Refactoring KilledByAI: Multi-Agent Orchestration with Claude, Gemini, and Orca

I’m currently pushing a deep refactoring cycle for killedbyai.xyz, focusing on fine-tuning specialized skill definitions for my custom agent suite.

To improve overall output fidelity, I’ve moved away from single-model dependency. While Gemini handles high-throughput synthesis exceptionally well, pairing it with Claude for complex reasoning and nuanced generation creates a far more resilient pipeline.

However, scaling a multi-agent system introduces an immediate operational threat: uncontrolled autonomous drift.

After letting agents run with too much autonomy for a period, output predictability degraded. Unmonitored agentic execution without explicit boundaries quickly leads to garbage output and wasted tokens. To fix this runtime anti-pattern, I transitioned orchestration over to Orca.

Orca provides the explicit control plane needed to direct, manage, and audit multi-agent workflows. It enforces deterministic constraints over agent communication while still leveraging their parallel problem-solving capabilities.

For pure multi-agent coordination, Orca is rapidly becoming my primary driver.

AI agents are only as good as the system architecture governing them. Giving models infinite freedom sounds good on paper, but engineering excellence demands strict orchestration, explicit boundaries, and hard metrics.