Tool Orchestration in Production Agent Systems: Code vs Protocols

This technical note examines how orchestration is implemented in production AI agent systems and compares two common architectural patterns: inference-based orchestration and code-first orchestration. Protocols such as Model Context Protocol (MCP) standardize how tools are described and invoked, enabling interoperability across agent platforms. However, production deployments introduce additional concerns including coordination overhead, debugging complexity, security boundaries, and cost predictability.The note argues that while MCP solves a real interoperability problem, reliable multi-step workflows are often better handled in deterministic execution environments. In code-first architectures, the language model generates a plan or program once and a runtime executes it using existing APIs, SDKs, or CLI tools. This shifts orchestration logic outside repeated model inference and aligns better with established software engineering practices such as testing, logging, and policy enforcement.The document discusses trade-offs between these approaches, highlights scenarios where MCP is beneficial (e.g., cross-platform tool ecosystems and exploratory environments), and outlines a practical pattern for production systems where the model generates a plan and the runtime executes it deterministically.