What can the model do?
How reliably does it solve your tasks? Check real comparisons with both typical and challenging examples.
Use models selectively
How much reasoning does the task need?
Reasoning helps with planning, comparison and review. Good results also require clear instructions, suitable information and reliable tests.
What should the model do?
Guidance from the framework, not a model benchmark. This choice does not configure a real model or estimate runtime or cost.
FOUR DISTINCTIONS
The model size is only one part of the decision.
How thoroughly does it work?
Reasoning models can spend additional computation on an answer. Available settings vary by model and tool.
What does it know here?
The current spec, relevant files, specific errors, and test results form the working context. More text is not automatically more useful. Improve AI skills and AI context in the Learning Loop.
What is it allowed to do?
Reading, testing, changing, and releasing are separate permissions. More reasoning does not grant more authority.
IN THE BUILD LOOP
Plan thoroughly. Build deliberately. Review independently.
Resolve uncertainty
A more capable model with more analysis can help when dependencies are unknown. The team makes business decisions.
Build from a clear spec
A smaller model may be enough for a small, unambiguous change if it has performed well on similar tasks.
Assess using evidence
A separate review context checks criteria and counterexamples. People decide on approval and remaining risks.
What “thinking” means here
A model can break down tasks, compare options, and revise outputs using feedback. This describes observable capabilities; it does not demonstrate human consciousness.
Additional reasoning can help with difficult tasks while increasing time and cost. It does not guarantee a correct answer.
What we require as an outcome
Ask for a concise rationale, assumptions, alternatives checked, test results, and open questions. A long reasoning trace is not evidence of quality.
If the same error recurs, stop the run and provide the exact error and attempts so far. Then diagnose the issue or correct the spec.
CONTEXT & SOURCES
From guide to verifiable practice.
This site turns the co-developed Guide 1.3 into interactive learning examples. Shopping cart rules, example figures, and the decision aid are illustrative choices. LoopsWork is a framework proposal.
Anthropic · Building effective agentsSimple workflows, feedback from tools, and bounded agent runs.Anthropic · Effective context engineeringSelect relevant AI context and keep it current for longer tasks.Claude Platform · Extended thinkingA vendor-specific example of additional reasoning effort. This is not a product recommendation. Sources checked on 7 October 2026.