Single-Agent vs Multi-Agent Architectures

In a single-agent system, one LLM handles all reasoning, planning, and action selection.

How it works: - One agent receives the task - Same LLM reasons about all aspects - Single context window holds all information - One orchestration loop manages execution

Advantages: - Simpler to build, test, and debug - No coordination overhead - Easier to maintain consistency - Lower latency (no agent-to-agent communication) - More predictable behavior

When to use single-agent: - Task fits in one context window - Doesn't require fundamentally different skills - Speed matters - You want simpler debugging - Starting out (iterate to multi-agent if needed)

Most production agent systems today are single-agent. Don't over-engineer.

Multi-agent systems use multiple specialized agents that communicate and collaborate.

Common patterns:

Manager + Workers - Manager agent decomposes tasks and assigns to workers - Workers execute specific subtasks - Manager synthesizes results

Pipeline - Agents process sequentially (research → analyze → write → review) - Each agent specializes in one phase - Output of one becomes input to next

Debate/Critique - Multiple agents propose solutions - Critic agent evaluates and selects best - Improves quality through adversarial checking

Swarm/Collaborative - Agents work in parallel on different aspects - Communicate to share findings - Converge on final answer

When multi-agent makes sense: - Task genuinely requires different expertise - Parallel processing provides speedup - Quality benefits from multiple perspectives - Single context window can't hold everything

Multi-agent systems introduce significant complexity:

Coordination overhead: - Agents must communicate clearly - Information gets lost or distorted between agents - Coordination takes time and tokens

Consistency problems: - Different agents may contradict each other - Maintaining shared understanding is hard - State synchronization across agents

Debugging difficulty: - Failures can occur anywhere in the pipeline - Agent-to-agent interactions create new failure modes - Tracing issues through multiple agents

Cost multiplication: - Each agent uses LLM tokens - Communication uses additional tokens - Parallel agents multiply costs

Common anti-pattern: Building multi-agent when single-agent would work. Multi-agent looks impressive but often adds complexity without benefit. Start simple.

Decision framework for agent architecture:

Start with single-agent when: - You're building your first agent system - Task is well-defined with clear scope - Speed and simplicity matter - You want predictable behavior

Consider multi-agent when: - Single agent consistently fails at task complexity - Clear separation of concerns exists - Different subtasks need different tools/prompts - Parallel processing provides real benefit - You have resources to handle the complexity

Hybrid approach: Start single-agent. Monitor where it struggles. Add specialized sub-agents only for specific bottlenecks. This gives you multi-agent benefits where needed without full complexity.

Example evolution: 1. Single agent handles customer support 2. Add specialized "refund processor" sub-agent for complex refunds 3. Keep main agent for everything else 4. Only add more specialists when data shows need

Practical aspects of each architecture:

Single-agent implementation: - One orchestration loop - Unified tool set - Single prompt template (or small set) - Straightforward state management - Standard logging and monitoring

Multi-agent implementation needs: - Agent communication protocol - Task assignment logic - State sharing mechanism - Conflict resolution rules - Centralized logging across agents - Timeouts and failure handling per agent

Frameworks: - LangGraph: Good for both, with explicit state machines - AutoGen: Designed for multi-agent conversations - CrewAI: Multi-agent with role-based agents - Custom: Often simpler for single-agent

Testing strategy: - Single-agent: Test the one agent thoroughly - Multi-agent: Test each agent, then integration, then end-to-end - Multi-agent testing is significantly more complex