Modularizing the provided framework for scalability and reliability will involve breaking down the overall architecture into smaller, more manageable pieces, as well as introducing additional features and capabilities to enhance reliability. Here's a list of ideas to achieve this:

### 1. Dynamic Agent Management

To ensure the swarm is both cost-effective and efficient, dynamically creating and destroying agents depending on the workload can be a game changer:

**Idea**: Instead of having a fixed number of agents, allow the `AutoScaler` to both instantiate and destroy agents as necessary.

**Example**: 
```python
class AutoScaler:
    # ...
    def remove_agent(self):
        with self.lock:
            if self.agents_pool:
                agent_to_remove = self.agents_pool.pop()
                del agent_to_remove
```

### 2. Task Segmentation & Aggregation

Breaking down tasks into sub-tasks and then aggregating results ensures scalability:

**Idea**: Create a method in the `Orchestrator` to break down larger tasks into smaller tasks and another method to aggregate results from sub-tasks.

**Example**:
```python
class Orchestrator(ABC):
    # ...
    def segment_task(self, main_task: str) -> List[str]:
        # Break down main_task into smaller tasks
        # ...
        return sub_tasks
    
    def aggregate_results(self, sub_results: List[Any]) -> Any:
        # Combine results from sub-tasks into a cohesive output
        # ...
        return main_result
```

### 3. Enhanced Task Queuing

**Idea**: Prioritize tasks based on importance or deadlines.

**Example**: Use a priority queue for the `task_queue`, ensuring tasks of higher importance are tackled first.

### 4. Error Recovery & Retry Mechanisms

**Idea**: Introduce a retry mechanism for tasks that fail due to transient errors.

**Example**:
```python
class Orchestrator(ABC):
    MAX_RETRIES = 3
    retry_counts = defaultdict(int)
    # ...
    def assign_task(self, agent_id, task):
        # ...
        except Exception as error:
            if self.retry_counts[task] < self.MAX_RETRIES:
                self.retry_counts[task] += 1
                self.task_queue.put(task)
```

### 5. Swarm Communication & Collaboration

**Idea**: Allow agents to communicate or request help from their peers.

**Example**: Implement a `request_assistance` method within agents where, upon facing a challenging task, they can ask for help from other agents.

### 6. Database Management

**Idea**: Periodically clean, optimize, and back up the vector database to ensure data integrity and optimal performance.

### 7. Logging & Monitoring

**Idea**: Implement advanced logging and monitoring capabilities to provide insights into swarm performance, potential bottlenecks, and failures.

**Example**: Use tools like Elasticsearch, Logstash, and Kibana (ELK stack) to monitor logs in real-time.

### 8. Load Balancing

**Idea**: Distribute incoming tasks among agents evenly, ensuring no single agent is overloaded.

**Example**: Use algorithms or tools that assign tasks based on current agent workloads.

### 9. Feedback Loop

**Idea**: Allow the system to learn from its mistakes or inefficiencies. Agents can rate the difficulty of their tasks and this information can be used to adjust future task assignments.

### 10. Agent Specialization

**Idea**: Not all agents are equal. Some might be better suited to certain tasks. 

**Example**: Maintain a performance profile for each agent, categorizing them based on their strengths. Assign tasks to agents based on their specialization for optimal performance.

By implementing these ideas and constantly iterating based on real-world usage and performance metrics, it's possible to create a robust and scalable multi-agent collaboration framework.