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/evaluator-optimizer/README.md

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+# Evaluator-Optimizer Pattern
+
+This project demonstrates the Evaluator-Optimizer 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).
+
+![Evaluator-Optimizer](https://www.anthropic.com/_next/image?url=https%3A%2F%2Fwww-cdn.anthropic.com%2Fimages%2F4zrzovbb%2Fwebsite%2F14f51e6406ccb29e695da48b17017e899a6119c7-2401x1000.png&w=3840&q=75)
+
+## Overview
+
+The Evaluator-Optimizer pattern implements a dual-LLM process where one model generates responses while another provides evaluation and feedback in an iterative loop, similar to a human writer's iterative refinement process. The pattern consists of two main components:
+
+- **Generator LLM**: Produces initial responses and refines them based on feedback
+- **Evaluator LLM**: Analyzes responses and provides detailed feedback for improvement
+
+## How It Works
+
+1. The generator LLM produces an initial solution for the given task
+2. The evaluator LLM assesses the solution against quality criteria
+3. If the solution passes evaluation, it's returned as the final result
+4. If improvements are needed, feedback is incorporated into a new generation cycle
+5. The process repeats until a satisfactory solution is achieved
+
+```mermaid
+graph TD
+    Task[Task Input] --> G[Generator LLM]
+    G --> E[Evaluator LLM]
+    E -->|PASS| Out[Final Output]
+    E -->|NEEDS_IMPROVEMENT| F[Feedback]
+    F --> G
+```
+
+## When to Use
+
+This pattern is particularly effective when:
+
+- Clear evaluation criteria exist for assessing response quality
+- Iterative refinement provides measurable value to the output
+- Tasks benefit from multiple rounds of critique and improvement
+
+### Example Applications
+
+- Literary translation requiring capture of subtle nuances
+- Complex search tasks needing multiple rounds of searching and analysis
+- Code generation where quality can be improved through systematic review
+- Content creation requiring multiple drafts and specific improvements
+
+## Implementation
+
+The implementation uses Spring AI's ChatClient for LLM interactions and consists of:
+
+```java
+public class EvaluatorOptimizer {
+    public RefinedResponse loop(String task) {
+        // 1. Generate initial solution
+        Generation generation = generate(task, context);
+        
+        // 2. Evaluate the solution
+        EvaluationResponse evaluation = evaluate(generation.response(), task);
+        
+        // 3. If PASS, return solution
+        // 4. If NEEDS_IMPROVEMENT, incorporate feedback and generate new solution
+        // 5. Repeat until satisfactory
+        return new RefinedResponse(finalSolution, chainOfThought);
+    }
+}
+```
+
+### Usage Example
+
+```java
+ChatClient chatClient = // ... initialize chat client
+EvaluatorOptimizer agent = new EvaluatorOptimizer(chatClient);
+
+// Process a task
+RefinedResponse response = agent.loop(
+    "Create a Java class implementing a thread-safe counter"
+);
+
+// Access results
+System.out.println("Final Solution: " + response.solution());
+System.out.println("Evolution: " + response.chainOfThought());
+```
+
+## Customization
+
+The pattern can be customized through:
+
+1. **Custom Prompts**: Provide specialized prompts for generator and evaluator
+```java
+agent = new EvaluatorOptimizer(
+    chatClient,
+    customGeneratorPrompt,
+    customEvaluatorPrompt
+);
+```
+
+2. **Default Templates**: Modify the default prompts for common use cases
+   - `DEFAULT_GENERATOR_PROMPT`: Template for solution generation
+   - `DEFAULT_EVALUATOR_PROMPT`: Template for solution evaluation
+
+## Response Formats
+
+### Generation Response
+```json
+{
+    "thoughts": "Brief description of approach",
+    "response": "Actual solution content"
+}
+```
+
+### Evaluation Response
+```json
+{
+    "evaluation": "PASS|NEEDS_IMPROVEMENT|FAIL",
+    "feedback": "Detailed feedback for improvement"
+}
+```
+
+## Dependencies
+
+- Spring AI
+- Spring Boot
+- Java 17 or later
+
+## References
+
+- [Building Effective Agents (Anthropic Research)](https://www.anthropic.com/research/building-effective-agents)