Reasoning failure
Planning Failure
When an AI agent decomposes a task incorrectly, picks a wrong strategy, skips required steps, or fails to adapt to new information.
What failed
Planning failure occurs when an AI agent decomposes a task incorrectly, chooses the wrong strategy, skips required steps, performs steps in the wrong order, or fails to adapt when new information appears. It is a core failure mode in multi-step agent workflows.
Architecture context
Research agents, coding agents, data analysis agents, support agents, sales operations, IT automation, procurement workflows, and multi-step business process agents.
Impact
Many enterprise tasks require sequencing, dependency management, validation, and escalation. A planning failure can cause the agent to work on the wrong subtask, call the wrong tools, miss constraints, or produce a final answer that looks polished but is incomplete.
Symptoms
- The agent skips necessary discovery or validation.
- It solves a simpler version of the task.
- It chooses tools in the wrong order.
- It fails to revise the plan after tool output.
- It does not identify dependencies or blockers.
- It continues despite uncertainty.
Detection signals
- Missing expected workflow steps.
- Tool calls out of expected sequence.
- Low-quality final output despite successful steps.
- Repeated user corrections about task scope.
- Failure to escalate when plan confidence is low.
Mitigations
- Use explicit workflow plans.
- Add required checkpoints.
- Validate assumptions before action.
- Use state machines for critical workflows.
- Add escalation when plan confidence drops.
- Evaluate planning separately from final answer quality.