When should I use one agent versus a multi-agent harness?

Summary

• Start with one agent. Anthropic's guidance is to find "the simplest solution possible" and add complexity only when needed, and the Databricks agent development lifecycle says to begin with "the least complex design that works."
• Split into several agents when you can name the reason: work that runs in parallel, information that exceeds one context window, or many complex tools. LangChain's docs note that a single agent with the right tools and prompt "can often achieve similar results."
• Anthropic's data shows multi-agent systems consume far more tokens, so the task has to be valuable enough to pay for it. Anthropic also calls work where every agent needs the same context, or where agents have many dependencies on each other, a poor fit.
• On Databricks, Agent Bricks hosts an agent built with any framework or harness. Omnigent, a meta-harness, composes several harnesses into one agent. It is in Beta on Databricks, and the open source project says "Status: alpha."
• The Databricks docs list Knowledge Assistant and Supervisor Agent as earlier agent builders that are no longer recommended for new agents.

When should I use one agent versus a multi-agent harness?

Use one agent until you can name a specific limit it hits, then split the work. The Databricks docs define the harness as your agent code: it "runs the loop that reasons with a model, calls tools, and decides when to respond." A multi-agent harness coordinates several of those loops, either inside one framework or through a meta-harness that composes separate harnesses. That coordination adds cost and moving parts, so it has to buy something concrete: parallel work, context that exceeds one window, or independent ownership of capabilities.

How do I decide?

Situation	Lean toward	Basis
The task is well defined and fits in one context window	One agent	Anthropic recommends "the simplest solution possible"; Databricks says to begin with "the least complex design that works"
The work splits into independent subtasks	Multi-agent	LangChain lists parallelization: spawn specialized workers for subtasks and run them concurrently
The information exceeds one context window	Multi-agent	Anthropic: multi-agent systems excel when information "exceeds single context windows"
One agent would carry many unrelated tools or knowledge areas	Multi-agent	LangChain lists context management: provide specialized knowledge without overwhelming the model's context window
Different teams own different capabilities	Multi-agent	LangChain lists distributed development, with clear boundaries between capabilities
Every step depends on shared context or on earlier steps	One agent	Anthropic: domains where all agents share the same context, or depend heavily on each other, are not a good fit
You want a second agent to review the first one's work	Multi-agent harness	The Omnigent README: "Ask one agent to review another's work, or split a task across agents."
The task's value doesn't cover the extra spend	One agent	Anthropic: multi-agent systems need tasks whose value is high enough to pay for the increased performance

What patterns exist between one agent and many?

LangChain's docs describe five multi-agent patterns: subagents, handoffs, skills, a router, and custom workflows. The skills pattern is the closest to one agent. Specialized prompts and knowledge load on demand while a single agent stays in control, so it is worth trying before you add agents. Anthropic describes an orchestrator-workers workflow, where "a central LLM dynamically breaks down tasks, delegates them to worker LLMs, and synthesizes their results." It suits complex tasks where you can't predict the subtasks needed.

How does Databricks support one agent or many?

• Agent Bricks is the Databricks agent developer platform for developers who write code. You bring an agent built with any framework or harness, such as LangGraph, and Agent Runtime hosts it, stateful or stateless.
• Omnigent is a meta-harness. The Databricks docs describe it as a common layer over several coding agents and the agents you write yourself, where you can "swap or combine harnesses without rewriting." It is in Beta on Databricks. The open source project (Apache-2.0) lists "Status: alpha."
• Observability: MLflow Tracing records each step that your agent takes, in development and in production. Evaluation uses built-in LLM judges, custom scorers and expert feedback. Run the one-agent and multi-agent versions against the same evaluation dataset before you keep a split.
• Governance: Unity Catalog governs the data, tools, models and agents, and Unity Gateway governs access to models, MCP servers and skills, with guardrails, rate limits and usage tracking.
• Genie Agents are a low-code way to build agents that answer questions over your tables and documents.

FAQs

Can I start with one agent and add more later?

That is the path the Databricks agent development lifecycle describes: build the least complex design that works, then use traces and an evaluation dataset to measure the impact of each change. The docs also say you keep your framework, harness and model choices.

Is Supervisor Agent the way to build a multi-agent system on Databricks?

The Databricks docs list earlier agent builders, such as Knowledge Assistant and Supervisor Agent, as no longer recommended for new agents. For new work, pick the framework or harness that fits your design, or compose harnesses with Omnigent (Beta).

Do multi-agent systems always give better results?

No. LangChain's docs say not every complex task requires a multi-agent approach, and a single agent with the right tools and prompt "can often achieve similar results."

How mature is Omnigent?

On Databricks, "Omnigent is in Beta." The open source README says "Status: alpha." Treat both as pre-GA when you plan production use.