Self-Improving GPT-5.3-Codex Agent for Code Generation & Refinement
Build a self-improving agent using the OpenAI Agents SDK, leveraging GPT-5.3-Codex's advanced code generation and reasoning capabilities. Inspired by OpenAI's claim of a model instrumental in creating itself, this challenge focuses on an agent that can autonomously generate code solutions for a given problem, then critically evaluate, test, and iteratively refine its own code to improve correctness, efficiency, and adherence to specified coding standards. The system should manage longer-running tasks, potentially involving multiple stages of generation, testing, and debugging, with robust observability and evaluation.
What you are building
The core problem, expected build, and operating context for this challenge.
Build a self-improving agent using the OpenAI Agents SDK, leveraging GPT-5.3-Codex's advanced code generation and reasoning capabilities. Inspired by OpenAI's claim of a model instrumental in creating itself, this challenge focuses on an agent that can autonomously generate code solutions for a given problem, then critically evaluate, test, and iteratively refine its own code to improve correctness, efficiency, and adherence to specified coding standards. The system should manage longer-running tasks, potentially involving multiple stages of generation, testing, and debugging, with robust observability and evaluation.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
All Tests Pass
Verify that all provided unit tests pass with the final generated code.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Code Syntax Check
Ensure the final_code is syntactically valid Python.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Test Pass Rate
Percentage of unit tests that passed with the final code. • target: 1 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Efficiency (Iterations)
Number of iterations taken to reach a working solution (lower is better). • target: 2 • range: 1-5
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Code Quality Score
A static analysis score (e.g., using Pylint or Flake8) for the final code. • target: 8 • range: 0-10
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Master the OpenAI Agents SDK for defining agent roles, tools, memory, and orchestrating complex, multi-turn interactions for code development.
Implement advanced prompt engineering for GPT-5.3-Codex to generate functional, robust, and idiomatic code for diverse programming problems.
Design an iterative self-improvement loop where the agent uses `DeepEval` to evaluate its generated code against unit tests and style guides, then uses that feedback to refine its own prompts or code.
Orchestrate the entire code generation, testing, and refinement pipeline using Dagster, ensuring each step (e.g., generate, test, debug, refine) is a managed operation.
Integrate Agent Protocol for standardized communication with an external 'Execution Environment' agent that runs generated code and returns test results.
Build a Gradio web interface for submitting coding challenges, displaying the agent's generated code, test outputs, and iterative refinements in real-time.
Develop strategies for managing persistent context and memory within the OpenAI Agents SDK to enable the agent to 'remember' previous attempts, errors, and successful patterns.
How this agent runs
The self-improving agent will be evaluated on its ability to generate correct, efficient, and well-structured code solutions for a variety of coding problems. Key metrics include test pass rate, code quality, and the...
Challenge input
{"problem_description": "str", "unit_tests": [{"test_case": "str", "expected_output": "str"}], "max_iterations": "int"}
Agent execution
The configured agent processes the input under the challenge policy.
Evaluated output
{"final_code": "str", "test_results": {"pass": "int", "fail": "int"}, "iterations": [{"iteration_num": "int", "generated_code": "str", "feedback":...
- Verify that all provided unit tests pass with the final generated code.
- Ensure the final_code is syntactically valid Python.
- Percentage of unit tests that passed with the final code. • target: 1 • range: 0-1
- Test Pass Rate target: 100%
- 1 public reference case
- Python execution harness
View technical recipe
Configured tools
No tool records are attached.
Evaluation contract
- All Tests Pass · Weight 1
- Code Syntax Check · Weight 1
- Test Pass Rate · Weight 1
- Efficiency (Iterations) · Weight 1
- Code Quality Score · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.
Run this agent on your dataset and AI stack
Bring your dataset, model providers, and success criteria. We will scope the right managed run for your team.
Scope a managed run[ok] Wrote CHALLENGE.md
[ok] Wrote .versalist.json
[ok] Wrote eval/examples.json
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