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AI agents do not usually “lock” when they have permission to act. Under heavy demand, they congest: new work arrives faster than the system can complete it, queues grow, and delays spread across models, tools, and connected services. Authorization decides what an agent may do; capacity controls decide how much work the system accepts and how it behaves when demand exceeds what it can sustain.
How agent traffic turns into congestion
Congestion is a relationship between the rate work arrives, how long each job occupies resources, and the system’s sustainable processing capacity. When arrivals exceed completions, the difference accumulates as backlog. A queue makes waiting work visible and can smooth a brief burst, but it does not make that work finish faster or create processing capacity. Akka explains the distinction in its guide to queued agent work during traffic spikes.
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Agent workflows can make the imbalance harder to spot than a simple request-per-second figure suggests. A workflow may call a model repeatedly, invoke tools in sequence, retry after failures, or hold a connection open while it works. The duration and resource use of a task therefore matter alongside the number of tasks admitted.
Why long-running inference can stress the GPU cache
For agentic batch inference, one pressure point is the GPU key–value (KV) cache, which stores state used during model generation. The 2026 ICML paper “CONCUR: High-Throughput Agentic Batch Inference of LLM via Congestion-Based Concurrency Control” describes sustained, cumulative cache pressure as agent state persists. The authors call a resulting cache-efficiency collapse “middle-phase thrashing” and propose using runtime cache signals to control how many agents run concurrently.
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In the workloads they studied, the authors report throughput improvements of up to 4.09× on Qwen3-32B and 1.90× on DeepSeek-V3 with CONCUR. Those are results for that system and those evaluated workloads, not a general performance guarantee or a safe-concurrency figure for other models and deployments.
Find the tightest limit across the runtime path
An agent’s apparent capacity may be constrained by any component it depends on: its scheduler, model-serving layer, gateway, tool, connector, API, communication channel, or downstream service. Microsoft’s Copilot Studio planning guidance names these as possible rate-limit scopes and warns that the limiting point in the runtime path shapes the user experience. A gateway may accept work that a connector or external API cannot process at the same pace.
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Map the full path for each important workflow and establish what each layer limits. Microsoft recommends estimating both average and peak traffic, including connected services, and looking at short windows such as minutes and hours. A weekly or monthly average can hide a concentrated burst that overwhelms a service. Its guidance is specific to Copilot Studio planning; numerical product limits are handled in separate quota guidance and may change. See Microsoft Learn’s Copilot Studio throughput and rate-limit planning guide for the planning approach.
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There is no single control that handles every form of excess demand. The useful choice depends on where congestion forms, what signal reveals it, and whether the system should admit, delay, reject, or shed work.
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| Control | Where it acts | What it helps manage | What to watch |
|---|---|---|---|
| Admission control | Agent scheduler or model-serving layer | How many tasks or agents run concurrently | Use a relevant live signal—such as active work or KV-cache pressure—rather than assuming a fixed concurrency limit fits every workload. |
| Rate and usage limits | Gateway, API, tool, connector, or downstream service | Requests, tokens, connection duration, or other consumption | Limits at one layer do not remove tighter limits elsewhere in the path. |
| Bounded queues | Queue or runtime | How much work may wait | Set what happens when the queue is full; an unbounded queue can turn overload into growing delay and resource use. |
| Backpressure | Between a consumer and work producers | How quickly new work enters when processing falls behind | Check whether upstream producers can slow down, pause, or respond to rejected work. |
| Elastic capacity | Compute or serving layer | Temporary increases in processing capacity | Account for how quickly capacity can grow and for its cost and scaling limits. |
Admission control and cache-aware concurrency
Admission control limits active work before it consumes more scarce resources. The CONCUR paper is an inference-specific example: it uses runtime cache signals to inform agent-level concurrency. Its findings support measuring resource pressure rather than treating the number of simultaneous agents as a universal constant.
Gateway limits for different kinds of consumption
AWS describes Amazon Bedrock AgentCore Gateway as supporting per-user ceilings for requests, model tokens, and connection duration across tools, models, and agents behind the gateway. AWS also describes temporal policies that can evaluate sequences of actions and track a session budget. These are platform-specific capabilities, not a universal feature set. The distinctions matter operationally: a retry loop may drive request volume, a reasoning-heavy task may consume tokens, and a long-held connection may occupy capacity even without a high request rate. Details are in AWS’s AgentCore capabilities announcement.
Queues, backpressure, and scaling
A queue can absorb a short burst if the work is allowed to wait and the backlog drains before the next burst. It cannot resolve a sustained mismatch between arrivals and completions on its own. Backpressure addresses that mismatch by reducing or pausing intake when a consumer is saturated; adding capacity can increase the completion rate when resources are available. Akka presents backpressure and capacity growth as parts of managing queued agent work, but the right behavior depends on the application: some tasks can wait, while others need to be rejected or abandoned rather than allowed to become stale.
Include the cost of the control layer
Traffic shaping and rate limiting can prevent a burst from overwhelming downstream buffers, but controls have their own trade-offs. Google Research’s account of the Carousel traffic-shaping work describes potential CPU and memory overhead, accuracy costs, and head-of-line blocking, where one delayed item can hold up others. Evaluate the control layer as part of the system: measure its overhead and check whether it adds latency or changes which work gets delayed.
Quick Recap
Operational checks before a pilot
- Estimate peaks, not just averages. Model expected demand in short windows and include bursts, retries, long-running tasks, and traffic to connected services.
- Trace the request path. Record relevant limits and capacity constraints at the scheduler, model service, gateway, tools, connectors, APIs, and downstream systems.
- Measure the resource that is actually under pressure. Depending on the workflow, that may be active-agent count, cache use, request rate, token consumption, connection duration, queue depth, or service latency.
- Set explicit intake and waiting bounds. Decide how many tasks may run, how much work may queue, and whether excess work is delayed, rejected, or shed.
- Test overload behavior. Include retry loops and slow or rate-limited dependencies. Confirm that failures do not trigger more retries than the system can handle.
- Monitor backlog and latency together. A growing queue can signal that completions are falling behind even before a hard resource limit is reached. Watch for both user-facing delays and the rate at which the backlog drains.
- Check control overhead. Confirm that admission checks, shaping, and policy evaluation do not introduce unacceptable latency or become a new bottleneck.
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