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You’ve been working with two assistants: Zippy and Savvy.
  • Zippy handles quick tasks and orchestration.
  • Savvy handles research and summarization.
Together they’re useful — but they still have important gaps. Ask them to recall something from last week, and they have no idea. Ask them to run code, query a database, or execute a script, and they can’t. They lack persistent memory and a real execution environment. To fill these gaps we add two more specialists:
  • Meshy — memory specialist. Meshy stores and retrieves persistent data across sessions: user preferences, past conversations, and important facts. When Zippy needs to recall something from last week, he asks Meshy.
  • Cody — code and automation specialist. Cody runs scripts, queries databases, and executes tasks that require a real computing environment. When Zippy needs a computation performed or a script executed, he delegates to Cody.
With all four working together, Zippy becomes more than an assistant — he becomes an orchestrator. Instead of trying to do everything himself, Zippy coordinates specialists so each component stays focused and reliable. A single-agent system is one LLM running in a loop with tools attached. Some problems are better solved by splitting work across multiple agents, each specialized for part of the job. A single agent with dozens of tools and an enormous system prompt can get confused; splitting the workload keeps each agent simpler and more dependable. For example, three agents with five tools each is typically easier to manage than one agent with 15 tools. Common multi-agent design patterns below show trade-offs between parallelism, sequential refinement, and adversarial verification.
  • Orchestrator + Specialists: One agent coordinates others that specialize in research, memory, execution, etc.
  • Pipeline: Agents work in sequence, refining results at each stage (planner → executor → reviewer).
  • Debate / Adversarial: Agents argue different perspectives; a synthesizer issues a final recommendation.
Example orchestration: if a user asks, “Research the best productivity tools, save my preferences, write a script to track my tasks, and email me a summary,” Zippy (the orchestrator) would:
  1. Ask Savvy to research the tools.
  2. Ask Meshy to save the user’s preferences.
  3. Ask Cody to write and run the script.
  4. Ask Savvy to summarize the findings and prepare the email.
A stylized diagram titled "Orchestrator + Specialists" showing a central "Orchestrator" bot (Zippy) coordinating three specialist bots: Savvy for research, Meshy for memory, and Cody for code. Colorful lines connect the orchestrator to each specialist with labeled buttons for their functions.
Savvy and Cody can work concurrently — Savvy researching while Cody writes the script — allowing parallelism that a single-agent loop would otherwise perform sequentially. This parallel approach reduces latency on independent subtasks. Pipeline pattern
  • Use when later steps depend on earlier results or verification is critical.
  • Typical roles:
    1. Planner — breaks the task into concrete steps.
    2. Executor — carries out steps using tools.
    3. Reviewer — verifies results and catches errors before final reply.
A dark-themed diagram titled "PIPELINE PATTERN" showing three numbered boxes: Planner (breaks down task into steps), Executor (executes steps using tools), and Reviewer (verifies results before reply). Arrows connect the boxes to show a sequential refinement workflow.
Debate / adversarial setup
  • Two or more agents take opposing positions to surface trade-offs (e.g., cost vs. convenience).
  • A synthesizer agent listens to both sides and issues a final recommendation that respects user preferences and constraints.
How agents pass information
  • Shared message history: all agents read/write the same conversation log (simple but riskier for consistency).
  • Hand-off messages: one agent summarizes and passes results to the next (cleaner boundaries).
  • Shared memory store: a centralized database or vector store that agents can read from and write to (scales well, supports persistence).
Each approach has trade-offs in complexity, latency, and consistency — choose based on the use case and failure modes you’re willing to accept. When should you go multi-agent? Multi-agent systems add development and operational complexity. Use them when they clearly solve problems a single agent struggles with:
An infographic titled "When to Go Multi-Agent" showing a green "Single" start bubble on the left and three colored decision boxes: "Tools Overload," "Separable Tasks," and "Mixed Models," each with a short explanation. It notes multi-agent setups add complexity and should be used only when necessary, with a small "Zippy" robot logo in the corner.
Start with a single agent and clear tools. Split into multiple agents when the workload, toolset, or modeling needs justify the extra complexity.
Decision checklist
  • Tools overload: A single agent managing many disparate tools becomes error-prone.
  • Separable tasks: Task cleanly decomposes into independent subtasks (e.g., research, memory, execution).
  • Mixed models or latency needs: Different subtasks benefit from different model families (fast vs. deep reasoning) or parallel execution.
Table: Patterns at a glance Operational trade-offs and best practices
  • Start with one well-designed agent; instrument it with good logging and a clear tool interface.
  • Add agents only when you consistently hit limits: performance, accuracy, or tool management.
  • Define clear communication protocols (message schemas, memory contracts).
  • Monitor for latency, consistency issues, and failure modes introduced by coordination.
  • Use vector stores or small databases for persistent memory and retrieval consistency.
Key takeaways
  • Multi-agent systems split work across specialized agents to improve reliability and parallelism.
  • Single-agent systems are still the right choice when tasks are well-scoped and fit within one agent’s capabilities.
  • Use multi-agent setups when tasks have separable subtasks, require specialization, or need different models.
  • Common patterns: orchestrator + specialists (parallel), pipeline (sequential refinement), and debate (trade-off analysis).
  • Communication options include shared message history, hand-off messages, and shared memory stores — each with trade-offs.
  • Adopt multi-agent architectures deliberately; they add complexity and operational overhead.
An infographic titled "Key Takeaways" comparing multi-agent and single-first approaches. It lists when to use multi-agent (separable tasks, specialization, mixed models) and example patterns (orchestrator, pipeline, debate) plus notes on communication.
Further reading and references

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