Zerango editorial · October 16, 2026

Learn AI agents through small, evaluated workflows

Learn model basics, tool calls and retrieval with a test set before adding autonomy.

Begin with a bounded task

Choose a task such as finding a lesson from a small document collection. Define a useful answer, an unsupported answer and a case where the system should ask for more information.

Separate the components

Distinguish the model, instructions, retrieved context and tools. A workflow follows a planned sequence; an agent may choose a next action from the available tools. Learn this distinction with one simple implementation before comparing frameworks.

Create evaluation examples

Prepare a small set containing ordinary questions, ambiguous requests and missing-source cases. Save the expected behavior and inspect failures manually. Track useful task completion alongside latency and cost.

Add tools gradually

Start with a read-only search tool. Validate its inputs and outputs, limit the number of steps and inspect the execution trace. Add a human review point before an action that changes data.

Compare changes with evidence

Re-run the same examples after changing the model, retrieval setup or instructions. Explain which cases improved and which regressed. A documented comparison is a stronger learning outcome than a demo that succeeds once.

Reviewed: October 16, 2026