ORCA Framework Enhances Auditability of AI Agents

Currently, most agent stacks are optimized for demonstrating functionality but lack the Operational Accountability required in real production environments. Key issues pointed out include the difficulty of understanding why a system chose a particular path, whether results can be reproduced, and what control mechanisms exist before execution.

ORCA proposes an approach that designs agent behavior not as simple prompt combinations but as a structured execution system. The core elements are as follows:

• Explicit step boundaries
• Typed input/output contracts
• Deterministic control flow
• Control of high-risk tasks through Policy-gated execution
• Replay and auditability secured through Full execution traceability

This framework separates a 'Discovery mode' for flexible exploration from a 'Production mode' with verification and governance applied. Through this, it serves as a technical bridge for reliably deploying AI agents in sensitive domains where security, infrastructure, and regulatory compliance are essential.