--- title: Single vs Multi-Agent System? link: https://www.philschmid.de/single-vs-multi-agents source: philschmid-de published: 2025-06-20T00:00:00Z updated: 2025-06-20T00:00:00Z first_seen: 2026-09-27T19:29:15.293681927Z summary: Single vs. multi-agent? The real secret to building AI agents is 'read vs. write'. Learn which to use for your task and build reliable systems. content: extracted html: 2025-06-20-single-vs-multi-agent-system.html preview: file: 2025-06-20-single-vs-multi-agent-system.preview-3de6fad0dce5.webp width: 256 height: 144 alt: Single vs Multi-Agent System? color: '#111216' images: - source: https://www.philschmid.de/static/blog/single-vs-multi-agents/thumbnail.jpg original: file: 2025-06-20-single-vs-multi-agent-system.image-bfb5997e20db.jpg width: 961 height: 540 color: '#040507' - source: https://www.philschmid.de/static/blog/single-vs-multi-agents/cognition.png original: file: 2025-06-20-single-vs-multi-agent-system.image-2f734734d273.png width: 1406 height: 1230 color: '#f8f9fa' - source: https://www.philschmid.de/static/blog/agentic-pattern/multi-agent.png original: file: 2025-06-20-single-vs-multi-agent-system.image-0258fa333921.png width: 1477 height: 562 variants: - file: 2025-06-20-single-vs-multi-agent-system.image-644c2aa5d368.webp width: 320 height: 122 - file: 2025-06-20-single-vs-multi-agent-system.image-c8119f292abc.webp width: 640 height: 244 - file: 2025-06-20-single-vs-multi-agent-system.image-17937f455f37.webp width: 960 height: 365 - file: 2025-06-20-single-vs-multi-agent-system.image-211721c2bc9e.webp width: 1280 height: 487 - file: 2025-06-20-single-vs-multi-agent-system.image-49c01ddf9b8d.webp width: 1477 height: 562 color: '#fdfdfd' --- The debate on whats the right way to build Agents in the AI community heated up with Cognition's ["Don't Build Multi-Agents"](https://cognition.ai/blog/dont-build-multi-agents) and Anthropic's ["How we built our multi-agent research system"](https://www.anthropic.com/engineering/built-multi-agent-research-system). Despite their opposing titles both are surprisingly aligned. The choice between a single agent and a multi-agent system isn't about ideology, it is about picking the right tool for the right job. Before we take a closer look, let's establish a simple definition. > AI agent is a system that uses a Large Language Model (LLM) as reasoning engine to decide the control flow of an application. ## What Are Single Agents? A single-agent system operates as a “single process”. Think of it as one highly-focused worker tackling a task from start to finish. It maintains a continuous thread of thought (memory) and action (tools) to ensure that every step is informed by everything that came before it. ![cognition-single-agent](https://www.philschmid.de/static/blog/single-vs-multi-agents/cognition.png) \[Credits: [Cognition Don’t Build Multi-Agents](https://cognition.ai/blog/dont-build-multi-agents)\] **Key characteristics:** - **Sequential:** Actions are performed one after another. The agent completes step A before moving to step B. - **Unified Contenxt:** Maintains a single, continuous history of the entire conversation. Every new step has access to all previous steps, thoughts, and tool outputs. - **Stateful:** Decisions made early on directly and fully inform later actions without the need for message passing. **Pros:** - **Context Continuity**: No information is lost between steps - **Simplicity**: Easier to debug, test, and maintain - **Transparency**: Clear execution path and decision trail **Cons:** - **Sequential Bottlenecks:** Slow for tasks where parts could be handled in parallel. - **Context Window Limitations**: Eventually exceed the (useful) context window, leading to errors and forgotten details. - **Inefficiency**: May waste tokens on repetitive context and are limited to single model capabilities/instructions ## What Are Multi-Agent Systems? A multi-agent system is structured like a team. It typically involves an “lead agent" that breaks down a goal into smaller subtasks, which it then delegates to multiple "worker" agents that can operate in parallel. ![multi-agent](https://www.philschmid.de/static/blog/agentic-pattern/multi-agent.png) **Key characteristics:** - **Parallel Execution:** Subtasks can be handled simultaneously by multiple agents. - **Delegation:** A "lead" agent typically decomposes the main goal, delegates subtasks, and synthesizes the results from the worker agents. - **Distributed Context:** Each agent operates with its own context, which is often a subset of the total information. **Pros:** - **Parallelization**: Can explore multiple paths simultaneously, reducing latency. - **Specialization**: Each agent can be optimized and instructed for specific tasks - **Breadth**: Can solve complex, multi-faceted problems. **Cons:** - **Context**: Sharing the right context between agents is hard - **Coordination:** Agents may duplicate work or make conflicting decisions - **Cost:** Can be more token-intensive. (Anthropic: 15x more tokens compared to a standard chat interaction) ***Note:*** It's worth mentioning other multi-agent pattern, e.g. swarm are not using a "lead" agent. Instead they collaborate in a more peer-to-peer fashion to solve a problem, which has both single agent and multi agent characteristics, e.g. Unified Context, but individual instructions but comes with its on challenges. ## Comparison Single Agents vs Multi-Agent Systems | Aspect | Single Agent System | Multi-Agent System | | ---------------------- | ---------------------------------------------------- | ------------------------------------------------------------------------------- | | **Context Management** | Continuous, no loss | Complex sharing required | | **Execution Speed** | Sequential | Parallel | | **Token usage** | 4x chat tokens | 15x chat tokens | | **Reliability** | High, predictable | Lower, emergent behaviors | | **Debugging** | Straightforward | Complex, non-deterministic | | **Best For** | Sequential, state-dependent tasks ("write" tasks). | Parallelizable, exploratory tasks ("read" tasks). | | **Coordination** | None needed | Critical success factor | | **Example Use Case** | Refactoring a codebase, writing a detailed document. | Researching a broad market trend, identifying all board members of the S&P 500. | | **Core Strength** | Context Continuity & Reliability | Parallelism & Scalability | | **Primary Challenge** | Context Window Limits & Sequential Speed | Context Fragmentation & Coordination Complexity | ## The Universal Truths of Building Agents Despite their opposing common principles apply for building serious Agentic system. 1. **Context Engineering is everything:** Architecting systems that dynamically maintain the right information at the right time for reliable decision-making is the key to success and reliability. This isn't just prompt engineering. 2. **"Read" vs "Write" Agents:** Are important distinction isn't single vs multi-agent. it is whether your task primarily involves **reading** (research, analysis, information gathering) or **writing** (code generation, content creation, file editing). - **Read tasks** are easier to parallelize and suite multi-agent better - **Write tasks** create coordination problems when parallelized, favoring single agents - **Mixed tasks** should separate read and write phases architecturally 3. **Reliable Agents Need Different Tooling:** Building reliable agents requires more than a good model. It requires a robust infrastructure for durable execution to survive failures, observability to debug behavior, and evaluation to measure what actually matters. 4. **Economic Viability and Model Improvements:** Models themselves are improving at an incredible rate. Don't over-engineer a solution for today that a much simpler can solve for you tomorrow. * * * Thanks for reading! If you have any questions or feedback, please let me know on [Twitter](https://twitter.com/_philschmid) or [LinkedIn](https://www.linkedin.com/in/philipp-schmid-a6a2bb196/).