RUBIKSCORPAI bots
Why AI agents fail halfway through a task
Agents chain many steps, so small errors compound. A wrong tool call, a missing field or a loop can derail a task that worked perfectly in testing, and the failure may go unnoticed.
Trace the workflow one step at a time
An agent can produce a plausible plan while failing to complete the task. Trace the actual tool calls, validated inputs, results and final state. Identify which actions can be retried and which create external effects. A timeout does not prove an action failed: the external system may have completed it before the response was lost. This distinction matters for emails, bookings and record changes.
What to check
- Test missing fields, unavailable tools, partial results and unexpected response formats.
- Interrupt a task after a consequential action and check how the workflow resumes.
- Inspect loops, retry counts and the evidence used to declare the task complete.
What to change
Use explicit states for work in progress, completed actions and cases needing human help. Validate tool inputs and outputs. Add idempotency where an action might be retried. Bound the number of steps and retries, and preserve enough task state to recover safely. Confirm the external result before telling a user that the task succeeded.
How to evaluate the result
Track completed tasks, manual interventions, duplicate actions and recovery success. Report failures by stage so a team can distinguish model planning problems from integration or data issues.
What the result does not establish
Automation suitability depends on the consequences of a mistake. Keep a human decision point for actions that cannot be safely reversed, and agree who will handle stalled work before launching an agent.
Further reading
Want us to fix this for you?
We trace where your agent breaks and how often. Register and we’ll help you make multi-step automations more reliable and easier to watch.