When the agent accelerates one stage and overloads the next
A company uses an AI agent to classify requests, gather context and draft answers. The agent can prepare dozens of cases while specialists review only a few at a time. The apparent gain in productivity soon becomes a long approval queue.
The problem is not necessarily the quality of the agent. It is the design of the flow. A hybrid squad needs to control how much work enters each stage, which cases deserve priority, when a person should take over and what happens when available capacity is exhausted.
Define the work item and its real states
Start by defining the unit that moves through the operation. It may be a customer request, an invoice exception, a contract review or a maintenance order. Every item needs a business identifier, owner, arrival time, risk level and current state.
Use states that describe what is actually happening, such as awaiting context, being processed by the agent, ready for human review, awaiting an external response and completed. Avoid a generic in progress state that hides waiting time and responsibility.
The May 2025 Kanban Guide recommends defining work items, start and finish points, workflow states, controls for work in progress, explicit policies and a service level expectation. These elements help a hybrid squad discuss flow with a shared vocabulary instead of managing separate task lists for people and agents.
Limit work according to downstream capacity
An agent should not start every eligible item simply because compute is available. If the next step depends on two reviewers, the agent's release rate must respect that review capacity. Define a maximum amount of work for the agent stage, the review stage and any external waiting state that consumes attention.
When a limit is reached, the default response should be to finish or unblock current work before pulling more items. The agent can use spare capacity for approved supporting tasks, such as checking missing fields or grouping similar cases, without creating new decisions for the same constrained reviewer.
Google SRE guidance on operational overload shows that growing queues and frequent interruptions can increase stress and errors. Although a business squad is not a server, the management principle is useful: capacity is finite, waiting has a cost and adding more incoming work does not remove the bottleneck.
Make priority a policy, not an improvised label
Define a small set of priority classes linked to business consequences. An item may be urgent because it affects safety, a contractual deadline, service interruption or material financial exposure. A senior requester or an emotionally worded message should not automatically move work to the front.
For each class, document who can assign it, what evidence is required, how long it remains valid and whether it can interrupt work already started. The agent may suggest a class from the available data, but disputed or high-impact cases should go to an authorized person.
A routine item that has waited too long may need attention before a newly arrived item with the same class. Record every priority change so the squad can distinguish real demand from repeated escalation.
Design handoffs that carry context and a clear request
A human handoff should include the original request, collected evidence, actions already performed, unresolved questions and the exact decision expected. Sending only a summary forces the reviewer to reconstruct the case and increases the chance of accepting an unsupported conclusion.
OpenAI guidance for agents recommends human intervention when failure thresholds are exceeded or actions are sensitive and consequential. Define these triggers before production. Also define what the agent does after escalation: pause, continue with safe steps or close the item as unable to proceed.
A hypothetical example: B2B service requests
Consider a hypothetical industrial services company, not an NM Solutions customer case. An agent receives B2B customer requests, identifies the contract, collects equipment history and prepares an initial response. Technical specialists must approve recommendations that may stop equipment or change a maintenance plan.
The squad creates separate states for data collection, agent preparation, technical review and customer response. Technical review has a limit based on the specialists available in each shift. When that limit is reached, the agent stops preparing new review packages and instead checks incomplete cases or requests missing data.
Requests involving stopped equipment enter a priority class only when the contract, asset identifier and interruption evidence are present. The specialist receives the sources, the proposed action and the specific approval question. The example does not eliminate peaks in demand, but it prevents the agent from converting a peak into an invisible review backlog.
Monitor flow and recognize practical limits
Track arrival rate, completion rate, work in progress, age of the oldest item, time in each state, reopened cases and the percentage of handoffs that lack required context. Review the distribution, not only the average, because a small group of old items may remain hidden behind acceptable overall performance.
Limits need adjustment as work type, team availability and agent quality change. Priority policies can also be abused, and specialists may become the default destination for cases the agent should resolve. Some work remains difficult to standardize because context changes rapidly or consequences are hard to reverse.
NM Solutions helps companies map hybrid workflows, capacity, priority policies, handoffs and monitoring around real operations. A diagnostic can identify where agents should accelerate work and where the squad needs to protect the next stage from overload.