Execution Feedback Driven Iterative Refinement

Skill: execution-feedback-driven-iterative-refinement

1. Capability Definition & Real Case

• Professional Definition: The ability to plan, generate, and execute an action sequence within a closed-loop environment, subsequently performing a dynamic 'Error -> Reflection -> Correction' cycle based on execution feedback (such as compiler errors, API failure logs, or numeric optimization scores). This capability encompasses meta-verification of reasoning trajectories to ensure step-wise accuracy and the adaptive discovery of hidden environment parameters through interactive tool probing.
• Dimension Hierarchy: Open-World Real-World Planning -> Technical Research and Optimization -> execution-feedback-driven-iterative-refinement

Real Case

[Case 1]

• Initial Environment: A user order-retrieval system with two APIs: one for listing order history (get_user_orders) and another for fetching details (get_order_details). The documentation is incomplete, missing the specific user-identification parameter for the history list and utilizing an outdated parameter name for the details tool.
• Real Question: Can you fetch the details of my recent orders? I need to know the products, status, and delivery date.
• Real Trajectory: 1. [Tool Planning] Attempt to fetch history using get_user_orders(). 2. [Observation] Receive error: 'Input parameters missing; provide user ID'. 3. [Reflection] Identify the missing constraint and decide to provide user_id. 4. [Tool Invocation] Execute get_user_orders(user_id='123'). 5. [Observation] Success, found Order #456. 6. [Tool Invoation] Execute get_order_details(is_id='456') based on documentation. 7. [Observation] Error: 'Invalid parameter is_id'. 8. [Reflection] Hypothesize the parameter name evolved and try order_id as an integer. 9. [Tool Invocation] Execute get_order_details(order_id=456). 10. [Observation] Success, retrieved status 'Shipped'.
• Real Answer: Your recent order (ID: 456) for the Apple iPhone 12 is currently 'Shipped'.
• Why this demonstrates the capability: This case demonstrates iterative refinement because the agent encountered multiple execution failures (missing parameters and outdated naming) and used each specific error message as a 'learning signal' to adjust its tool invocation strategy. It proves the agent can navigate production environments with imperfect documentation by applying a deliberate reflection loop rather than simply giving up.

[Case 2]

• Initial Environment: A weather forecasting tool get_weather_forecast that requires a specific date format. The user provided the date in a natural language format ('Christmas Day') which is not recognized by the backend server.
• Real Question: What is the weather forecast for London on Christmas Day 2024?
• Real Trajectory: 1. [Thought] I will query the weather for London on December 25, 2024. 2. [Action] get_weather_forecast(city='London', date='December 25, 2024'). 3. [Observation] 'Invalid date format. Use YYYY-MM-DD format'. 4. [Reflection] The tool feedback explicitly specifies the YYYY-MM-DD pattern. I must convert 'Christmas Day' to '2024-12-25'. 5. [Action] get_weather_forecast(city='London', date='2024-12-25'). 6. [Observation] Success: 'Light Snow, 2°C'.
• Real Answer: On Christmas Day 2024, London will experience light snow with a temperature of 2°C.
• Why this demonstrates the capability: This demonstrates execution-feedback-driven refinement by showing an 'Error -> Reflection -> Correction' paradigm. The agent recognizes its initial invocation failed due to a format constraint provided in the feedback and re-invokes the tool with corrected parameters to achieve the goal.

Pipeline Execution Instructions

To synthesize data for this capability, you must strictly follow a 3-phase pipeline. Do not hallucinate steps. Read the corresponding reference file for each phase sequentially:

1. Phase 1: Environment Exploration Read the exploration guidelines to discover raw knowledge seeds: references/EXPLORATION.md

2. Phase 2: Trajectory Selection Once Phase 1 is complete, read the selection criteria to evaluate the trajectory: references/SELECTION.md

3. Phase 3: Data Synthesis Once a trajectory passes Phase 2, read the synthesis instructions to generate the final data: references/SYNTHESIS.md