Most AI apps do not fail because the model is bad. They fail because the system surrounding the model lacks structure.
Define a stateful workflow, separate model decisions from runtime, use typed tools, enforce structured outputs, and add observability.
Define a stateful workflow, separate model decisions from runtime, use typed tools, enforce structured outputs, and add observability.
Summary
The article argues that building reliable AI systems requires more than a powerful LLM; it demands a well‑structured architecture. It warns against treating the model as the central controller, instead recommending that the system decides which tools to call and validates each step. The author proposes a stateful workflow where the agent’s goal, steps, and status are explicitly defined. Typed tools with clear risk levels and execution signatures are introduced to prevent unsafe calls. Structured outputs are mandated so the system can unambiguously interpret the model’s decisions. Observability is highlighted as essential for debugging non‑deterministic agent behavior. The piece outlines a five‑layer architecture: API, runtime, model, tool, and control layers. It concludes that keeping the model as a suggestion engine while the runtime enforces rules yields trustworthy AI applications.
Key changes
- Use a stateful workflow with AgentState
- Separate model from runtime logic
- Define typed tools with risk levels
- Enforce structured outputs for decisions
- Add observability to trace steps
- Implement a five‑layer architecture (API, runtime, model, tool, control)