feat: Phase 1 Workflow Enhancements - Observability, API Testing & Validation

.augment/rules/package-source.instructions.md

@@ -1,3 +1,8 @@
+---
+type: "agent_requested"
+description: "Example description"
+---
+
 # TTA.dev - AI Development Toolkit
 
 **Production-quality agentic primitives and workflow patterns for building reliable AI applications.**
@@ -380,14 +385,14 @@ async def execute(self, input_data: dict, context: WorkflowContext) -> dict:
 async def execute(self, input_data: dict, context: WorkflowContext) -> dict:
     """
     Process input with validation.
-    
+
     Args:
         input_data: Data to process
         context: Workflow context
-        
+
     Returns:
         Processed result
-        
+
     Example:
 ```python
         result = await processor.execute({"key": "value"}, context)
@@ -868,11 +873,11 @@ from tta_dev_primitives.core.base import WorkflowContext, WorkflowPrimitive
 
 ## Anti-Patterns to Avoid
 
-❌ Using `pip` instead of `uv`  
-❌ Creating primitives without type hints  
-❌ Skipping tests ("will add later")  
-❌ Global state instead of `WorkflowContext`  
-❌ Modifying code without running quality checks  
+❌ Using `pip` instead of `uv`
+❌ Creating primitives without type hints
+❌ Skipping tests ("will add later")
+❌ Global state instead of `WorkflowContext`
+❌ Modifying code without running quality checks
 ❌ Using `Optional[T]` instead of `T | None`
 # Package Source Code Guidelines
 
@@ -912,14 +917,14 @@ class MyWorkflow(WorkflowPrimitive[InputType, OutputType]):
     async def execute(self, input_data: InputType, context: WorkflowContext) -> OutputType:
         """
         Brief description.
-        
+
         Args:
             input_data: Description
             context: Workflow context for tracing
-            
+
         Returns:
             Description
-            
+
         Example:
 ```python
             workflow = MyWorkflow()
@@ -1004,21 +1009,21 @@ Every public class and method needs Google-style docstrings:
 async def execute(self, input_data: dict, context: WorkflowContext) -> dict:
     """
     Process input data with validation and transformation.
-    
+
     This method validates the input structure, applies transformations,
     and returns the processed result.
-    
+
     Args:
         input_data: Raw input containing 'query' and optional 'params'
         context: Workflow context with session tracking info
-        
+
     Returns:
         Processed data with 'result' and 'metadata' keys
-        
+
     Raises:
         ValidationError: If required fields are missing
         TimeoutError: If processing exceeds configured timeout
-        
+
     Example:
 ```python
         processor = DataProcessor(timeout=5.0)
@@ -1056,7 +1061,7 @@ from pydantic import BaseModel, Field
 
 class InputData(BaseModel):
     """Input structure for processing."""
-    
+
     query: str = Field(..., description="Search query")
     max_results: int = Field(10, ge=1, le=100, description="Maximum results")
     metadata: dict[str, Any] = Field(default_factory=dict)
@@ -1077,21 +1082,21 @@ Before committing, ensure:
 ntext) -> dict:
     """
     Process input data with validation and transformation.
-    
+
     This method validates the input structure, applies transformations,
     and returns the processed result.
-    
+
     Args:
         input_data: Raw input containing 'query' and optional 'params'
         context: Workflow context with session tracking info
-        
+
     Returns:
         Processed data with 'result' and 'metadata' keys
-        
+
     Raises:
         ValidationError: If required fields are missing
         TimeoutError: If processing exceeds configured timeout
-        
+
     Example:
         ```python
         processor = DataProcessor(timeout=5.0)
@@ -1129,7 +1134,7 @@ from pydantic import BaseModel, Field
 
 class InputData(BaseModel):
     """Input structure for processing."""
-    
+
     query: str = Field(..., description="Search query")
     max_results: int = Field(10, ge=1, le=100, description="Maximum results")
     metadata: dict[str, Any] = Field(default_factory=dict)

.augment/rules/scripts.instructions.md

@@ -1,3 +1,8 @@
+---
+type: "agent_requested"
+description: "Example description"
+---
+
 # Scripts Guidelines
 
 ## Core Principle
@@ -62,7 +67,7 @@ def build_workflow(models: list[str]):
             timeout_seconds=30.0
         )
         model_tests.append(inject_name >> test)
-    
+
     # Run all in parallel, cache for 1 hour
     return CachePrimitive(
         ParallelPrimitive(model_tests),
@@ -74,9 +79,9 @@ async def main():
     models = ["phi-4", "qwen-0.5b", "qwen-1.5b"]
     workflow = build_workflow(models)
     context = WorkflowContext(workflow_id="model-eval")
-    
+
     results = await workflow.execute({}, context)
-    
+
     for result in results:
         print(f"{result['model']}: {result['score']}")
 
@@ -132,7 +137,7 @@ def build_startup_workflow(servers: list[str]):
             timeout_seconds=10.0
         )
         server_starts.append(inject_name >> start >> health)
-    
+
     # Start all servers in parallel, then validate
     return SequentialPrimitive([
         ParallelPrimitive(server_starts),
@@ -143,7 +148,7 @@ async def main():
     servers = ["basic", "agent_tool", "knowledge_resource"]
     workflow = build_startup_workflow(servers)
     context = WorkflowContext(workflow_id="mcp-startup")
-    
+
     result = await workflow.execute({}, context)
     print(f"All servers started: {result['status']}")
 
@@ -188,17 +193,17 @@ async def run_tests(data: dict, ctx: WorkflowContext) -> dict:
 def build_validation_workflow(package: str):
     """Build validation workflow with parallel checks."""
     inject_package = LambdaPrimitive(lambda d, c: {"package": package})
-    
+
     # Run format, lint, types in parallel
     parallel_checks = ParallelPrimitive([
         LambdaPrimitive(run_formatter),
         LambdaPrimitive(run_linter),
         LambdaPrimitive(run_type_check),
     ])
-    
+
     # Then run tests (depends on code quality)
     tests = LambdaPrimitive(run_tests)
-    
+
     # Aggregate results
     aggregate = LambdaPrimitive(
         lambda d, c: {
@@ -207,15 +212,15 @@ def build_validation_workflow(package: str):
             "all_passed": all(r["passed"] for r in d if isinstance(r, dict))
         }
     )
-    
+
     return inject_package >> parallel_checks >> tests >> aggregate
 
 async def main():
     workflow = build_validation_workflow("tta-dev-primitives")
     context = WorkflowContext(workflow_id="validation")
-    
+
     result = await workflow.execute({}, context)
-    
+
     print(f"Package: {result['package']}")
     print(f"All checks passed: {result['all_passed']}")
 
@@ -257,10 +262,10 @@ async def main():
     parser = argparse.ArgumentParser(description="Script description")
     # Add arguments
     args = parser.parse_args()
-    
+
     workflow = build_workflow()
     context = WorkflowContext(workflow_id="script-name")
-    
+
     result = await workflow.execute(input_data, context)
     print(f"Result: {result}")