refactor: clarify agent pattern names

- Remove redundant workflow suffix from agentic pattern names and related classes
- Update documentation to reflect pattern name changes

agentic-patterns/orchestrator-workers/README.md

@@ -0,0 +1,85 @@
+# Orchestrator-Workers Workflow Pattern
+
+This project demonstrates the Orchestrator-Workers workflow pattern for building effective LLM-based systems, as described in [Anthropic's research on building effective agents](https://www.anthropic.com/research/building-effective-agents).
+
+![Orchestration Workflow](https://www.anthropic.com/_next/image?url=https%3A%2F%2Fwww-cdn.anthropic.com%2Fimages%2F4zrzovbb%2Fwebsite%2F8985fc683fae4780fb34eab1365ab78c7e51bc8e-2401x1000.png&w=3840&q=75)
+
+## Overview
+
+The Orchestrator-Workers pattern is a flexible approach for handling complex tasks that require dynamic task decomposition and specialized processing. It consists of three main components:
+
+- **Orchestrator**: A central LLM that analyzes tasks and determines required subtasks
+- **Workers**: Specialized LLMs that execute specific subtasks
+- **Synthesizer**: Component that combines worker outputs into a final result
+
+## When to Use
+
+This pattern is particularly effective for:
+
+- Complex tasks where subtasks can't be predicted upfront
+- Tasks requiring different approaches or perspectives
+- Situations needing adaptive problem-solving
+- Tasks benefiting from specialized processing
+
+
+## Implementation
+
+The implementation uses Spring AI's ChatClient for LLM interactions and consists of:
+
+```java
+public class OrchestratorWorkers {
+    public WorkerResponse process(String taskDescription) {
+        // 1. Orchestrator analyzes task and determines subtasks
+        OrchestratorResponse orchestratorResponse = // ...
+
+        // 2. Workers process subtasks in parallel
+        List<String> workerResponses = // ...
+
+        // 3. Results are combined into final response
+        return new WorkerResponse(/*...*/);
+    }
+}
+```
+
+### Usage Example
+
+```java
+ChatClient chatClient = // ... initialize chat client
+OrchestratorWorkers agent = new OrchestratorWorkers(chatClient);
+
+// Process a task
+WorkerResponse response = agent.process(
+    "Generate both technical and user-friendly documentation for a REST API endpoint"
+);
+
+// Access results
+System.out.println("Analysis: " + response.analysis());
+System.out.println("Worker Outputs: " + response.workerResponses());
+```
+
+## Customization
+
+The pattern can be customized through:
+
+1. **Custom Prompts**: Provide specialized prompts for orchestrator and workers
+```java
+agent = new OrchestratorWorkers(
+    chatClient,
+    customOrchestratorPrompt,
+    customWorkerPrompt
+);
+```
+
+2. **Default Templates**: Modify the default prompts for common use cases
+   - `DEFAULT_ORCHESTRATOR_PROMPT`: Template for task analysis
+   - `DEFAULT_WORKER_PROMPT`: Template for worker processing
+
+## Dependencies
+
+- Spring AI
+- Spring Boot
+- Java 17 or later
+
+## References
+
+- [Building Effective Agents (Anthropic Research)](https://www.anthropic.com/research/building-effective-agents)