Agents vs. teams vs. workflows: How to choose the right architecture
Building intelligent automation—AI systems that actually solve problems—means choosing the right architecture. If you're working with Agno, you'll encounter three core abstractions: Agents, Teams, and Workflows. Each solves a different problem, and picking the wrong one can mean the difference between a clean, maintainable system and an expensive mess.
Here's how to think about them.
Agents: The building block
An agent is an AI program that reasons, uses tools, and maintains state.
Under the hood, agents are stateful control loops built around stateless models. The model reasons, calls tools, evaluates results, and iterates based on your instructions. Around that loop, you can add capabilities like memory, knowledge, storage, human-in-the-loop approvals, and guardrails as needed.
Think of an agent as a single, focused worker. You give it a role ("You're a research analyst"), hand it tools (a web scraper, a database connector), and set it loose. It maintains state across reasoning steps, which means it can handle multi-step problems that require context from earlier in the process.
Agents get more powerful as you layer on capabilities: memory for learning from past interactions, knowledge bases for domain-specific references, storage for persistence, guardrails to keep behavior on track, and human-in-the-loop checkpoints for high-stakes decisions.
When to use an agent: the task fits within one domain, you want to minimize token costs, or you're still prototyping. Start with one agent and add complexity only when you hit its limits.
To build effective agents, start simple:
from agno.agent import Agent
from agno.models.anthropic import Claude
from agno.tools.hackernews import HackerNewsTools
agent = Agent(
model=Claude(id="claude-sonnet-4-5"),
tools=[HackerNewsTools()],
instructions="Write a report on the topic. Output only the report.",
markdown=True,
)
agent.print_response("Trending startups and products.", stream=True)
See more in our building agent docs.
Teams: Collaboration at scale
A Team is a group of agents (or sub-teams) that collaborate under a leader to solve complex tasks. The leader coordinates by decomposing tasks and delegating to the right member based on their roles and capabilities.
For example:
If the task is “Create a market research report”, the leader might decompose it into:
Gather competitor data → Research agent
Analyze trends → Analyst agent
Write report draft → Writer agent
Review and polish → Editor agent
Why not just build one big agent with all the tools? Because single agents hit limits fast. Context windows fill up, decision-making gets muddy with too many responsibilities, and debugging becomes a nightmare when you can't isolate what went wrong.
Teams address these limitations directly. By distributing responsibility across specialized agents, you gain structure, clarity, and scale:\
- Specialization: each agent masters one domain instead of being mediocre at everything
- Parallel processing: multiple agents work simultaneously on independent subtasks
- Maintainability: when something breaks, you know exactly which agent to fix
- Scalability: add capabilities by adding agents, not rewriting everything
The tradeoff is coordination overhead. Agents need to communicate and share state. If you get this wrong, you've just built a more expensive failure mode.
That’s why the structure of collaboration matters. Not all teams coordinate the same way, and the pattern you choose determines how tasks flow, how outputs are combined, and where control lives.
Agno provides three team patterns:
Supervisor (default): The leader decomposes tasks, delegates, reviews output, and synthesizes a final response. Best for quality control and unified answers.
Router: The leader routes requests directly to the most appropriate specialist, who responds without synthesis. A smart dispatcher for clearly distinct domains.
Broadcast: The leader sends the task to all members simultaneously. Ideal for parallel research or gathering multiple perspectives.
When to use a team: the task demands multiple specialized agents with different tools, a single agent's context window isn't enough, or you need each agent focused on a narrow scope.
Here’s a minimal example for building teams:
from agno.team import Team
from agno.agent import Agent
from agno.models.openai import OpenAIResponses
from agno.tools.hackernews import HackerNewsTools
from agno.tools.yfinance import YFinanceTools
news_agent = Agent(
name="News Agent",
role="Get trending tech news from HackerNews",
tools=[HackerNewsTools()]
)
finance_agent = Agent(
name="Finance Agent",
role="Get stock prices and financial data",
tools=[YFinanceTools()]
)
team = Team(
name="Research Team",
members=[news_agent, finance_agent],
model=OpenAIResponses(id="gpt-4o"),
instructions="Delegate to the appropriate agent based on the request."
)
team.print_response("What are the trending AI stories and how is NVDA stock doing?", stream=True)
See more in our building teams docs.
Workflows: Predictable pipelines
A Workflow orchestrates agents, teams, and plain functions as a sequence of defined steps. Output from each step flows to the next, creating a repeatable, auditable pipeline.
If teams are about flexible collaboration, workflows are about predictable execution. Steps can run sequentially, in parallel, in loops, or conditionally, but the overall structure is defined upfront.
The key difference between teams and workflows is when decisions are made. A team figures out how to solve a problem at runtime, with a leader dynamically deciding which agent does what. A workflow has the execution plan baked in at design time, where the order of steps, conditions, and branching logic are predetermined.
Steps can be an Agent (an individual AI executor), a Team (for complex sub-tasks needing collaboration), or a Python function (for deterministic logic like data transformation or validation).
Workflows also support conditional branching, parallel execution, loops for iterative refinement, early stopping, conversational interactions mid-workflow, and background execution for long-running processes.
When to use a workflow: you need predictable, repeatable execution; tasks have clear sequential steps with defined inputs and outputs; or you need audit trails and consistent results across runs.
Here’s a simple workflow that takes a topic, researches it, and writes an article:
from agno.agent import Agent
from agno.workflow import Workflow
from agno.tools.hackernews import HackerNewsTools
researcher = Agent(
name="Researcher",
instructions="Find relevant information about the topic",
tools=[HackerNewsTools()]
)
writer = Agent(
name="Writer",
instructions="Write a clear, engaging article based on the research"
)
content_workflow = Workflow(
name="Content Creation",
steps=[researcher, writer]
)
content_workflow.print_response("Write an article about AI trends", stream=True)
See more in our building workflows docs.
Agents vs. teams vs. workflows: The decision framework
Workflows, teams, and agents are not mutually exclusive. A workflow can include teams as steps, and teams are built from agents. The key is choosing the right level of abstraction for each part of your system.
The golden rule: Start with a single agent. If it reaches its limits, such as context overflow, too many tools, or competing responsibilities, evolve the architecture. Use a team when tasks require collaboration across specialized agents. Use a workflow when you need a predefined, structured execution pipeline.
Agents vs. teams vs. workflows: Quick comparison
Workflows, teams, and agents are not mutually exclusive. A workflow can include teams as steps, and teams are built from agents. The key is choosing the right level of abstraction for each part of your system.
The golden rule: Start with a single agent. If it reaches its limits, such as context overflow, too many tools, or competing responsibilities, evo
Wrapping up: What are the differences between agents, teams, and workflows?
Agents, teams, and workflows are not competing paradigms. They are complementary layers in an AI system architecture. The difference between agents, teams, and workflows lies in their role and level of coordination.
- Agents are the individual workers. They reason, use tools, and complete focused tasks.
- Teams enable multiple agents to collaborate, combining specialized skills to solve more complex problems.
- Workflows provide structure and repeatability, defining how tasks execute from start to finish.
The best AI systems use all three where appropriate. An agent handles a specific task. A team manages nuanced, multi-domain problem solving. A workflow connects everything into a reliable, production-ready process.
If you are deciding between agents, teams, and workflows, start simple. Use a single agent first. Introduce a team when collaboration is required. Implement a workflow when you need structured execution and predictable outcomes.
Let the requirements of your task guide your architecture decisions.