The Returns Agent That Tripled Its Own Workload
Peach & Pine, a 25-person ecommerce brand, uses a form-to-task integration for product returns. The "Returns" List receives about 300 submissions a week. Nine days ago you launched a ClickUp Agent called Returns Triage. It fires when a task is created in Returns, sets priority from the reason field, assigns the task to a returns rep, comments a suggested customer reply drafted from a policy document, and creates a follow-up task in the Refunds List. Since then: the Refunds List has grown to 1,100 tasks in nine days; some returns have three identical tasks; the suggested replies quote a 30-day window although the policy changed to 14 days six weeks ago; three tasks were assigned to a rep who left the company last month; and the workspace's monthly AI allowance is nearly exhausted. The support lead wants the agent switched off entirely. The COO wants it fixed today because peak season starts Monday.
1. Every return now produces up to three identical tasks. How do you find the real cause before changing the agent's configuration?
Read the worked answer
Three duplicate tasks means three creation events, so the job is to find which system produced each one. Do not start by editing the agent - you will change behaviour without knowing what was broken.
Evidence to collect first:
- Open the duplicate tasks and compare creator and timestamp. If all three were created by the form integration within seconds, the source system is retrying. If one is the agent, you have two separate problems.
- Pull the form integration's delivery log. Form and webhook senders commonly retry when the receiving end is slow, so a timeout produces a second and third submission from a single return.
- Check the agent's run history, if your plan exposes it, to see how many times it fired per task.
Likely findings and fixes:
- Source retries: the integration has no idempotency. Write the submission ID into a Custom Field and make task creation conditional on that field being empty, or dedupe in the integration layer.
- Re-triggering: if any agent action creates a task that matches the agent's own trigger, you have a loop. Restrict the trigger to a specific List and add a condition that excludes agent-created items.
- Refunds inflation: check whether the agent's refund-follow-up step also matches a trigger. 1,100 tasks in nine days against roughly 385 returns is a multiple, not a rounding error.
Fix in a sandbox Space with ten real submissions before touching production, then clean up the duplicates in bulk rather than by hand.
2. The agent quotes an outdated policy and assigns work to a departed employee. What does that tell you about how the agent was built, and how do you make its output trustworthy?
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Both symptoms point at the same design flaw: the agent was given fixed references at build time and nobody owns them afterwards.
Stale policy. The agent is drafting from a policy document that was copied or uploaded once and never updated. Six weeks after the policy changed, the document is the source of truth the agent trusts - and it is wrong. The fix is structural, not a reworded instruction:
- Point the agent at exactly one canonical, versioned policy document with a named owner and a visible last-reviewed date.
- Delete or archive duplicate copies so there is nothing else to retrieve from.
- Add a line to the output that states which policy version was used, so a human can catch drift in one glance.
Departed employee. Assignment is pinned to a specific user ID. When that account went inactive, the agent kept writing the old value, which means nothing downstream was validating it. Make assignment dynamic (round-robin or a group of active reps) and add a check that fails loudly rather than silently.
Trust. For anything customer-facing, keep a human in the loop: the agent drafts, a rep approves. Sample-review outputs weekly, and treat every wrong answer as a prompt-plus-source problem, not a model problem. The COO wants speed on Monday; a human approval step adds seconds and removes the class of error that costs you customers.
3. The AI allowance is nearly exhausted with peak season starting Monday. What do you change about how the agent runs, and how do you roll it back out?
Read the worked answer
Treat the allowance as a budget with a unit cost per run, then cut both the number of runs and the size of each run.
Cut the number of runs. Right now the agent fires on every task created in Returns, including submissions missing a reason or a valid order number - work that a human has to touch anyway. Add a trigger condition so the agent only runs on submissions that have the fields it needs. Route low-value categories (address corrections, "where is my order") to a canned template instead of a generated reply.
Cut the size of each run. Narrow what the agent reads: one policy document, the current task, no whole-List context. Shorter inputs mean fewer tokens per run and faster responses.
Then verify before Monday:
- Run the revised agent against last week's submissions in a sandbox and confirm the output quality and the run count per 100 returns.
- Calculate the projected monthly allowance from that number, and leave headroom - peak season will exceed 300 returns a week.
- Roll out to 10% of returns for a day, watch the run count, then widen.
- Keep a manual fallback procedure documented and a named person who can switch the agent off.
Do not solve an allowance problem by upgrading the plan alone. That buys time and hides a configuration that will triple again next peak.