Planning
Working out a sequence of steps before taking them — the thing agent demos imply models can do, and the evidence says they mostly can't.
When not to use it
- When you know the sequence. Write it down. This is most cases.
- When ordering errors are expensive. A bad plan reads exactly like a good one until it executes.
- On genuinely novel structure. Unfamiliar constraints are where the obfuscation result bites.
- Autonomously, over long horizons. The evidence doesn't support it, whatever the demo showed.
Reach for something else instead
- A hard-coded workflow — if you can write the steps, this is faster, cheaper, and correct.
- ReAct-style incremental — never needs a plan; decides the next step from feedback.
- Classical planners (PDDL) — solved this in the 1970s. Use the LLM to translate into them.
- Human plan, model execution — the person orders the steps, the model does them.
Sources & further reading
- Valmeekam et al. (2023), On the Planning Abilities of Large Language Models: A Critical Investigation — the obfuscation result; the paper agent marketing skips.
- Yao et al. (2023), Tree of Thoughts: Deliberate Problem Solving with Large Language Models — search over candidate steps; better, and expensive.
- Liu et al. (2023), LLM+P: Empowering Large Language Models with Optimal Planning Proficiency — the model translates, a classical planner reasons. It works, and it's unfashionable.
Primary sources, listed so you can check the claims on this page rather than take them on trust.
Where people go wrong
- Reading a fluent plan as a good plan. Fluency is free; correctness isn't.
- Testing on conventional tasks. It's seen those orderings. Test where the constraints are unusual.
- Assuming demos generalise. Agent demos are chosen; your task wasn't.
- Ignoring classical planners because they're old. They do the search correctly, which is the part the model can't.
At a glance
FieldAI Agents
What the evidence saysmuch weaker than demos imply
The diagnosticobfuscate the problem; structure-reasoners are invariant, pattern-matchers collapse
Better atvalidating plans than generating them
Best current shapeLLM translates, symbolic planner reasons
DifficultyIntermediate
Flashcards for this concept
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Often compared with
LLM planning vs. classical planning — one produces fluent plausible sequences, the other does actual search with backtracking. The best current systems use the LLM to translate into the second.