Candidate Generation and Selection

Sat, 08 Aug 2026 15:54:00 +0100

The previous chapter built a boundary that stops arbitrary model output from acquiring execution authority without explicit checks. It made one class of failure inspectable and enforceable, and it is silent about another.

Suppose the model is asked to solve a coding problem. One run produces the right patch. The next produces a plausible but incomplete one. A third produces something better again. Nothing is malformed, nothing violates the action schema, and every one of them would pass the boundary we just built. Validity and quality are different properties. A proposal can be completely valid and still be a poor choice, which relocates the uncertainty rather than removing it:

Agents From First Principles 02: AI Agent Gives Inconsistent Answers? Generate Multiple Candidates and Rank Them

Sat, 08 Aug 2026 15:54:00 +0100

One of the first things you notice when you build anything around a large language model is that the same prompt does not always produce the same quality of answer.

Sometimes the first response is excellent.

Sometimes it is merely acceptable.

Sometimes it misses the point entirely.

That creates a very common agent-engineering question:

If the model is inconsistent, should the agent trust the first answer it gets?

Often, no.

Candidate Ranking on AIBussin — AI applications, systems and books

Candidate Generation and Selection

Agents From First Principles 02: AI Agent Gives Inconsistent Answers? Generate Multiple Candidates and Rank Them