Anthony Adamo's Portfolio

This month’s goal was to advance the intelligence and responsiveness of my NPC agent by enhancing the action execution, integrating feedback, and stabilizing the system for milestone testing. I focused improving the communication between Minecraft and the backend language model, and validating the NPC behavior through targeted testing commands.

To accomplish this, I worked within a Mineflayer architecture (originally starting with Fabric), implemented new command handlers, refined the recovery and action sequences, and profiled the performance using Clinic.js. This iteration strongly focused on accuracy, repeatability, and verifying the NPC’s ability to interpret tasks and adapt to player feedback.

A core issue I faced early in the month was inconsistent decision responses from the NPC after executing several tasks. My strategy for solving this involved stepping through each component, such as commands, action handlers, dialogue routing, and queue logic, to isolate where state was dropping or being overwritten. This led to multiple refinements in the command structure and error handling, which resulted in more predictable behavior across sessions.

Successes:

• I successfully built and tested all the milestone commands for NPC evaluation.
• The NPC is now far more stable during repeated test sessions.
• Backend routing improvements increased the clarity and usefulness of LLM responses.
• Version control and release management have been consistent throughout the month.

Problems:

• Several early errors in command handling caused ambiguous or repeated fallback responses.
• Pathfinding tests initially produced inconsistent movement due to incomplete recovery logic.
• The migration away from Fabric introduced work that delayed testing cycles.

Going Forward:

• Continue to expand the automated test scenarios to identify regressions early.
• Maintain a higher update frequency with my advisor to validate feature direction.
• Begin organizing performance metrics earlier in each milestone to reduce pressure.

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Additional Reflection

• Time Utilization: I managed my tasks effectively, though some refactoring work took longer than expected due to the architectural changes.
• Info Gained: This month improved my understanding of sequencing, backend routing to LLMs, and NPC state management.
• Course Application: Concepts from AI programming, software architecture, and distributed systems were applicable during backend NPC communication.
• Preparation: With the feedback and testing commands in place, I am positioned to begin the memory learning and evaluation cycles planned for the next milestone.