The best AI agent picked the better path at a key fork only 59.7% of the time. Put checkpoints where your workflows branch
Agents are good at finishing steps. Choosing which road to take early in a long job is a different skill, and it is weaker.
What happened
Researchers from Microsoft and City University of Hong Kong built Taste-Bench, a set of 502 questions taken from real agent runs on coding and AI research tasks. Each question freezes a run at a "decision fork", where two directions were possible and one later turned out better, hides everything after it, and asks a model to pick.
A question counted only if the model chose correctly with the options in both orders, so guessing scores 25%. Of 14 models tested, the best, GPT-5.6 Sol, scored 59.7%, with GPT-5.5 at 59.5%. When the deciding clue was already visible before the fork, average accuracy was 62.3%. When it only showed up after a lot of later work, accuracy fell to 21%. Raising the reasoning setting barely changed results.
The authors also showed taste can be trained: a student model distilled from a teacher that had seen outcomes made better choices on unseen tasks.
My take
Long running automations are full of forks. Which records to treat as duplicates. Whether to rebuild a pipeline or patch it. Which lead source to trust when two disagree. A bad call looks fine at first and only costs you later, which is exactly where these models were weakest.
How I handle it:
- Make the big forks design decisions. I decide the structure of a client workflow up front; the agent fills in the steps.
- Put a check soon after each branch, so a wrong turn shows up in minutes, not at the end of a 3 hour run.
- Add a human checkpoint before anything hard to undo, like merges, deletes or bulk sends.
- For high value tasks, run two cheap attempts and compare the outcomes instead of trusting one path.
- Do not expect a bigger thinking budget to fix it. The study says it does not.
Agents can do the work. You still own the route.
Got a long, branching process you would like an agent to run? Message me through romielwillautomate.dev and describe it, and I will map where the checkpoints go.
More posts
- MiniMax M3.1 Flash costs $0.15 in and $0.60 out per million tokens, but its hallucination rate is 93%. Give it a way to say unknownOct 20, 2026
- Attackers aimed cheap AI agents at mid-sized online stores. Successful breaches took about 23 hoursOct 20, 2026
- n8n ran a full AI stack on one NVIDIA DGX Spark with no cloud API. Invoice extraction took under a second with thinking offOct 20, 2026
