How to Choose the Right Agent Architecture

Assess Task Complexity

Start by analyzing the tasks your agent system will handle. Count the distinct steps in a typical task. Identify how many different types of expertise or capability are needed. Determine whether steps are sequential (each depends on the previous) or independent (can run in parallel). Note whether the task structure is fixed (every task follows the same steps) or dynamic (the steps depend on the specific input or intermediate results).

Low complexity (1-5 steps, single expertise, fixed structure): a customer support bot that searches a knowledge base and responds, a code review tool that analyzes a diff and produces comments, a data extraction agent that reads documents and fills a template. These tasks fit cleanly in a single-agent architecture.

Medium complexity (5-15 steps, 2-3 areas of expertise, mostly sequential): a content creation workflow that researches, outlines, drafts, and reviews. A data pipeline that collects, cleans, analyzes, and reports. These tasks work well with either a capable single agent or a pipeline architecture, depending on whether the distinct stages benefit from specialized prompts and tools.

High complexity (15+ steps, multiple areas of expertise, dynamic structure): a software development agent that handles requirements, design, implementation, testing, and deployment across frontend, backend, and infrastructure. A research system that dynamically determines what to investigate based on initial findings. These tasks generally require multi-agent architecture with a coordination mechanism that can handle the dynamic task decomposition.

Evaluate Scale Requirements

Scale requirements determine the runtime pattern. How many tasks arrive per hour? Is the arrival rate steady or bursty? How many tasks need to be processed concurrently? What is the expected growth trajectory?

Low scale (fewer than 100 tasks per hour, steady arrival): a single agent instance handling tasks sequentially is sufficient. No scaling infrastructure needed. The agent can run as a simple process or serverless function.

Medium scale (100-10,000 tasks per hour, moderate variability): queue-based architecture provides natural load balancing and scaling. Add consumers when the queue grows, remove them when it shrinks. Most production agent deployments fall in this range.

High scale (10,000+ tasks per hour, bursty): queue-based architecture with auto-scaling and potentially multiple queues for different task priorities. Event-driven architecture on serverless infrastructure provides automatic scaling without capacity management. At this scale, cost optimization becomes a primary concern because even small per-task cost reductions save significant money across thousands of daily executions.

Also consider the growth trajectory. If you expect to go from 100 tasks per hour to 10,000 within a year, choosing a scalable architecture from the start avoids a painful migration later. If the scale will remain stable, optimize for simplicity over scalability.

Determine Latency Constraints

Latency requirements often eliminate architecture options that would otherwise be good fits.

Real-time (sub-second to a few seconds): chat interfaces, interactive tools, live decision support. Single-agent architecture with a fast model and minimal tool calls. No queuing, no multi-step pipelines. Every additional step adds latency that degrades the user experience.

Near-real-time (seconds to a minute): email responses, ticket triage, content moderation. Single-agent or short pipeline architectures with event-driven activation. Queue-based processing is acceptable if the queue is rarely deep. Parallelism can reduce latency for tasks with independent steps.

Batch (minutes to hours): report generation, data analysis, content creation, code review. Any architecture pattern works because latency is not the binding constraint. Optimize for quality, cost, and reliability instead. Pipeline and multi-agent architectures are most valuable here because the quality improvement justifies the additional latency.

For each latency tier, estimate the time budget for each step and verify that the total fits within requirements. Include LLM API latency (typically 1-10 seconds per call depending on model and prompt size), tool execution time (milliseconds for local tools, seconds for API calls), and coordination overhead (negligible for single-agent, meaningful for multi-agent with orchestration).

Match Patterns to Requirements

With complexity, scale, and latency assessed, match them against the available patterns using these guidelines.

Single-agent is the default choice. Use it unless you have a specific reason not to. It handles low-to-medium complexity at any scale (since individual agents are independent) with the lowest operational overhead. Start here and graduate to other patterns only when you encounter concrete limitations.

Pipeline is the right upgrade when your task has natural sequential stages that benefit from independent optimization. If you can draw the task as a flowchart with a single path from start to finish, a pipeline is a good fit. If the flowchart branches or loops frequently, consider orchestrated multi-agent instead.

Multi-agent with orchestrator is appropriate when the task requires multiple types of expertise, benefits from parallelism, or has a dynamic structure. The orchestrator adds coordination overhead but provides the flexibility to handle tasks that single-agent and pipeline architectures cannot.

Supervisor is a layer you add to multi-agent systems when reliability is critical. If agent failures must be automatically detected and recovered, add a supervisor. If the system can tolerate occasional manual intervention for failed tasks, a supervisor may be unnecessary overhead.

For runtime patterns, the default choices are: event-driven for reactive workloads (responding to triggers), queue-based for throughput-oriented workloads (processing backlogs), tick-based for proactive workloads (monitoring and maintenance), and GenServer for stateful workloads (maintaining context across interactions). These defaults can be combined: a GenServer agent activated by events from a queue, supervised for fault tolerance.

Validate with a Prototype

Before committing to a full implementation, build a minimal prototype of the selected architecture and test it against representative tasks. The prototype does not need production-grade infrastructure. It needs to demonstrate that the architecture handles the actual task structure, that the agent produces acceptable results, that the latency meets requirements, and that the cost per task is within budget.

Run at least 20 representative tasks through the prototype. Examine not just the final outputs but the intermediate steps: which tools did the agent call, in what order, with what inputs, and how did it interpret the results? These details reveal whether the architecture supports the agent's reasoning process or creates friction that degrades performance.

Measure the cost of each prototype run carefully. Multiply by your expected daily volume. If the projected daily cost exceeds your budget, either optimize the architecture (reduce LLM calls, use smaller models for simpler steps, cache repeated operations) or reconsider whether the task should be fully automated versus partially automated with human involvement for the expensive steps.

The prototype also reveals whether your initial complexity assessment was correct. If the single-agent prototype handles the task well, do not upgrade to multi-agent because you expected to need it. If the pipeline prototype struggles because stages need to communicate bidirectionally, the task might need an orchestrator instead. Let the prototype results guide the final architecture decision.