Multi-Agents

Overview

A multi-agent system is a team of specialized AI agents that work together on one goal: each agent has its own role, knowledge, and tools, and they hand work to each other instead of one agent doing everything. In Taskade Genesis you build that team in plain English, no code, and the agents share one workspace memory so the researcher's findings flow straight to the writer and on to the reviewer. Run them in sequence, in parallel, or with a supervisor that routes each task to the right specialist.

Would you like to team up with personalized AI assistants that think just like you? Multi-Agents are an evolution of Taskade's agentic workflow designed to work on multiple tasks concurrently in the background while you focus on other things.

Instead of one agent doing everything, you compose specialized agents that pass work to each other — a researcher who gathers sources, a writer who drafts, a reviewer who catches errors. Each agent has its own prompt, tools, and knowledge; together they behave like a real team.

TL;DR: A multi-agent system is several specialized AI agents that hand work to each other: a researcher gathers, a writer drafts, a reviewer checks. In Taskade Genesis you build the whole team by describing it, no code. The agents share one workspace memory, so each one's output flows to the next, and you run them in sequence, in parallel, or with a supervisor. Build an agent team free →

Answer first (40-second version): A multi-agent system divides a job among AI agents that each own one skill, then coordinate the handoffs toward a shared outcome. Frameworks like CrewAI, AutoGen, LangGraph, and OpenAI's Agents SDK do this with Python code. Taskade Genesis does it without code, inside one workspace, with persistent memory the whole team shares. That last part is the difference: most tools wire agents together, then make you wire up the data plumbing too. Taskade hands every agent the same living memory for free. Try it free →

This is the same pattern the whole industry calls a multi-agent system (sometimes "multi agent AI" or "agent orchestration"). The 2026 playbook names three topologies that keep coming up: a supervisor that dispatches workers, an orchestrator-worker loop (the most common in production), and a swarm of peer agents with no central boss. Taskade Genesis supports all three through one tab and one Taskade EVE prompt, which is covered in detail below.

The solid arrows are the forward pass. The dashed arrows are feedback loops — reviewers can bounce work back to earlier agents, which is how real teams work. This same pattern powers Taskade Genesis: multiple specialized agents collaborate internally to build an app from a single prompt.

How Taskade EVE Delegates to a Team

The fastest way to spin up a team is to ask Taskade EVE, the meta-agent that orchestrates Memory, Intelligence, and Execution. You describe the outcome in one sentence, and EVE creates the specialists, wires their handoffs, and hands the team back to you running. EVE is the supervisor in the diagram below: it reads the request, picks who acts, and routes the result.

The dashed arrow is the part competitors charge you to build. Every agent reads and writes the same workspace memory, so Taskade EVE never has to copy a result from one agent's output into another agent's input. The memory is the connective tissue. See Workspace DNA for how Memory, Intelligence, and Execution reinforce each other.

Round-Robin vs Hierarchical Orchestration

Two teams can hold the same agents and behave completely differently depending on how the work moves. A round-robin (peer / swarm) team passes the baton agent to agent with no boss. A hierarchical (supervisor) team puts one agent in charge of routing. Pick by the shape of the job.

Use round-robin when every stage adds value in a fixed order, like a content pipeline. Use hierarchical when the next step depends on context, like support tickets that could be billing, technical, or sales. Taskade Genesis runs both, plus a parallel mode, from the orchestration setting shown later in this guide.

A Hand-Off, Step by Step

A handoff is the moment one agent finishes and the next one picks up with full context. In code-first frameworks this is a function that returns another agent. In Taskade Genesis it is automatic: the shared memory carries the context, so the next agent already knows what happened. Here is one support ticket moving through a supervisor team.

No copy-paste between agents. The triage agent's finding ("two charges found") is already in memory when the billing agent starts, because they share one brain. That is the hand-off Taskade gives you for free.

Single Agent or a Team?

Use one agent for one job. Reach for a team when the work needs different specialists in sequence, or when one agent would have to be trained on too many unrelated things.

The advantage Taskade has over standalone agent tools: your agents share workspace memory, so the researcher's findings flow straight to the writer with no copy and paste. See Custom AI Agents to build the individual specialists first.

Taskade Genesis vs Code-First Multi-Agent Frameworks

The developer world has strong multi-agent frameworks: CrewAI, AutoGen, LangGraph, and OpenAI's Agents SDK. They are excellent if you write Python and want to assemble the plumbing yourself. Taskade Genesis aims at a different person: the operator who wants a working agent team today, in a workspace, without code. Here is an honest side-by-side.

A few honest notes so you choose well. CrewAI has the gentlest learning curve of the code frameworks and a clean role model, great when you already write Python. LangGraph gives the most control for complex branching and is the most production-mature of the four. AutoGen is strong for conversational, human-supervised research, though Microsoft has folded its future into a broader agent framework. OpenAI's Agents SDK is the supported successor to the Swarm reference project, built around handoffs and guardrails on the OpenAI stack. If your team writes code and wants maximum control, those are real options.

The reason to pick Taskade Genesis is the row that the others all answer with "you configure it": shared memory. In a code framework you connect the agents, then separately stand up the database, the retrieval, and the state passing. In Taskade, the workspace memory already exists, every agent already reads it, and the whole team is one prompt away. That is the "multi-agent without code, in one workspace, with memory" promise made concrete. For a ranked tour of the category, see Best Multi-Agent Platforms.

Multi-Agent System vs a Single Powerful Model

People sometimes ask why not just use one very capable model. The 2026 answer from production teams: a multi-agent system wins when the work genuinely splits into specialties, runs in parallel, or benefits from one agent critiquing another. It costs more tokens, so reach for it when the job earns it.

Important: We have introduced a completely new way to chat and interact with multiple agents. Check our guide to AI Teams to learn more.

See a Multi-Agent Team Running

The fastest way to understand a multi-agent system is to clone one and watch the agents coordinate. These are real, live Taskade Genesis apps from the Community Gallery. Click either screenshot to open the app, then clone it in about a minute and look at how the team is wired.

The Sales Agent Studio runs a lead-qualifier, a follow-up writer, and a CRM-updater as one coordinated team. The Support Agent triages incoming messages, drafts answers from your docs, and routes anything it cannot resolve. This is the multi-agent pattern no open-source framework ships ready to run, because each agent already shares the same workspace memory. Clone either, open the Agents tab, and you can see exactly how the handoffs are set up.

This is the difference one of our first Enterprise customers described after building a production Service Pro Dashboard on Taskade Genesis: "what I did in weeks would have taken 40 people 18 months." A multi-agent team is how a single operator runs work that used to need a department.

Watch Agents Use Tools and Hand Off Work

Each agent in a team brings its own tools to the job. The agent-workflows view below shows agents picking up tasks, calling tools, and passing results along, all inside one workspace.

Every Taskade agent ships with 34 built-in tools plus the full 100+ bidirectional integration catalog: triggers pull events in, actions push data out. One agent can search the web while another updates your CRM and a third posts to Slack, all in the same run. See Tools for AI Agents for the full catalog.

Set Up Your AI Agents

You can build and deploy a team of multi-agents using your existing Custom AI Agents, or you can create new agents for this purpose.

🤔 When to create AI agents: If a task comes up regularly or takes over 15 minutes of your focus, or if you're frequently troubleshooting the same problem, setting up an AI agent can be a game changer.

• Create a new agent:

1. Go to the Agents tab at the top of your workspace/folder.

• Click ➕ Create agent to open the agent creator.

Method #1: Start from scratch

• In the agent creator, click ➕ Start from scratch.

• Create a unique name for your custom agent.
• Select an avatar or upload your own.

Method #2: Use one of the available agent templates.

• Pick an agent template from the list at the bottom.
• Create a unique name for your custom agent.
• Select an avatar or upload your own.
• Click Create to save the new agent.

💡 Note: Visit our Custom AI Agents guide for detailed instructions.

Team Up with AI Agents

There are two ways you can use collaborate with Multi-Agents inside projects:

• Assign a single agent to several tasks:
• Select the first task you want your agent to work on.
• Type / + custom command associated with your agent.

1. You don't need to type the entire command.
2. Press ⌨️ Enter to execute the command.
3. Click anywhere in the project to minimize the agent window.
4. Repeat steps 1-3 to assign the agent to additional tasks.

💡 Note: Agent names are visible under the tasks they are working on, with a spinning circle indicating tasks in progress. Click the names to show a conversation history of each of the agents assigned to a task.

• Assign multiple agents concurrently:

1. Select the first task you want an agent to work on.

• Type / + custom command associated with an agent.
• Press ⌨️ Enter to execute the command.
• Click anywhere in the project to minimize the agent window.
• Use a different command on a task to deploy another agent.
• Repeat the process to deploy additional agents.

💡 Note: You can deploy multiple agents to work on the same task.

Iterate on the Output

Every agent comes equipped with both short and long-term memory, which allows you to browse its conversation history and iterate on the output.

• Reprompt the agent:

1. Click the agent name under a task.

• Type a follow-up prompt in the chat interface.
• Browse previous interactions using the arrows in the top-right corner.

💡 Note: Not sure where to start? Visit our guide to Writing Agent Prompts.

Chat with Agents & Your Team

Shared agent chats let your team interact with custom AI agents together. Collaborate on problem-solving, generate ideas, and ensure everyone benefits from AI insights.

1. Open the chat box (see the previous sections).
2. Choose an agent from the drop-down at the top.
3. (optional) Click the ⁺ 👤 button to invite people to the conversation.
4. Type a prompt to start a conversation.

Note: When you start a shared AI chat, a tooltip at the bottom of the chat box will let you know if the chat is public or private, so you can see who has access.

Copy / Duplicate Multi-Agents

• Create a custom agent following the steps in the previous sections.
• Navigate to the 🤖 Agents tab and choose your custom AI agent.
• Click the three dots ··· next to an agent.
• Click Copy and select the target workspace/folder.

• Choose the target workspace/folder.
• Decide if you want the agent to retain its knowledge.

Share Agents

1. Navigate to the 🤖 Agents tab and choose your AI agent.
2. Click the ✎ Edit agent button in the top-right corner.
3. Click Share button in the top-right corner.
4. Enable Public access in the top-right.
5. Copy the agent link using the ⧉ button.
6. Share the agent link with others.

Workspace DNA

Memory

Intelligence

Execution

🌎

Browse templates, agents, and apps built by the Taskade community at taskade.com/community.

MCP Connectors — Your agent teams can access external tools and data sources through MCP v2 (Model Context Protocol) connectors. This means each agent in a team can specialize with different tool sets — one agent searches the web while another manages your CRM.

Multi-Agent Team Templates

Explore multi-agent orchestration apps from the community:

• Team Capacity Planner — Multi-agent resource allocation
• Client Portal — Agent-powered customer management
• Event Management Portal — Multi-agent event coordination

Build your own → taskade.com/create

Orchestration Patterns

Multi-agent teams support three orchestration modes:

Taskade EVE can create and manage multi-agent teams for you. Just describe what you need — "Create a content team with a writer, editor, and SEO specialist" — and Taskade EVE sets it up automatically.

Sequential Mode

Agents work one after another in a defined order. Output from Agent A becomes input for Agent B. Best for: content pipelines (research → write → edit → publish).

Parallel Mode

Multiple agents work simultaneously on different parts of a task. Results are combined at the end. Best for: data analysis from multiple sources, multi-language translations.

Supervisor-Delegated Mode

A supervisor agent routes tasks to specialist agents based on context. The supervisor decides which agent handles each sub-task. Best for: customer support (route to billing, technical, or sales agents).

Build a Multi-Agent Team in Taskade, Step by Step

You can stand up a working team in about ten minutes, no code. Follow these five parts, then use the Verify table at the end to confirm the team is wired correctly.

Here is the same team as an at-a-glance map. Read it top to bottom: the goal flows into a supervisor, the supervisor routes to specialists, and every specialist reads and writes the same shared memory.

              ┌─────────────────────────────────────┐
   GOAL  ───▶ │  Taskade EVE  (supervisor / router) │
              └───────┬───────────┬───────────┬──────┘
                      ▼           ▼           ▼
                ┌──────────┐ ┌──────────┐ ┌──────────┐
                │ Research │ │  Writer  │ │ Reviewer │   ◀── each: own role,
                │  Agent   │ │  Agent   │ │  Agent   │       knowledge, tools
                └────┬─────┘ └────┬─────┘ └────┬─────┘
                     │            │            │
                     ▼            ▼            ▼
              ╔══════════════════════════════════════╗
              ║   SHARED WORKSPACE MEMORY            ║  ◀── ▲ Memory
              ║   projects · knowledge · live state  ║      every agent
              ╚══════════════════════════════════════╝      reads + writes

Ask Taskade EVE to skip the manual setup entirely: "Create a content team with a researcher, a writer, an editor, and an SEO specialist, run them in sequence." EVE creates all four, wires the handoffs, and hands the team back ready to run.

Verify Your Team Is Wired Right

Run the team on three real examples, then read the result against this table. Each row tells you exactly which dial to turn.

When all three examples pass, the team is ready. The last row is the payoff: a proven team becomes a Taskade Genesis app with its own link, custom domain, and one-click clone.

Credit Considerations

Each agent in a multi-agent team consumes credits independently. A 3-agent sequential pipeline uses ~3x the credits of a single agent. Use efficient Standard-tier models for intermediate processing steps and reserve Thinking or Reasoning-tier models for final output.

Error Handling

If an agent in a sequence fails, the pipeline pauses and surfaces the error. You can configure retry logic via automations or set fallback agents for critical workflows.

The Bigger Picture: One Workspace, Living Software

A multi-agent team is one part of a larger idea. In Taskade Genesis you do not build software, you describe it, and the workspace runs it. Your agents are the team, your projects are the memory, and your automations are the execution. Put together, they become living, cloneable software that an operator runs instead of a department.

This loop is Workspace DNA: Memory feeds Intelligence, Intelligence triggers Execution, and Execution writes new Memory. A multi-agent team sits right in the middle, drawing on all three. The agents read live data, run frontier models (15+ from OpenAI, Anthropic, Google, plus open-weight providers), and trigger automations that update your projects, which makes the next decision smarter.

The vision is simple. AI Apps mean you describe an app and get a running one you can publish, give a custom domain, and let anyone clone. AI Agents v2 are the intelligence inside that app, a team rather than a single bot. Automation runs the work on a schedule or a trigger without you watching. And the Community Gallery plus buy-once-clone App Kits mean a team built once can be cloned by thousands. Software you describe instead of build, run by every operator as living, cloneable apps. Start free at taskade.com/create.

Common Questions

What is a multi-agent system in simple terms?

It is a team of AI agents that work together, each with its own job. One researches, one writes, one reviews. They pass work between each other and share the same workspace memory, so the team produces a better result than a single agent doing everything.

What is multi-agent AI and how is it different from one agent?

Multi-agent AI splits a job across several specialized agents that coordinate handoffs, instead of one agent trying to do everything. You reach for it when the work has distinct stages, runs faster in parallel, or improves when one agent checks another. A single agent is better for quick, linear, single-skill tasks. The 2026 industry consensus is the same: a team wins when the work genuinely benefits from specialization or critique.

How do AI agents collaborate in Taskade?

Agents share your workspace memory, so one agent's output becomes the next agent's input automatically. You can run a team in sequence, in parallel, or with a supervisor agent that routes each task to the right specialist. Set this up in the AI Teams tab.

Do I need to code to build an AI agent team?

No. You build each agent by describing its role in plain English, then add them to a team in the AI Teams tab. You can also ask Taskade EVE to create the whole team for you: "Create a content team with a writer, an editor, and an SEO specialist." Code-first frameworks like CrewAI, AutoGen, and LangGraph require Python; Taskade Genesis does not.

How is Taskade different from CrewAI, AutoGen, or LangGraph?

CrewAI, AutoGen, and LangGraph are Python frameworks for developers who want to assemble multi-agent systems in code. Taskade Genesis builds the same kind of team without code, inside one workspace, with shared memory that every agent reads for free. In a code framework you wire the agents and then separately stand up the database and state passing. In Taskade the workspace memory already exists. See the comparison table above and Best Multi-Agent Platforms.

What are the main multi-agent orchestration patterns?

Three patterns dominate in 2026. Sequential (round-robin) passes work agent to agent in a fixed order, best for pipelines. Parallel runs agents at the same time and combines results, best for multi-source analysis. Supervisor (hierarchical) puts one agent in charge of routing each task, best for support and triage. Taskade Genesis runs all three from one orchestration setting.

How many agents can work together?

You can add multiple specialists to a team and even assign several agents to the same task. Each agent consumes credits independently, so a three-agent pipeline uses roughly three times the credits of one agent. Use efficient models for intermediate steps. See AI Usage & Credits.

Do the agents share memory automatically?

Yes. Every agent in a Taskade team reads and writes the same workspace memory: projects, knowledge, and live state. The researcher's findings are already in memory when the writer starts, so there is no copy-paste between agents. This is the connective tissue that code frameworks make you build yourself. See Agent Knowledge & Memory.

When should I use a team instead of one agent?

Use a team when the work has distinct stages that need different knowledge or tools, or when a second agent checking the first improves quality. Use one agent when a single skill and knowledge set covers the job. See the decision table above.

Can I publish or share a multi-agent team?

Yes. Once a team works reliably, publish it as a Taskade Genesis app with its own link, optional custom domain, and one-click clone. Browse live examples like the Sales Agent Studio and Support Agent in the Community Gallery, then clone one to see how the team is wired.

Is building a multi-agent team free?

Yes. Multi-agent teams are included on every Taskade plan, including Free, which starts with one-time AI credits. AI usage is metered in credits, not seats. Paid plans (Starter $6, Pro $16, Business $40, Max $200, Enterprise $400, annual billing) add more credits and capacity. See AI Usage & Credits.

Related guides

• AI Teams — The new way to chat with and orchestrate multiple agents
• Custom AI Agents — Build the individual specialists first
• Build an AI Agent: The Complete Playbook — Create, train, tool, and deploy one agent first
• Taskade EVE — Ask the meta-agent to create a whole team for you
• How Taskade Genesis Works: Workspace DNA — The architecture behind multi-agent apps
• Projects & Databases: The Memory Pillar — Data that agents share and learn from
• Automations: The Execution Pillar — Trigger agent teams from automation workflows
• Agent Knowledge & Memory — Train agents with shared knowledge
• Tools for AI Agents — The full 34-tool catalog each agent can use
• MCP Connectors — Connect agents to external tools

Related reading

• Best Multi-Agent Platforms — A ranked tour of the multi-agent category
• What Are AI Agents? — The plain-English explainer behind agent teams
• Multi-Agent Systems (Wiki) — The concept, defined
• AI Agents · AI Apps · Automations — The three pillars, on one page each

Try It: Build Your First Team Free

You do not need code, a deployment pipeline, or a team of engineers. Describe the outcome, let Taskade EVE assemble the specialists, and watch them coordinate inside one workspace with shared memory. That is the whole promise: multi-agent without code, in one workspace, with memory.

Start free at taskade.com/create →

▲ ■ ●  Memory feeds Intelligence, Intelligence triggers Execution, Execution writes new Memory. That is how one operator runs the work of a team.