Single Agent vs Multi-Agent Architectures

Single-Agent Architecture

In a single-agent architecture, one agent handles every user interaction. It has access to all tools, carries all context, and manages all conversation state. When the user switches from asking about database performance to requesting a code review to checking deployment status, the same agent handles all three requests, drawing from the same tool set and the same context.

The primary advantage is simplicity. There is one prompt to maintain, one context window to manage, one conversation thread to track, and one set of tools to configure. Debugging is straightforward because the path from user input to response passes through a single agent. There are no inter-agent communication failures, no delegation errors, and no coordination overhead. When something goes wrong, you know exactly where to look.

The limitation appears with tool count and context complexity. A single agent with 30 tools produces more tool selection errors than one with 10, because the model has to distinguish between more options. A single agent that needs to maintain context for three different active topics (deployment, code review, database optimization) may mix up details between them because everything lives in the same context window. And a single agent with a very long system prompt (covering all possible behaviors, tool usage patterns, and edge cases) may follow instructions less reliably than one with a focused, concise prompt.

Multi-Agent Architecture

In a multi-agent architecture, specialized agents handle different aspects of the workflow. A customer support system might have an intake agent that classifies requests, a technical support agent with database and logging tools, a billing agent with payment system access, and a escalation agent that creates tickets for human review. Each agent has a focused prompt, a small tool set relevant to its specialty, and behavioral guidelines specific to its role.

The advantages are specialization and scalability. Each agent's prompt can be concise and focused, which improves instruction following. Each agent's tool set is small, which improves tool selection accuracy. Agents can be developed, tested, and updated independently, which simplifies the development workflow. And different agents can use different models, putting the most capable (and expensive) model on the most complex tasks while using cheaper models for routine operations.

The disadvantages are coordination complexity and communication overhead. Every inter-agent handoff is a potential failure point. Information can be lost or distorted when passed between agents. The user may experience inconsistency if different agents have different communication styles or make contradictory statements. And debugging becomes harder because a bad response might originate from the wrong agent being selected, from a communication failure between agents, from a coordination timing issue, or from the actual agent that produced the response.

Memory in Multi-Agent Systems

Memory architecture is fundamentally more complex in multi-agent systems. In a single-agent system, all memories belong to one agent, and retrieval is straightforward. In a multi-agent system, you face questions that do not arise in single-agent designs: should all agents share the same memory, or should each have its own? If agents share memory, how do you prevent one agent from overwriting memories that another agent relies on? If agents have separate memories, how do you keep them consistent when they observe the same facts?

The most practical approach is a shared memory layer that all agents can read from and write to, with metadata that tracks which agent stored each memory. This gives every agent access to the full knowledge base while maintaining provenance. Retrieval is scoped to the querying agent's context, so a billing agent searching for "payment history" gets billing-relevant memories while a technical agent searching for "error history" gets technical memories, even though both draw from the same store.

Adaptive Recall works well as a shared memory layer for multi-agent systems because its entity graph naturally connects memories across agent boundaries. When the technical support agent stores a memory about a customer's server configuration and the billing agent stores a memory about the same customer's subscription tier, the entity graph connects both memories through the customer entity. Any agent querying about that customer can retrieve both pieces of information through spreading activation, regardless of which agent originally stored them.

The Hybrid Approach

Most production assistants use a hybrid architecture that combines elements of both patterns. A single primary agent handles the user-facing conversation and most routine tasks. For specific complex operations (deep research, multi-system coordination, document generation), the primary agent delegates to specialized sub-agents that execute the task and return results. The user interacts with one consistent agent, preserving conversation coherence, while complex work is distributed to specialists behind the scenes.

This hybrid pattern gets the best of both worlds: the simplicity and consistency of single-agent interaction with the specialization benefits of multi-agent execution. The primary agent maintains the conversation state, manages the user relationship, and handles memory. Sub-agents are stateless workers that receive a task description, execute it using their specialized tools, and return results. This keeps the architecture manageable while enabling sophisticated task handling.

Decision Criteria

Start with a single agent. Add agents only when you have a specific problem that multi-agent architecture solves. If your single agent's tool count is causing selection errors, consider splitting tools across specialized agents. If your system prompt is getting too long and instruction following is degrading, consider moving specialized behaviors into separate agents. If you need different models for different task types, multi-agent makes that possible. But do not start with multi-agent because it seems more sophisticated. The additional complexity has real costs, and the benefits only materialize for genuinely complex workflows.