AI Agent Task Queue: Handle Many Requests Without Drops

An AI agent task queue is a durable waiting line that records every incoming request before any work starts, then hands items to the agent one at a time with a clear status and owner. Priorities decide what goes first, and acknowledgments confirm that each item was received, started and finished. Together they stop simultaneous requests from being lost or done twice.

Key takeaways

• Record every request in a durable queue before the agent touches it, so a crash or a busy hour never erases work.
• Use a short priority scheme that a person can read and explain, and set it with rules rather than leaving urgency to the model's guess.
• Acknowledge at three points (received, started, finished) and treat silence as a signal to retry or escalate to a person.
• Prevent duplicates with an idempotency key per request, so a repeated message or a retried step never produces a second effect.
• Watch the queue itself: the age of the oldest item, stuck items and rejected items reveal problems before customers complain.

Why do simultaneous requests get lost or duplicated?

A single agent working in a conversation processes whatever sits in its context. When an email, a chat message and a form submission arrive within the same minute, all three can end up in one thread. The agent may answer only the latest, merge two unrelated requests into one reply, or finish the first and never return to the others. Nobody sees a failure, because nothing technically broke. The work simply vanished.

Duplicates have a different cause. Senders retry when they do not get confirmation. A customer who hears nothing writes again. A connected system that does not receive a quick response sends the same event a second time. Stripe's webhook documentation says plainly that an endpoint may receive the same event more than once and should be built to handle that. The lesson applies well beyond payments: any channel that retries will eventually deliver the same request twice.

Both problems share a root. The agent's memory is not a ledger. A model holds what is in front of it, and it cannot reliably tell you what it was asked yesterday, what is half finished or what it already sent. A queue moves that record out of the model and into a place that survives restarts, handovers and busy mornings.

What does an agent task queue actually contain?

A queue is a list of work items, each with a state, stored outside the model. The agent does not remember the list. It reads the next item from it. A useful item carries these fields:

• Identifier: a unique reference, plus an idempotency key that identifies the underlying request.
• Source and requester: which channel it came from and who asked.
• Request: what is being asked, in a form specific enough to test later.
• Priority class: set by rule when the item is created, and visible to the team.
• Status: waiting, in progress, done, failed or needs a person.
• Lease: which worker holds the item and when that claim expires.
• Attempts: how many times the item has been tried.
• Result: the output and the evidence that it was completed.

Items move forward through defined states, and every change is written to a log. That log is what lets an operations lead answer the question that matters most after an incident: what happened to this request?

The same logic appears in the observability concepts described by OpenTelemetry, which separate signals such as logs, metrics and traces. A queue benefits from all three. Logs tell you what happened to one item, metrics tell you how the whole line is behaving, and traces show how one request crossed several tools.

How should priorities work?

Priority is a business decision, so it should be written down by the business. Keep the scheme small. Three classes are usually enough, and each should have a plain definition:

• Urgent: a deadline with legal or safety weight, or a customer who cannot operate at all. These go first and notify a person immediately.
• Standard: normal requests with an agreed response window. Most work lives here.
• Background: useful work with no clock on it, such as tidying records or preparing summaries.

Set the class with deterministic rules wherever you can: the channel, the sender's account type, a field on a form, a named keyword list. The model can suggest a class when the rules are silent, but a rule or a person should confirm anything that lands in the top class. Otherwise a confident but wrong guess can push low-value work ahead of real emergencies.

Two refinements prevent most priority trouble. First, add aging: a background item that has waited past its limit moves up, so low priority never means never. Second, do not interrupt work in the middle of a step. Let the agent finish the current step, save its progress, and then take the urgent item. Saved progress is the subject of AI agent checkpoints: how to resume unfinished work, and it is what makes switching tasks safe.

Priority also never grants permission. An urgent request to change a payment detail still needs the same approval as a standard one. The rules for which actions need a person belong in your AI agent guardrails, and they apply to every class equally.

What should an acknowledgment confirm?

An acknowledgment is a recorded statement that a stage happened. A queue needs three of them, and each serves a different reader.

• Received: sent to the requester as soon as the item is stored. It gives a reference and sets expectations. This single message removes most of the retries and repeated emails that create duplicates.
• Started: recorded internally when a worker takes the item. The worker holds a lease with an expiry time, so the claim is temporary rather than permanent.
• Finished: recorded with the result and the evidence that the work met its criteria. A definition of done for AI agents explains why this should be proof, not a statement that the agent believes it succeeded.

The lease is what handles silence. If a worker takes an item and then stalls, crashes or loses a connection, the lease expires and the item returns to the waiting state with its attempt count raised. Without a lease, that item would sit marked as in progress forever, which is the quietest way to drop a request.

Set a limit on attempts. After the limit, the item should not keep cycling. It moves to a needs-a-person lane with its history attached, and a named human receives it. This lane is not a failure of the design. It is the design working: some requests need judgment, and the queue should say so clearly.

Anthropic's engineering writing on long-running agents makes a related point. Work that spans several sessions needs written progress records, because each new session starts without memory of the last. A queue item with a status and a result field is that record for individual requests.

How do you stop the same request from running twice?

The tool for this is idempotency: performing the same operation twice has the same effect as performing it once. Stripe's API documentation on idempotent requests describes the pattern clearly. The caller attaches a unique key to a request, and if the request is repeated with that key, the system returns the original result instead of acting again. You can apply the same idea to your agent in two places.

At intake, build a key from something stable: the message identifier from the source system, or a combination of requester, subject and time window when no identifier exists. Before creating a new item, check whether that key already exists. If it does, attach the new message to the existing item and tell the requester it is already in progress. Do not silently merge it, because the second message might add a correction.

At action time, pass the key to anything with a side effect. Before sending an email, creating a record or issuing a refund request, the tool checks whether that key already has a stored outcome. If it does, the tool returns that outcome instead of repeating the action. This matters most after a lease expires, because the earlier worker may have finished the action just before it stalled. The new worker must be able to discover that, not repeat it. For background on how tools are called, see What Is Tool Calling in AI?.

Capacity belongs in the same discussion. A queue lets you cap how many items run at once, which protects the systems the agent calls. When an outside service starts rejecting calls, the queue slows down and waits rather than burning attempts. API rate limits and AI agents covers how to plan for that.

What does this look like in practice?

Here is an illustrative scenario. It is not a real case.

A small distribution company uses one agent to handle order questions. On a Monday morning, a customer emails asking to change a delivery address. Two minutes later the same customer sends a chat message with the same request because nobody has replied. At the same time, a warehouse supervisor submits a form reporting a damaged pallet that must be handled before a truck leaves.

With a queue, the email and chat message produce the same idempotency key, so the second joins the first item and the customer receives a note that the request is already in progress. The pallet report matches the urgent rule, jumps ahead, and notifies the operations lead. The agent finishes its current step on the address change, saves progress, handles the pallet item, and then returns. When the address change is complete, the item records the confirmation from the order system as evidence. Nothing is lost, nothing is sent twice, and every step can be reviewed afterward.

To introduce this in your own setup, work through these steps in order:

• Step one: list every channel through which requests reach the agent, including the informal ones such as forwarded emails.
• Step two: choose the durable place where items will be stored, and route every channel into it before the agent sees anything.
• Step three: define the item fields and the allowed statuses, and write down who may move an item between them.
• Step four: write the priority rules in plain language, name an owner for them, and add an aging rule.
• Step five: define the three acknowledgments, including the wording sent to requesters.
• Step six: add idempotency keys at intake and in every tool that changes something.
• Step seven: set lease length, attempt limits and the needs-a-person lane with a named recipient.
• Step eight: test with a deliberate burst of mixed, repeated and malformed requests in a test environment before going live.
• Step nine: choose the queue measures you will review regularly, such as the age of the oldest item and the contents of the needs-a-person lane.

The burst test in step eight is the one teams skip. It is also the one that finds the problems, because real traffic is uneven and a gentle test hides every race condition.

Do you need a queue, or is a workflow enough?

Not every agent needs a full queue. If requests arrive rarely, one at a time, and a person watches each result, a simple log may be enough. A queue earns its place when requests arrive from several channels, when the same sender may repeat themselves, when work takes long enough to overlap, or when a missed item has real cost.

Also ask whether the task needs an agent at all. If the steps are fixed and the inputs are predictable, a deterministic workflow with a queue in front is often cheaper and easier to audit. AI agent or deterministic workflow? offers a decision guide. Where the agent is needed, the queue still helps, because it separates the question of what work exists from the question of how the agent does it.

How does Be Human handle request queues?

Be Human treats the queue as part of the harness around the agent, not as an add-on. In a first engagement, we define the agent's role, the channels that feed it, the priority rules, the approval boundaries and the evidence that counts as done. We build intake, item states, leases, idempotency keys and the needs-a-person lane so that your team can read and change them. In managed operations, we monitor queue health, review stuck and escalated items, maintain integrations as channels change, and make controlled improvements to the rules, with your team approving anything that changes what the agent may do.

Frequently asked questions

What is the difference between a queue and just giving the agent a list in its prompt? A prompt list lives inside the model's context, which is temporary and can be truncated, reordered or forgotten. A queue lives outside the model in durable storage with statuses and logs. The agent reads from it, so a restart or a long day cannot erase the list.

How do I decide how long a lease should last? Base it on how long a normal item takes plus a reasonable margin, and measure that from real runs rather than estimating. A lease that is too short causes duplicate attempts, while one that is too long leaves stalled items hidden. Start cautious, review the stuck items, and adjust.

Should the agent decide priorities on its own? It can propose a priority, especially for messages that fit no rule. A written rule or a person should decide anything that affects deadlines, safety or money. This keeps the scheme explainable, which matters when someone asks why one request waited.

What should I monitor once the queue is running? Watch how old the oldest waiting item is, how many items sit in the needs-a-person lane, how often leases expire and how many incoming requests match an existing key. Rising numbers in any of these point to a problem with capacity, rules or a connected tool. Review them on a regular schedule, not only after a complaint.

Be Human editorial guidance. Examples are illustrative unless explicitly attributed. Sources provide background; implementation choices depend on your workflow.