RAVANA AGI

Self-Modification Process

Self-Modification Process

Table of Contents

  1. Introduction
  2. Self-Modification Workflow
  3. Safety Mechanisms
  4. Real Example of Self-Modification
  5. ActionRegistry and Action Validation
  6. Critical Risks
  7. Monitoring and Manual Override

Introduction

The Self-Modification Process enables the AGI system to autonomously identify, propose, test, and apply code changes to its own modules based on insights from self-reflection. This process is designed to improve system performance, fix bugs, and adapt behavior through a secure, sandboxed, and auditable workflow. The implementation is primarily located in the self_modification.py module within the agent_self_reflection package, and it integrates with the LLM, testing framework, and action registry to ensure safe and effective modifications.

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Self-Modification Workflow

The self-modification workflow is a multi-stage process that begins with reflection analysis and ends with conditional code application. The core function run_self_modification() orchestrates this process.

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Step-by-Step Breakdown

  1. Reflection Analysis: The process starts by loading reflections from reflections.json using load_reflections(). Entries containing keywords like "fail", "error", "bug", or "issue" are flagged as actionable.

    • Source: find_actionable_reflections() in self_modification.py

  2. Bug Extraction: For each actionable reflection, the system uses the LLM with a log_bug_report tool to extract structured bug information (filename, function, summary, severity).

    • Source: extract_bug_info() in self_modification.py

  3. Code Context Retrieval: The relevant code block is extracted from the specified file and function using extract_code_block(), which includes surrounding context lines for accurate patching.

    • Source: extract_code_block() in self_modification.py

  4. Patch Generation: The LLM is prompted with the bug summary and original code to generate a fix using the propose_code_patch tool. The patch includes the filename, line range, and new code.

    • Source: generate_patch() in self_modification.py

  5. Sandboxed Testing: The proposed patch is tested in an isolated environment using test_patch(), which:

    • Creates a temporary directory
    • Copies the entire module
    • Applies the patch
    • Runs tests via run_tests()
    • Restores the original code
    • Source: test_patch() and run_tests() in self_modification.py

  6. Conditional Application: If tests pass (test_result.success == True), the patch is permanently applied using edit_file(), and a success reflection is logged.

    • Source: run_self_modification() in self_modification.py

Safety Mechanisms

The self-modification process incorporates multiple safety layers to prevent system instability.

Code Diff Validation

Before applying any patch, the system validates that the LLM response is not a "lazy" or generic output using is_lazy_llm_response(). This prevents low-effort or placeholder code from being applied.

Execution Sandboxing

All testing occurs in a temporary directory created with tempfile.mkdtemp(). The run_tests() function copies the entire module to this sandbox, executes the test command, and ensures the original code is never modified during testing.

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Rollback Procedures

The test_patch() function implements automatic rollback:

  • Creates a backup of the original file before patching
  • Uses a try...finally block to ensure the backup is restored even if an exception occurs
  • The shutil.move(backup_path, original_path) in the finally block guarantees restoration

Real Example of Self-Modification

A concrete example is demonstrated in the test suite test_self_reflection.py, where a simulated bug triggers the self-modification process.

test_reflection = {
    "timestamp": "2025-07-01T00:00:00Z",
    "task_summary": "Test bug in dummy_function",
    "outcome": "Function failed on edge case.",
    "reflection": "1. What failed?\n- The function 'dummy_function' in 'llm.py' does not handle empty input correctly.\n2. ..."
}

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In this scenario:

  1. The reflection mentions a failure in dummy_function in llm.py
  2. extract_bug_info() identifies the bug in llm.py with function dummy_function
  3. The system extracts the relevant code block
  4. The LLM generates a patch to handle empty input
  5. The patch is tested in a sandbox using a temporary reflections.json
  6. If successful, the real llm.py is updated

Another example from the codebase shows a performance bottleneck triggering self-modification:

  • Context: Mock shared state shows "Planned a complex task" with outcome "plan was inefficient"
  • Hypothesis Generation: generate_hypothesis() analyzes this history and could produce: "I hypothesize that my planning accuracy decreases when I am in a 'sad' mood"
  • Action: This hypothesis could lead to a self-modification attempt to improve the planning algorithm
  • Source: self_modification.py lines 590-600

ActionRegistry and Action Validation

The ActionRegistry plays a critical role in validating actions that may be created or modified during self-modification.

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Key Functions:

  • __init__(): Pre-registers core actions like WritePythonCodeAction and ExecutePythonFileAction
  • register_action(): Public method to register new action instances, with overwrite warnings
  • discover_actions(): Dynamically discovers and registers actions from the core.actions package
  • get_action_definitions(): Formats action metadata for LLM prompts, ensuring the AGI understands available capabilities

During self-modification, if a new action is proposed, it must be registered through ActionRegistry.register_action() to become available for execution. The registry ensures all actions follow a consistent interface and are properly integrated with the system and data services.

Critical Risks

Infinite Modification Loops

A self-modification could inadvertently create a new bug that triggers another modification, leading to an infinite loop. Mitigation:

  • The process only runs on actionable reflections, which require explicit failure indicators
  • Each modification is logged in self_modification_audit.json, enabling detection of repetitive patterns
  • The audit log includes the original reflection ID, allowing traceability

Breaking Changes

A patch might pass tests but introduce subtle breaking changes in other contexts. Mitigation:

  • Tests are run in a comprehensive sandbox that includes the full module environment
  • The run_tests() function uses the actual test command, ensuring realistic validation
  • The system relies on existing test suites like test_system.py and test_enhanced_action_manager_fix.py for thorough validation

Security Vulnerabilities

Malicious or flawed code could be introduced. Mitigation:

  • All LLM-generated code is validated with is_valid_code_patch() and is_lazy_llm_response()
  • The sandbox prevents direct access to sensitive system areas
  • Every modification is audited and recorded in JSON format with full context

Monitoring and Manual Override

For production deployments, monitoring and manual oversight are essential.

Monitoring Recommendations

  • Audit Log Monitoring: Regularly review self_modification_audit.json for patterns of frequent modifications or failures
  • Performance Tracking: Monitor system performance metrics before and after modifications
  • Test Coverage: Ensure high test coverage in test_enhanced_action_manager_fix.py and other test files to catch regressions
  • Alerting: Set up alerts for failed self-modification attempts or repeated modifications to the same file

Manual Override Protocols

  • Disable Self-Modification: Comment out or remove the call to run_self_modification() in the main loop
  • Audit Log Inspection: Examine the audit log to understand recent changes and their justifications
  • Rollback Procedure: Manually restore files from version control if an unwanted change is applied
  • Testing in Staging: Deploy self-modification features first in a staging environment with restricted permissions

The system is designed to be transparent and controllable, ensuring that autonomous self-improvement remains a safe and beneficial capability.

Referenced Files in This Document