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Building Agent Workflows That Stay Understandable

Practical notes on keeping AI agent systems observable, testable, and useful as they grow beyond demos.

AI AgentsEngineering

Agentic systems become useful when their behavior can be inspected and improved. The hard part is rarely calling a model. The hard part is building the surrounding workflow so decisions, failures, and tool calls remain legible.

Start With Boundaries

A good workflow makes responsibilities explicit. Retrieval, planning, execution, and verification should each have observable inputs and outputs.

The goal is not to hide complexity. The goal is to make complexity reviewable.

type AgentStep = {
  name: string;
  input: unknown;
  output: unknown;
  elapsedMs: number;
};

Small interfaces make it easier to test behavior before adding more autonomy.