diff --git a/examples/mmc_queue/README.md b/examples/mmc_queue/README.md
new file mode 100644
index 00000000..4587ad3f
--- /dev/null
+++ b/examples/mmc_queue/README.md
@@ -0,0 +1,53 @@
+# M/M/c Queue
+A classic [M/M/c queue](https://en.wikipedia.org/wiki/M/M/c_queue) implemented as a pure discrete event simulation in Mesa.
+
+## Summary
+Customers arrive according to a Poisson process (rate λ) and join a single FIFO queue. *c* servers each draw from the queue independently, with exponentially distributed service times (rate μ per server). This is the standard multi-server queuing model from operations research, with well-known analytical solutions for validation.
+
+### Mesa DES features demonstrated
+
+| Feature | Usage |
+|---|---|
+| `schedule_recurring` | Stochastic customer arrivals (exponential inter-arrival times) |
+| `schedule_event(after=...)` | Scheduling service completions |
+| `run_until` | Running a pure event-driven simulation to a target time |
+| Dynamic agent lifecycle | Customers created on arrival, `remove()`d after service |
+
+The model disables Mesa's default step schedule (`_default_schedule.stop()`) and is driven entirely by events.
+
+### Design: server-centric
+Servers are active agents. When a server completes service, it checks the queue and pulls the next customer itself — a natural ABM pattern, in contrast to the system-centric routing common in traditional DES.
+
+## How to run
+```bash
+python model.py
+```
+
+Runs the simulation for 10,000 time units and prints simulated vs. analytical steady-state metrics.
+
+## Files
+
+| File | Description |
+|---|---|
+| `agents.py` | `Customer` and `Server` agents |
+| `model.py` | `MMcQueue` model |
+| `analytical_mmc.py` | Erlang C closed-form solutions for validation |
+
+## Analytical validation
+For a stable M/M/c system (traffic intensity $ρ = λ/(cμ) < 1$), closed-form results exist via the Erlang C formula. The model includes `analytical_mmc()` to compute these, so simulation output can be compared directly:
+
+```
+M/M/3 Queue (λ=2.0, μ=1.0, T=10000.0)
+Customers served: 19992
+
+Metric                     Simulated   Analytical
+---------------------------------------------------
+Server utilization              0.6672       0.6667
+Avg wait time                   0.3716       0.3750
+Avg system time                 1.3716       1.3750
+```
+
+Results converge to analytical values as simulation time increases.
+
+## Visualisation
+There's no visualization yet, but an `app.py` implementation would be appreciated!
diff --git a/examples/mmc_queue/agents.py b/examples/mmc_queue/agents.py
new file mode 100644
index 00000000..f2e8316d
--- /dev/null
+++ b/examples/mmc_queue/agents.py
@@ -0,0 +1,74 @@
+"""Customer and Server agents for the M/M/c queue model."""
+
+from mesa import Agent
+
+
+class Customer(Agent):
+    """A customer in the queuing system.
+
+    Created on arrival, removed after service. Tracks timestamps
+    for wait time and system time calculations.
+    """
+
+    def __init__(self, model):
+        super().__init__(model)
+        self.arrival_time = model.time
+        self.service_start_time = None
+        self.service_end_time = None
+
+    @property
+    def wait_time(self):
+        """Time spent waiting in queue before service began."""
+        if self.service_start_time is None:
+            return None
+        return self.service_start_time - self.arrival_time
+
+    @property
+    def system_time(self):
+        """Total time in the system (wait + service)."""
+        if self.service_end_time is None:
+            return None
+        return self.service_end_time - self.arrival_time
+
+
+class Server(Agent):
+    """A server that pulls customers from the queue when idle.
+
+    Server-centric design: after completing service, the server
+    checks the queue and pulls the next customer itself.
+    """
+
+    def __init__(self, model, service_rate):
+        super().__init__(model)
+        self.service_rate = service_rate
+        self.current_customer = None
+        self.busy_time = 0.0
+        self._service_started_at = None
+
+    @property
+    def is_idle(self):
+        return self.current_customer is None
+
+    def start_service(self, customer):
+        """Begin serving a customer."""
+        customer.service_start_time = self.model.time
+        self.current_customer = customer
+        self._service_started_at = self.model.time
+
+        duration = self.model.rng.exponential(1.0 / self.service_rate)
+        self.model.schedule_event(self._complete_service, after=duration)
+
+    def _complete_service(self):
+        """Complete service and try to pull next customer from queue."""
+        customer = self.current_customer
+        customer.service_end_time = self.model.time
+        self.busy_time += self.model.time - self._service_started_at
+
+        self.model._record_departure(customer)
+        customer.remove()
+
+        # Server-centric: actively pull from queue
+        self.current_customer = None
+        self._service_started_at = None
+        if self.model.queue:
+            self.start_service(self.model.queue.popleft())
diff --git a/examples/mmc_queue/analytical_mmc.py b/examples/mmc_queue/analytical_mmc.py
new file mode 100644
index 00000000..c1149a50
--- /dev/null
+++ b/examples/mmc_queue/analytical_mmc.py
@@ -0,0 +1,29 @@
+from math import factorial
+
+
+def analytical_mmc(arrival_rate, service_rate, c):
+    """Compute analytical M/M/c steady-state metrics using the Erlang C formula.
+
+    Returns:
+        dict with analytical metrics, or None if system is unstable (rho >= 1).
+    """
+    rho = arrival_rate / (c * service_rate)
+    if rho >= 1.0:
+        return None
+
+    a = arrival_rate / service_rate  # offered load
+
+    sum_terms = sum(a**k / factorial(k) for k in range(c))
+    last_term = (a**c / factorial(c)) * (1 / (1 - rho))
+    p0 = 1.0 / (sum_terms + last_term)
+
+    erlang_c = (a**c / factorial(c)) * (1 / (1 - rho)) * p0
+
+    return {
+        "utilization": rho,
+        "avg_wait_time": erlang_c / (c * service_rate * (1 - rho)),
+        "avg_system_time": erlang_c / (c * service_rate * (1 - rho))
+        + 1.0 / service_rate,
+        "avg_queue_length": erlang_c * rho / (1 - rho),
+        "prob_queuing": erlang_c,
+    }
diff --git a/examples/mmc_queue/model.py b/examples/mmc_queue/model.py
new file mode 100644
index 00000000..07e9af5c
--- /dev/null
+++ b/examples/mmc_queue/model.py
@@ -0,0 +1,132 @@
+"""M/M/c queuing model: a pure discrete event simulation in Mesa.
+
+Demonstrates schedule_recurring (arrivals), schedule_event (service),
+and run_until — no step() needed.
+"""
+
+from collections import deque
+
+from mesa import Model
+from mesa.time import Schedule
+
+try:
+    from .agents import Customer, Server
+except ImportError:
+    from agents import Customer, Server
+
+
+class MMcQueue(Model):
+    """M/M/c queuing system.
+
+    Args:
+        arrival_rate: Mean arrival rate (λ). Customers per time unit.
+        service_rate: Mean service rate per server (μ).
+        n_servers: Number of servers (c).
+        rng: Random number generator seed.
+    """
+
+    def __init__(self, arrival_rate=1.0, service_rate=0.5, n_servers=2, **kwargs):
+        super().__init__(**kwargs)
+        self.arrival_rate = arrival_rate
+        self.service_rate = service_rate
+        self.n_servers = n_servers
+
+        # Queue
+        self.queue = deque()
+
+        # Metrics
+        self.customers_served = 0
+        self.total_wait_time = 0.0
+        self.total_system_time = 0.0
+
+        # Create servers
+        self.servers = [Server(self, service_rate) for _ in range(n_servers)]
+
+        # Disable default step schedule — pure DES
+        self._default_schedule.stop()
+
+        # Schedule stochastic arrivals
+        self.schedule_recurring(
+            self._customer_arrival,
+            Schedule(
+                interval=lambda m: m.rng.exponential(1.0 / m.arrival_rate),
+                start=0.0,
+            ),
+        )
+
+    def _customer_arrival(self):
+        """Handle a customer arrival."""
+        customer = Customer(self)
+
+        for server in self.servers:
+            if server.is_idle:
+                server.start_service(customer)
+                return
+
+        self.queue.append(customer)
+
+    def _record_departure(self, customer):
+        """Record metrics for a departing customer."""
+        self.customers_served += 1
+        self.total_wait_time += customer.wait_time
+        self.total_system_time += customer.system_time
+
+    # --- Metrics ---
+
+    @property
+    def avg_wait_time(self):
+        if self.customers_served == 0:
+            return 0.0
+        return self.total_wait_time / self.customers_served
+
+    @property
+    def avg_system_time(self):
+        if self.customers_served == 0:
+            return 0.0
+        return self.total_system_time / self.customers_served
+
+    @property
+    def server_utilization(self):
+        if self.time == 0:
+            return 0.0
+        return sum(s.busy_time for s in self.servers) / (self.n_servers * self.time)
+
+    @property
+    def current_queue_length(self):
+        return len(self.queue)
+
+
+if __name__ == "__main__":
+    try:
+        from .analytical_mmc import analytical_mmc
+    except ImportError:
+        from analytical_mmc import analytical_mmc
+
+    ARRIVAL_RATE = 2.0
+    SERVICE_RATE = 1.0
+    N_SERVERS = 3
+    SIM_TIME = 10_000.0
+
+    model = MMcQueue(
+        arrival_rate=ARRIVAL_RATE,
+        service_rate=SERVICE_RATE,
+        n_servers=N_SERVERS,
+        rng=42,
+    )
+    model.run_until(SIM_TIME)
+
+    analytical = analytical_mmc(ARRIVAL_RATE, SERVICE_RATE, N_SERVERS)
+
+    print(f"M/M/{N_SERVERS} Queue (λ={ARRIVAL_RATE}, μ={SERVICE_RATE}, T={SIM_TIME})")
+    print(f"Customers served: {model.customers_served}\n")
+    print(f"{'Metric':<25} {'Simulated':>12} {'Analytical':>12}")
+    print("-" * 51)
+    print(
+        f"{'Server utilization':<25} {model.server_utilization:>12.4f} {analytical['utilization']:>12.4f}"
+    )
+    print(
+        f"{'Avg wait time':<25} {model.avg_wait_time:>12.4f} {analytical['avg_wait_time']:>12.4f}"
+    )
+    print(
+        f"{'Avg system time':<25} {model.avg_system_time:>12.4f} {analytical['avg_system_time']:>12.4f}"
+    )