Add studdy_buddy agent tool call trace

agents/traces/study_buddy_trace.json

@@ -0,0 +1,88 @@
+{
+  "agent_id": "study_buddy",
+  "run_id": "run_study_001",
+  "status": "simulated",
+  "started_at": "2026-06-16T08:00:00Z",
+  "completed_at": "2026-06-16T08:00:06Z",
+  "user_request": "Create a 30-minute study session on machine learning basics using my class notes.",
+  "steps": [
+    {
+      "step": 1,
+      "action": "Receive user request",
+      "details": "Parsed request: build a 30-minute study session for machine learning basics using provided notes"
+    },
+    {
+      "step": 2,
+      "action": "Identify required tools",
+      "details": "Agent manifest requires: notes, web_search"
+    },
+    {
+      "step": 3,
+      "action": "Call mock tool",
+      "tool": "notes",
+      "input": { "action": "read", "note_ids": ["ml_intro_01", "ml_intro_02"] },
+      "output_summary": "Retrieved 2 class notes covering supervised learning, overfitting, and evaluation metrics"
+    },
+    {
+      "step": 4,
+      "action": "Call mock tool",
+      "tool": "web_search",
+      "input": { "query": "machine learning basics study guide overfitting evaluation metrics" },
+      "output_summary": "Found 3 reference snippets for beginner-friendly examples and terminology"
+    },
+    {
+      "step": 5,
+      "action": "Process tool response",
+      "details": "Grouped note content into review, practice, and recall segments; selected key terms for flashcards"
+    },
+    {
+      "step": 6,
+      "action": "Generate final answer",
+      "details": "Produced a timed study plan with flashcards and practice questions aligned to the requested duration"
+    },
+    {
+      "step": 7,
+      "action": "Save run history",
+      "details": "Run logged to trace store with run_id run_study_001"
+    }
+  ],
+  "final_output": {
+    "study_plan": [
+      {
+        "segment": "Review key concepts",
+        "duration_minutes": 10,
+        "focus": ["supervised learning", "features and labels", "overfitting"]
+      },
+      {
+        "segment": "Active recall",
+        "duration_minutes": 10,
+        "focus": ["explain evaluation metrics", "compare training vs validation data"]
+      },
+      {
+        "segment": "Practice questions",
+        "duration_minutes": 10,
+        "focus": ["apply concepts to a simple dataset", "identify common mistakes"]
+      }
+    ],
+    "flashcards": [
+      {
+        "front": "What is the difference between a feature and a label?",
+        "back": "A feature is an input variable used for prediction; a label is the target value being predicted."
+      },
+      {
+        "front": "What does overfitting mean?",
+        "back": "Overfitting happens when a model learns training data too closely and performs poorly on new data."
+      },
+      {
+        "front": "Why do we use a validation set?",
+        "back": "A validation set helps estimate model performance during development without using the test set."
+      }
+    ],
+    "practice_questions": [
+      "Explain why a model with very high training accuracy might still perform poorly on test data.",
+      "Given a small dataset, how would you decide whether the model is overfitting?",
+      "Name two evaluation metrics you would use for a classification task and describe when each is useful."
+    ],
+    "session_summary": "Completed a 30-minute beginner study session on machine learning basics using class notes and quick reference material."
+  }
+}