AI Agent Error Handling: Send Failed Ecommerce Jobs to a Recovery Queue

Your fulfillment agent checks a new order, spots an address mismatch, and tries to place the order on hold. The warehouse request times out. The agent has enough information to know the job did not finish, but it does not know whether the hold reached the warehouse.

A useful error-handling rule turns that uncertainty into a controlled piece of work. It saves the job state, identifies the kind of failure, chooses a bounded next action, and keeps the job open until the intended business result is confirmed or a person records another resolution.

Many ecommerce automations treat errors as technical events. A tool returns a timeout, an authentication error, or a validation message. The system logs the response and marks the run failed. That record may help an engineer, but it does not tell an operator what happened to the order, refund, inventory adjustment, or customer promise.

Preserve a business-level failure record. Include the job name, affected resource, original inputs, approvals, completed steps, failed step, tool response, current destination state, and expected result. Add timestamps and the identity of the agent or person that made each decision.

The expected result matters because a successful request is not the same as a completed job. An API can return a response while the order remains unchanged. Recovery ends when you verify the business state, such as an active warehouse hold on the correct order.

Technical error names can be useful evidence, but the recovery queue needs categories that drive action. A practical rule can send each failure to one of four outcomes: retry, wait for a dependency, request human review, or stop.

Retry temporary connection and service failures only when the action is safe to repeat. Wait when another system or person must provide something, such as address approval or a refreshed access token. Request review when records conflict, the business state is unclear, or the proposed action exceeds the agent's authority. Stop when the request is invalid and cannot succeed without a new job.

Keep the classification narrow. "Tool error" says nothing about what should happen next. "Result unknown, check warehouse before retry" gives the next worker a concrete task and protects the order from a duplicate write.

Retries help with short outages and rate limits. They also create noise, cost, and duplicate-action risk when nobody defines a stopping point. Store a retry budget with the job. The budget should state which errors qualify, how long to wait, how many attempts remain, and what happens after the last attempt.

Before each retry, read the destination again when the action could have completed without a response. If the expected state already exists, capture the result and close the job. If the inputs or underlying record changed, stop the retry path and reassess the job with current evidence.

A retry budget is not a general permission to keep trying. It is a controlled response to a known temporary condition. Authentication failures need corrected access. Validation failures need corrected inputs. Conflicting business records need an owner who can decide which record is right.

A recovery queue should let an operator understand each job without opening raw logs. Show the affected order or resource, failure class, last confirmed state, next action, owner, age, and escalation time. Attach the detailed trace for investigation, but keep the queue focused on decisions.

Assign ownership by workflow. Fulfillment failures may go to the operations lead. Refund conflicts may go to support or finance based on the amount and policy. Access problems may go to the person who manages the connected tool. Ownership should follow the business decision, not whichever engineer built the automation.

Track queue age as well as failure count. One blocked order waiting through a carrier cutoff can matter more than twenty low-priority catalog updates. Set escalation times around the customer or operating consequence of delay.

Consider a hypothetical apparel store. Order 1842 enters the fulfillment workflow with a shipping address that differs from the customer's recent orders. The AI employee gathers the order, payment, and customer history, then follows policy by requesting a warehouse hold while a person reviews the address.

The warehouse tool times out after the request. The employee records the result as unknown instead of failed. It saves the order ID, warehouse, requested hold reason, request time, tool response, and approval state. It then reads the warehouse order. No hold appears yet, so the job enters the recovery queue with one delayed retry and an instruction to check again before sending it.

On the next check, the hold appears. The employee records the warehouse hold ID and closes the tool step without another write. The job remains open because the address review still needs an owner. The queue assigns it to the fulfillment lead with the order evidence and a release deadline before the day's carrier cutoff.

If the hold had remained absent, the employee could use the approved retry. If the warehouse record had changed or another worker had released the order, it would stop and request review. Each branch protects the same goal: keep order 1842 from shipping until someone resolves the address concern.

ShopDucky AI employees keep tool actions, approvals, and run history attached to the ecommerce job they are executing. Teams can turn failures into reviewable recovery work with current evidence and a named next step. Book a demo to see approval-gated ecommerce work run across your existing stack.