Scaling Agent Systems: Architecture for Growth

Every agent system hits a wall. The prototype works beautifully with 10 users. At 100, things slow down. At 1,000, it collapses. The architecture decisions you make early determine whether that wall is at 1,000 users or 1,000,000.

I have scaled agent systems through three orders of magnitude. The lessons were expensive. Here is what I would do differently from day one.

The Fundamental Scaling Problem

Agent systems are expensive and slow relative to traditional services. A simple API endpoint costs fractions of a cent and responds in milliseconds. An agent task costs dollars and takes seconds to minutes. When you multiply that by thousands of concurrent users, the numbers get uncomfortable fast.

The cost problem and the performance problem are related but require different solutions. Cost optimization is about doing less work per task. Performance optimization is about doing work faster and more concurrently. You need both.

Separate Coordination from Execution

This is the most important architectural decision. The coordination layer manages what needs to happen. The execution layer does the actual agent work. They must be separate services that scale independently.

The coordination layer is lightweight. It receives task requests, determines which agents need to run, manages task queues, tracks progress, and aggregates results. It handles thousands of concurrent requests with minimal compute because it is not running any LLM inference.

The execution layer is heavyweight. It runs agents, makes LLM API calls, executes tools, and manages agent state. Each worker handles one or a few tasks at a time because each task is computationally expensive.

With this separation, you can scale coordination cheaply (add more lightweight instances) and scale execution precisely (add workers based on queue depth and cost budget). Without it, you are scaling expensive compute to handle cheap coordination work.

The Model Tier Strategy

Not every agent task needs Claude Opus. Not every sub-step needs the same model. Implementing a tiered model strategy is the single highest-impact cost optimization available.

Tier 1: Fast and cheap. Small models for classification, routing, simple formatting, and template-based generation. These handle 60-70% of all LLM calls in a typical agent system.

Tier 2: Capable and moderate. Mid-range models for summarization, moderate reasoning, and standard generation tasks. These handle 20-30% of calls.

Tier 3: Powerful and expensive. Top-tier models for complex reasoning, creative generation, and tasks where quality directly impacts the user. These handle 5-10% of calls but produce the outputs users actually see.

The routing decision happens at the coordination layer. Each task type is mapped to a model tier. When you need to cut costs, you can shift tasks down a tier. When you need higher quality, shift them up. This flexibility is only possible if the architecture supports it from the start.

Caching: Your Best Friend at Scale

Caching in agent systems is underutilized because people think of agent outputs as unique. They are not. In practice, agents receive many similar or identical requests, especially in enterprise deployments.

Exact-match caching. Hash the complete input (prompt + context + parameters) and cache the result. Identical requests get cached responses instantly. For FAQ-style agents, this alone can handle 30-50% of requests without any LLM calls.

Semantic caching. Embed the input and check if any cached input is sufficiently similar. If the similarity exceeds a threshold, return the cached result. This is riskier because "sufficiently similar" is a judgment call, but it dramatically increases cache hit rates.

Component caching. Cache intermediate results, not just final outputs. If an agent's first step is always to retrieve and summarize relevant context, cache that summarization. The agent still reasons over it, but you skip the most expensive retrieval and summarization step.

At scale, aggressive caching reduces LLM API costs by 50-70%. That is not an optimization. It is the difference between a viable business and burning money.

Queue Architecture for Variable Workloads

Agent tasks have wildly variable execution times. A simple classification takes 2 seconds. A complex multi-step research task takes 5 minutes. Your queue architecture needs to handle this gracefully.

Use priority queues with separate worker pools. High-priority interactive tasks get dedicated workers that keep latency low. Background batch tasks share a separate pool that optimizes for throughput over latency. This prevents a surge of batch work from starving interactive users.

Implement task timeout hierarchies. The overall task has a timeout. Each agent step within the task has its own timeout. Each LLM call within a step has its own timeout. When a step times out, the agent can retry or skip it. When the overall task times out, it fails gracefully with partial results.

Dead letter queues catch tasks that fail repeatedly. Instead of retrying forever and burning tokens, failed tasks go to a dead letter queue for analysis. This preserves your budget and gives you signal about systematic problems.

Resilience at Scale

At scale, failures are not exceptions. They are constants. Every minute, something fails. An LLM API returns a 500. A tool times out. A worker crashes mid-task. Your architecture must treat failure as a normal operating condition.

Circuit breakers prevent cascading failures. When an external service starts failing, the circuit breaker trips and routes requests to a fallback instead of hammering the failing service. This protects both your system and the external service.

Bulkheads isolate failure domains. A misbehaving tenant or a surge in one task type should not affect other tenants or task types. Separate resource pools for different domains ensure that one overloaded segment does not drag everything down.

Graceful degradation means having a plan for reduced capacity. When your LLM budget is exhausted, switch to cached responses and smaller models. When a tool is unavailable, skip the step and note the limitation. Users getting reduced-quality results is better than users getting errors.

Cost Governance

At scale, cost management is not an optimization project. It is an ongoing operational concern.

Set per-tenant, per-task-type, and per-time-period cost budgets. Track spending in real time. Alert when spending exceeds thresholds. Automatically throttle or downgrade when budgets are exhausted.

Build cost visibility into every level. The coordination layer should report cost per task. The execution layer should report cost per agent step. Dashboards should show cost trends by tenant, task type, and model tier.

The teams that scale successfully are the ones that treat cost as a first-class metric alongside latency and quality. The teams that ignore cost until the invoice arrives are the ones that get shut down by finance.