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.

Advanced Agents From First Principles 00: When Should You Use an Advanced Agent Architecture?

Sat, 08 Aug 2026 22:27:00 +0100

Advanced Agents From First Principles 00: When Should You Use an Advanced Agent Architecture?

You built an agent.

It can:

call tools,
maintain state,
plan,
revise its own work,
search over alternatives,
remember useful information,
and verify whether the requested outcome actually happened.

Now the temptation begins.

You add another model.

Then a critic.

Then a planner.

Then a judge.

Then a router.

Then three specialist agents.

Then a tree search.

MCTS on Programmer.ie: Modern AI programming

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

Advanced Agents From First Principles 00: When Should You Use an Advanced Agent Architecture?

Advanced Agents From First Principles 00: When Should You Use an Advanced Agent Architecture?