Agent loop, reasoning/action/observation, and ReAct

Overview

• An agent loop alternates decision, action, and observation.
• ReAct is a pattern that combines deciding what information/action is needed with executing tools and observing results.
• “Reasoning” here means useful task planning and decision-making, not a requirement to expose hidden model reasoning.

Why this matters

A single model response may not know which tool will produce the information needed next.

An everyday example

A mechanic inspects a symptom, performs a test, sees the result, and chooses another test.

A technical example

Lookup claim → observe missing document → search required-documents policy → propose a checklist → finish.

How it works

1. Model chooses from allowed tools
2. Executor returns structured result
3. Loop checks progress and remaining budget
4. Model decides again.

Architecture and flow

Decide → approved action → observation → decide, with explicit stop conditions.

The insurance assistant example

Identify why a claim is delayed using read-only status and document tools.

When to use it

Exploration across several safe information tools.

When to avoid it

Do not use unrestricted loops for payments, policy changes, or simple exact lookups.

Interview answer

“ReAct alternates model-selected actions with observations. I log decisions and tool outcomes, enforce permissions, and stop on completion or bounded failure.”

Benefits, trade-offs, and production considerations

• Require max steps, total deadline, token budget, repeated-call detection, and result-size caps.
• Store concise decision labels and evidence; do not make production depend on internal chain-of-thought access.