue # Check time overlap if start res.start_time: conflicts.append({ "existing_start": res.start_time.isoformat(), "existing_end": res.end_time.isoformat(), }) hours = ( datetime.combine(requested_date, end) - datetime.combine(requested_date, start) ).seconds / 3600 return { "facility": facility.name, "date": requested_date.isoformat(), "requested_time": f"{start.isoformat()} - {end.isoformat()}", "available": len(conflicts) == 0, "conflicts": conflicts, "estimated_cost": round(facility.resident_hourly_rate * hours, 2), "hours": hours, } ``` ## FAQ ### How does the agent handle scholarship or fee reduction requests for low-income families? Most parks departments offer fee assistance programs. The agent checks whether the resident has an active fee reduction on file and automatically applies the discounted rate during registration. If no reduction is on file, the agent explains the assistance program, lists the eligibility criteria (typically based on income or enrollment in programs like SNAP or free school lunch), and provides the application form. The agent never asks for proof of income directly — it directs the resident to the fee assistance application process, which is handled by department staff with proper privacy controls. ### What happens when a program is full and a resident wants to be notified of openings? The agent adds the resident to the program's waitlist and provides their position number. When a spot opens (due to a cancellation or enrollment increase), the system sends an automated notification to the next person on the waitlist. They have 48 hours to confirm their registration before the spot moves to the next person. The agent can also suggest alternative programs with similar content, age range, and schedule that still have openings. ### Can the agent recommend programs based on a child's interests and past enrollments? Yes. The agent builds a participation profile from enrollment history — if a child has taken three swim classes and a diving class, the agent recognizes an interest in aquatic programs. When the parent asks "what should we sign up for this summer," the agent suggests the next skill level in swimming, introduces new aquatic programs like water polo or lifeguard training (if age-appropriate), and also surfaces programs in related categories the family has not tried. Recommendations are transparent: "Based on Sarah's swim history, she may be ready for Intermediate Swim (Tue/Thu 4 PM, $45)." --- #GovernmentAI #ParksAndRecreation #ProgramRegistration #FacilityBooking #CommunityServices #AgenticAI #LearnAI #AIEngineering --- Source: https://callsphere.ai/blog/ai-agent-parks-recreation-program-registration-facility-booking