new: implement environment-free agent-trace generation

[monatis]

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[amazon-q-developer]

Summary

This PR implements a significant new feature for environment-free agent-trace generation. I've identified 6 critical defects that must be fixed before merge:

Critical Issues Found:

1. Missing import - Any type not imported in schema_architect.py causing runtime NameError
2. Logic bugs in constraint handling - Three instances where or operator treats numeric values as falsy, breaking field constraints for int, float, and money types
3. Security vulnerabilities - Two instances of arbitrary code execution risk via exec() without sandboxing in verifier.py and schema_architect.py

Required Actions:

All 6 issues have code suggestions provided. Please apply the fixes to resolve the blocking defects before merging.

The overall architecture looks solid - the approach of using declarative environments and schema verification is well-designed. Once these critical defects are addressed, this will be a valuable addition to the codebase.

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[monatis]

/q review

[amazon-q-developer]

Summary

This PR implements environment-free agent-trace generation with sophisticated schema verification and simulation capabilities. However, critical security vulnerabilities and a logic error must be fixed before merge.

Critical Issues (Must Fix)

Security Vulnerabilities:

1. Arbitrary code execution vulnerability in verifier.py:_perform_dry_run() - Uses unrestricted exec() with full builtins access despite defining SAFE_BUILTINS
2. Arbitrary code execution vulnerability in schema_architect.py:_compile_model_classes() - Executes LLM-generated code with unrestricted builtins
3. Configuration contradiction in verifier.py - SAFE_BUILTINS contains __import__ while FORBIDDEN_CALL_NAMES blocks it

Logic Error:
4. Global state pollution in simulation_engine.py - Using global random.seed() instead of instance-level Random() breaks reproducibility when multiple contexts exist

Crash Risk:
5. Inadequate error handling in generator.py:generate() - Concurrent worker failures silently swallowed without proper error reporting

Testing

The implementation includes comprehensive tests covering simulation engine, verifier, and security checks. However, tests don't validate isolation between multiple SimulationContext instances or exec() namespace restrictions.

Recommendation

Do not merge until security vulnerabilities are addressed. The code execution vulnerabilities are exploitable and create significant risk when processing LLM-generated code.

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[monatis]

/q review

[amazon-q-developer]

Summary

This PR implements environment-free agent-trace generation with 2,253 additions across 24 files. The implementation introduces a novel synthetic trajectory generation system using LLM-driven schema creation and sub-millisecond declarative execution.

Critical Issues Found (Must Fix)

1. Security Vulnerability (verifier.py:303): Using exec() to execute LLM-generated code creates a code injection vulnerability (CWE-95). While SAFE_BUILTINS provides basic restrictions, it doesn't prevent resource exhaustion attacks or type confusion exploits.

2. Logic Error (trajectory_judge.py:109): Fallback exception handler returns is_valid=True, which could inject invalid training data into datasets when trajectory evaluation fails.

3. Resource Leak (trajectory_generator.py:110): Missing try-finally for chat_session cleanup causes resource leak when exceptions occur during trajectory generation.

4. Crash Risk (tool_environment.py:36): No exception handling for Pydantic model generation/serialization failures, causing trajectory generation to crash instead of returning error ToolObservation.

5. Crash Risk (generator.py:93,167): Direct metadata dictionary mutations without null checks risk AttributeError crashes if trajectory metadata is None or immutable.

Overall Assessment

The implementation demonstrates solid architectural design with strong separation of concerns. However, the critical security vulnerability in verifier.py and multiple error handling gaps need to be addressed before merge to prevent potential exploitation and runtime crashes in production.

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[monatis]

/q review

[amazon-q-developer]

This PR implements an environment-free agent trace generation system with sub-millisecond declarative tool execution. I've identified 3 critical defects that block merge:

Critical Issues (Must Fix):

1. Security vulnerability in verifier.py: The __import__ builtin allows LLM-generated code to bypass AST security checks and import dangerous modules through dynamic imports
2. Crash risk in simulation_engine.py: Inverted integer/float constraints (ge > le) cause ValueError crashes in randint/uniform calls
3. Logic error in trajectory_generator.py: Non-greedy regex truncates nested JSON in tool parameters, breaking parameter extraction for complex arguments

These issues prevent correct functionality and create security risks that must be resolved before merge.

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