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AI agents do not have to be blocked by a security rule to become unusable. When requests arrive faster than a system can finish them, work queues up and shared resources become congested. Authorization determines whether an agent may act; capacity controls determine how much work the runtime accepts and what happens when it cannot keep up.
How an agent traffic spike turns into congestion
Congestion is a relationship among arrival rate, work duration, and sustainable capacity. If new work arrives faster than it completes, the unfinished work becomes a backlog. A queue can make that waiting work visible and orderly, but it does not make it finish sooner: Akka’s guide puts it plainly, “A queue adds no capacity, so what drains the backlog is the capacity the runtime added.”
Agent workflows make the calculation more complicated than counting incoming prompts. An agent may run through multiple model calls and tools, retry failed actions, hold a connection open, or retain state between steps. A burst of users can therefore create overlapping work that consumes model-serving resources, gateway capacity, connectors, and downstream services at the same time.
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A system can have room left in one resource and still slow down elsewhere. The practical question is not just how many agents are allowed to start; it is whether the full runtime path can sustain their work at the rate and duration users require.
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Why long-running inference can slow before memory is full
One example comes from the 2026 ICML paper “CONCUR: High-Throughput Agentic Batch Inference of LLM via Congestion-Based Concurrency Control” by Qiaoling Chen and co-authors. The paper describes sustained pressure on the GPU key-value (KV) cache as agentic batch inference runs and agent state accumulates. Its authors call the resulting cache-efficiency collapse “middle-phase thrashing”: throughput can degrade before the cache reaches its memory limit.
CONCUR uses runtime cache signals to control concurrency proactively at the agent level. In the workloads reported by the paper, the authors found throughput improvements of up to 4.09× on Qwen3-32B and 1.90× on DeepSeek-V3. Those are results for the paper’s system and tested workloads, not general performance guarantees or a safe concurrency target for other models and deployments.
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Find the tightest limit in the whole request path
An agent’s effective throughput can be constrained at several points: the agent runtime, model-serving layer, gateway, tool, API, connector, communication channel, or a downstream service. Microsoft Learn’s Copilot Studio planning guidance calls out these different scopes and states, “The lowest limit in the runtime path determines the user experience.” A model endpoint that can accept more work will not help if a connector or downstream API is already throttling requests.
The Tool Desk
Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →Map the path each common workflow follows, then identify the limits and resource signals at each layer. Consider requests per interval, token consumption, active work, connection duration, cache pressure, and queue depth as appropriate. AWS, for example, describes Amazon Bedrock AgentCore gateway ceilings for requests, model tokens, and connection duration, along with temporal policies that can evaluate action sequences and session budgets. These are platform-specific capabilities; the relevant controls depend on the runtime a team uses.
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- EIGHT CHANNEL LPDDR5X - LPDDR5X is a new ground breaking memory small form factor installed on-board. With blazing speeds up to to 8000MT/s, it runs 1.5x faster than the DDR5 SODIMMs; 90% better performance over DDR5 SODIMMs in video conferencing and photo editing; 30% better performance in productivity apps; 12% better performance in digital content workloads.
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Plan for concentrated bursts rather than relying only on daily, weekly, or monthly averages. Microsoft recommends examining short windows such as minutes and hours, including both average and peak profiles, and accounting for connected services when estimating traffic. A low average can conceal a peak that overwhelms the slowest component.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose controls for the failure mode
There is no universally best control. The right choice depends on where work accumulates, what resource is under pressure, and whether the system can delay or reject work without breaking the workflow.
| Control | Where it acts | What it does when demand is too high | Trade-off to evaluate |
|---|---|---|---|
| Admission control | Agent scheduler or model-serving runtime | Limits how much work becomes active; may delay or refuse new work based on capacity signals | Can protect a constrained resource, but delayed work affects completion time. CONCUR is a workload-specific example of cache-signal-based agent concurrency control. |
| Rate or consumption limits | Gateway, tool, model, API, or downstream service | Caps measures such as request rate, token use, or connection duration | A single metric may miss other consumption patterns. AWS describes multiple per-user gateway ceilings for AgentCore. |
| Bounded queue | Queue before a worker or service | Holds a defined amount of unstarted work; excess work must be delayed, rejected, or shed | Makes waiting visible, but does not increase processing capacity. A queue that is allowed to grow without bound can turn a brief spike into long waits. |
| Backpressure | Between the work producer and the constrained consumer | Signals the producer to slow or pause intake as processing falls behind | Can help keep intake near sustainable processing capacity, but changes how upstream callers experience delay or refusal. Akka describes this approach in its vendor guide. |
| Elastic capacity | Compute or service layer | Adds capacity as demand rises and can remove it as demand falls | Capacity may not expand instantly, and scaling does not remove a bottleneck in another layer. |
Control layers also have costs. Google Research’s account of the 2017 Carousel traffic-shaping work notes that end-host shaping can add CPU and memory use, reduce accuracy, or cause head-of-line blocking. Measure the control mechanism’s overhead and its effect on tail latency, not just whether it reduces a queue.
Quick Recap
Plan and test for peak traffic
- Map the runtime path. List the model calls, gateways, tools, connectors, APIs, and downstream services used by important workflows. Record the relevant limits at each point.
- Estimate bursts in short windows. Model both typical and peak traffic over minutes and hours, including overlapping agent runs, retries, token demand, and open connections.
- Measure the constrained resource. Track active work, cache pressure, request and token rates, connection duration, backlog, and latency where they apply. Do not assume a single request-rate counter describes the load.
- Set explicit bounds and excess-load behavior. Decide how much active work and queued work the system will accept, and whether it delays, rejects, or sheds work beyond those limits. Ensure producers receive useful signals if intake must slow.
- Load-test connected services and recovery. Test realistic peaks, tool and downstream throttling, and retry behavior. Retries can add work precisely when a service is struggling.
- Pilot while watching queues and latency. Monitor backlog and completion times alongside resource use. A queue that keeps growing shows that accepted work is exceeding what the system can finish, even if requests are still being accepted.
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