An agent gives outdated pricing. What is the most likely planning failure?
Why D is the answer
Grounding data was not reviewed for timeliness
Timeliness is a grounding factor. The model can only reflect what it is given.
In detail
- Outdated answers point to stale data.
- Timeliness was not reviewed.
- Plan the refresh, not only the first load.
Use case
An illustrative scenario, not a real incident.
A price file was last updated in spring, and an agent quotes old prices. Nobody planned how the data would stay current.
See it in different ways
flowchart LR
A["Business data"] --> B{"Accurate, relevant, timely?"}
B -->|No| C["Clean it first"]
B -->|Yes| D["Check security and permissions"]
D --> E["Ground the agent"]mindmap
root((Grounding data))
Accuracy
Relevance
Timeliness
Cleanliness
Availability
Access controlstateDiagram-v2 state "Current data" as s0 state "Stale data" as s1 state "Wrong answers" as s2 [*] --> s0 s0 --> s1: no refresh s1 --> s2 s1 --> s0: refresh planned s2 --> [*]
Key point: Stale grounding gives stale answers.
Why the other options are wrong
- A. The agent's name was too long for its channel
- Name length has no effect on answers.
- B. The model's temperature was set a little too low
- Temperature does not make data old.
- C. The agent was given too many example prompts
- Examples do not change prices.