Advanced Agents From First Principles 04: Does Your Agent Prune Good Ideas Too Early? Use Monte Carlo Tree Search for Long-Horizon Reasoning

Sat, 08 Aug 2026 23:37:00 +0100

A common failure in search-based agents is easy to miss.

The agent generates several plausible branches.

It scores them.

One branch looks weak.

So the runtime prunes it.

Later, you discover that the discarded branch was the only one that could have reached the correct solution.

The problem was not generation.

The problem was not necessarily the model.

The problem was search allocation.

The agent spent too much compute exploiting what looked good early and too little compute exploring alternatives whose value only became visible later.

Agents From First Principles 08: AI Agent Picks the First Solution? Add Search Instead of One-Shot Generation

Sat, 08 Aug 2026 17:26:00 +0100

An AI agent often fails for a surprisingly ordinary reason:

it commits too early.

It finds one plausible next action, follows it, and then spends the rest of the run trying to make that first choice work.

That can look intelligent because the agent keeps reasoning, calling tools, revising plans, and explaining itself.

But underneath, the trajectory may be almost completely determined by an early mistake.

A coding agent chooses the wrong implementation strategy and spends twenty tool calls repairing it.

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Advanced Agents From First Principles 04: Does Your Agent Prune Good Ideas Too Early? Use Monte Carlo Tree Search for Long-Horizon Reasoning

Agents From First Principles 08: AI Agent Picks the First Solution? Add Search Instead of One-Shot Generation