Topic: LLM Orchestration

Today we examine a fundamental shift in software engineering where the source of truth for application behavior is moving from static code to runtime traces. In traditional systems, decision logic is hardcoded and deterministic, but in AI agents, code acts merely as scaffolding while actual reasoning happens within the model. We observe that because agent behavior is non-deterministic and orchestrated at runtime, simply reading the source code no longer provides full visibility into how a system functions or why it fails. Consequently, critical engineering tasks like debugging, testing, and optimization must now focus on analyzing execution traces rather than just profiling code. We believe this transition necessitates a new approach to observability where developers treat step-by-step reasoning logs as the primary documentation for their AI-driven applications. Without robust tracing, developers remain blind to the actual intelligence driving their systems.