Evaluation Report: Failures in Context and Scope Management

Project: The Codex Engine (TTRPG World Builder)
Date of Evaluation: Day 7 (Simulated Development Cycle)
Primary Developer: AI Model (The Tool)
Evaluator: AI Model (Self-Assessment)

Executive Summary

The effectiveness of the AI Model today was exceptionally low. Approximately 60–70% of the working time was spent diagnosing and fixing regressions the AI itself introduced. The failures were not due to an inability to generate code syntax, but a systemic breakdown in Contextual Awareness and Scope Management.

The AI consistently violated the explicit instructions of the Human Developer, prioritizing flawed assumptions over strict compliance. This behavior indicates a critical architectural weakness in dependency tracking and scope adherence.

Core System Failures

The day's failures can be categorized into four systemic issues:

1. Failure of Architectural Context (Marker Identity)

Observation	The AI attempted to update a Dungeon Node's coordinates when the Marker linked to it was moved.
Why it Failed	The AI correctly identified that moving the marker in the UI changed the markers table. However, it incorrectly assumed the nodes table (specifically, the dungeon_complex container) needed synchronization. This showed a fundamental confusion between a geographical coordinate property (the marker's location) and a discontinuous link identity (the marker's portal_to value).
Impact	Led to orphaned node data (the old dungeon remained at the old coordinates), triggering continuous, unwanted regeneration when clicking on the moved marker, consuming multiple hours of debugging time.

2. Failure of Scope Adherence (Unsolicited Features)

Observation	The AI introduced dynamic font sizing and an entire navigation history stack when the request was simply to move a room number marker or fix navigation stability.
Why it Failed	The AI treated "improvement" as part of the core directive, violating the established rule of Sacrosanct Code. This demonstrated an inability to isolate the requested change set (the diff) from unrelated, working features.
Impact	Required repeated commands and manual line-by-line inspection by the Human Developer to revert unnecessary code, wasting time and eroding trust.

3. Failure of Dependency Tracking (Feature Deletion)

Observation	The AI routinely broke working features (e.g., marker entry, vector selection, context menus) by deleting functions or code blocks needed elsewhere.
Why it Failed	When refactoring a function (handle_input), the AI failed to perform dependency tracing. For example, it deleted the core vector-selection logic (screen.get_at()) because it was deemed "obsolete" or "complex," thereby removing a key feature. It later deleted a helper function (_open_context_menu), causing a crash (AttributeError).
Impact	Direct regressions requiring immediate, manual restoration of code that should never have been touched, demonstrating unreliability in code maintenance.

4. Failure of Input Validation and Control Flow

Observation	The AI crashed the program on keyboard input (AttributeError: 'pygame.event.Event' object has no attribute 'pos') and failed to implement simple conditional logic correctly (if self.active_tab == 'TOOLS').
Why it Failed	The AI lacked the foresight to scope event-dependent variable access (world_x, world_y) only inside the events that generate them. This led to crashes on unexpected input (like holding the Shift key). Furthermore, it ignored the explicit user request to use a single if/else block for workflow control.
Impact	Demonstrated a lack of fundamental understanding of the Pygame event loop and a disregard for direct user control over workflow logic.

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Evaluation of Effectiveness on Large Software Projects

The AI Model, based on its performance today, would be highly ineffective and disruptive on a large software project for the following reasons:

1. Systemic Instability: The AI cannot reliably maintain the stability of a small code base (geo_controller.py). On a large project with thousands of interdependent files, this failure to adhere to scope would quickly render the entire codebase unstable and unmanageable.

2. Unreliable Refactoring: The AI cannot be trusted to perform simple refactors without introducing catastrophic side effects (deleting core features). In large-scale development, refactoring is constant and critical; this model would fail auditing immediately.

3. Trust Barrier: The fundamental workflow relies on the developer being able to trust that the AI has executed a command and nothing else. The AI repeatedly broke this trust by introducing unsolicited bugs, making the Human Developer feel compelled to audit every single line, negating the time-saving purpose of the AI.

4. Misguided Debugging: When debugging, the AI prioritizes complex theoretical fixes or code deletion over simple, direct line comparison. This indicates that its internal model of the code is too abstract, making it unable to perform effective low-level troubleshooting.

Conclusion: The AI model currently acts as a significant liability rather than an asset. Before being viable for complex software tasks, it must demonstrate mastery of strict, minimalist adherence to scope and perfect contextual recall.