Single-agent or multi-agent? Choosing the right architecture for your AI system

AI agents are getting smarter, but they're also getting harder to scale. If you've tried to build an agentic system you know about the latency spikes, memory issues, and coordination failures that weren't obvious until your app hit production.

That's why every agentic app builder faces a fundamental architectural decision: should you consolidate all reasoning into a single agent or distribute work across multiple specialized agents? This choice determines whether you're managing one reasoning pathway or coordinating multiple agents, each with different performance, cost, and complexity trade-offs.

This article covers when to choose single-agent versus multi-agent architectures and how to build both with infrastructure that handles production at scale.

What is agentic AI?

Before we dive into single-agent versus multi-agent architectures, it helps to understand agentic AI in the first place. Agentic AI makes decisions on its own. You give it a goal, and it figures out the steps without you micromanaging every prompt. This is different from ChatGPT-style apps where you guide each interaction. The autonomy changes what your infrastructure needs to support, whether you're running one agent or coordinating multiple.

So what actually makes an AI system "agentic"? There are four foundational characteristics that work together to create systems that go beyond simple automation. Think of these as the building blocks that transform a basic AI tool into something that can genuinely act on its own behalf:

• Autonomous decision-making: The agent chooses its own actions without requiring human intervention for every step
• Goal-directed behavior: The system works toward specific objectives, adjusting its approach based on results
• Environmental interaction: The agent perceives and responds to changes in its operating environment
• Adaptive behavior: The system refines its approach through memory and state management—storing context from past interactions to inform future decisions

Not every problem needs a fully autonomous agent. These characteristics help you figure out when you actually need agentic capabilities versus simpler automation.

What are single-agent systems?

Single-agent systems take a centralized approach by consolidating all reasoning, memory, and tool execution into one AI instance. It's like having one brain handling everything rather than distributing work across multiple specialized systems.

To make this work, you need four core components:

• LLM reasoning engine: Processes inputs and makes decisions about what should happen next
• Memory system: Provides context-aware behavior across sessions by embedding memory context into prompts, letting the agent reason based on both current inputs and prior knowledge
• Tool integration layer: Connects your agent to external data sources and capabilities, invoking specific actions like querying databases or triggering integrations
• Planning module: Decomposes complex tasks into executable steps and decides the sequence needed to achieve objectives

When these components work together effectively, they enable autonomous decision-making while maintaining state and executing multi-step workflows that would be impossible for simpler systems.

What are multi-agent systems?

Multi-agent systems distribute work across specialized agents that need explicit coordination mechanisms. Each agent focuses on what it does best, but they need ways to communicate and coordinate effectively.

At their core, multi-agent systems coordinate multiple autonomous agents that collaborate or compete within a shared environment. Performance in these systems depends critically on coordination mechanisms, rationality, and knowledge management.

Agents coordinate through message passing and shared state. One agent's output becomes another's input, with routing logic deciding which agent handles what. The coordination pattern—whether hierarchical, sequential, or parallel—depends on your workflow requirements.

Multi-agent systems let you specialize, but they introduce coordination overhead you need to account for. Each agent interaction requires LLM calls, which drives up token consumption. As you add more agents, communication overhead scales faster than linear because agents need to coordinate their work. That communication latency cascades through your workflow: what should execute in parallel gets serialized by coordination steps. This overhead compounds as you scale, which is why choosing suitable coordination mechanisms becomes essential. Inappropriate mechanisms create bottlenecks that prevent linear scaling, turning what should be a performance advantage into a coordination nightmare.

What's the difference between single-agent & multi-agent systems?

The architectural choice between single and multi-agent systems comes down to trade-offs in latency, cost, complexity, and task completion rates:

These differences show that performance advantages vary depending on your task type.

When to use single-agent systems

Single-agent architecture works best for straightforward workflows where the task stays within one unified reasoning context. Most apps start here because it's simpler, and that makes sense.

Here's what favors single-agent systems:

• Low task complexity: Workflows can handle everything in a single agent context without becoming unwieldy.
• No strict security boundaries: Systems don't need compliance-driven separation between different parts.
• Simple debugging needs: Troubleshooting benefits from having everything in one place where you can see the whole execution path.
• Latency-sensitive requirements: Apps need fast response times without the communication overhead that comes from coordinating multiple agents.

These characteristics work together to define where single-agent architecture delivers the best outcomes. When your workflows follow well-defined, sequential patterns with clear inputs and outputs, a single agent can handle the job efficiently.

Single-agent systems also have resource and cost benefits. You get faster response times for simple tasks, more efficient resource utilization for deterministic workflows, and debugging becomes much simpler when things break.

You'll know you've hit the limits of single-agent architecture when your agent starts making poor tool choices or exhibits chaotic execution despite having clear workflows to follow.

When to use multi-agent systems

Multi-agent systems become necessary when you hit specific constraints that single agents can't handle. The coordination overhead is real, but you get better task completion rates and lower compute costs if you architect it right.

Some examples of scenarios that make multi-agent coordination worth the added complexity:

• Hard security boundaries: Security or compliance requirements mandate architectural separation and isolation between agents
• Multi-domain scaling: Different tasks need specialized agents that can scale independently without affecting each other
• Organizational separation: Different teams or business units need to operate within the same system while maintaining clear boundaries

These requirements make multi-agent architecture necessary rather than optional, pushing you toward the more complex approach despite the overhead it introduces.

Build single & multi-agent systems with Redis

Whether you choose single-agent or multi-agent architecture depends on your task complexity, coordination requirements, and performance goals. But in practice, both architectures share the same fundamental infrastructure challenge. They need sub-millisecond latency for hot state management, semantic search capabilities for context retrieval, and persistent memory that survives server restarts.

Redis addresses these requirements through its in-memory database and native vector search capabilities. The infrastructure matches how production agents actually operate: hot context stays in memory for instant access with eviction policies managing cache lifecycle, while semantic history lives in vectors for retrieval. Whether you're building a single agent with LangGraph checkpointing or coordinating multiple agents through Pub/Sub and Streams, Redis provides the performance characteristics that agentic systems demand.

Ready to build your agent system? Try Redis free to get started with sub-millisecond operations and vector search, or meet with our team to discuss your specific architecture requirements.