AI Agent Patterns: The 5 You Need to Know (2026)
Davis Christenhuis
AI agent patterns are the architectural blueprints behind how an agent reasons, plans, and acts. The model gets most of the attention, but the pattern determines whether an agent handles a five-step workflow or falls apart after the second tool call.
This guide covers the five most common ones, what each does in practice, and how to pick the right one for your use case.
📌 TL;DR
Short on time? Here are the key takeaways from AI agent patterns:
- ReAct: An iterative loop where the agent reasons, acts, and observes before deciding the next step. Best for dynamic, open-ended tasks.
- Planning: The agent builds a complete plan before executing anything. Best for structured, predictable workflows.
- Reflection: The agent critiques and revises its own output before returning a final answer. Best for quality-sensitive work.
- Multi-Agent: Specialized agents divide a complex task between them, coordinated by an orchestrator. Best for cross-functional workflows.
- Tool Use: The enabling layer that connects agents to live data, APIs, and external systems. Most production use cases depend on it.
- Dust: A multiplayer AI workspace that handles the architecture layer so your team can deploy agents without building from scratch.
What are AI agent patterns?
AI agent patterns are architectural templates that define how an AI agent reasons, retrieves information, and decides what action to take at each step of a task. They are not specific to any tool, framework, or model. Instead, they describe reusable approaches to common challenges in agent design: how an agent should handle uncertainty, when it should call an external tool, how it should improve output quality, and how multiple agents should coordinate.
Think of them as the underlying playbook. When a support agent searches your knowledge base, determines it needs more context, pulls from a CRM record, and then composes a response, it is following a pattern. The pattern shapes what the agent can handle, how it performs at scale, and what it costs to run.
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The 5 core AI agent patterns
These five patterns are among the most widely discussed building blocks behind real-world AI agent deployments. Other frameworks identify additional patterns, including routing, prompt chaining, and parallelization, but the five below form a practical starting point for most teams.
1. ReAct (Reason + Act)
ReAct interleaves reasoning and action in a step-by-step cycle, drawing its name from the fusion of those two capabilities: at each step, the agent decides what to do next, acts on that decision, and uses the outcome to inform the following step, continuing until the task is complete.
Rather than planning upfront, the agent decides what to do at each step based on what it just observed. A customer support agent using ReAct might search a knowledge base, find a partial answer, pull additional context from a CRM record, and only then compose a response. Each observation shapes the next action.
This structure gives the agent flexibility, and it makes reasoning transparent, which helps with auditing and debugging. The trade-off is that it generates more model calls per task than simpler approaches.
- Best for: Dynamic, open-ended tasks such as answering support queries across multiple knowledge sources, researching accounts before a sales call, or handling requests where the full scope is not known upfront.
2. Planning
The Planning pattern front-loads the thinking: the agent maps out a full sequence of steps before it starts executing any of them. In its most common form, the agent creates a complete plan upfront and then carries out each step in sequence. More advanced variants use iterative planning, where the agent can revise its plan between steps based on intermediate results.
A research agent using this pattern might map out the full workflow before acting on any of it: search internal documents, pull competitor data, summarize findings, draft a report. Only after that plan is set does it begin executing. Because each execution step in a planning agent can use a simpler, cheaper model call than the full reasoning loop required by ReAct, the total cost per task is often lower even though the number of API calls may be comparable or higher.
The trade-off is rigidity. If something unexpected happens mid-execution, a planning agent has limited ability to adapt. It works best when the steps are predictable and conditions are unlikely to change along the way.
- Best for: Structured, predictable tasks such as generating reports, running compliance checks, producing content through a defined pipeline, or automating recurring internal workflows.
3. Reflection
The Reflection pattern gives an agent the ability to review what it produced, assess where it falls short, and rewrite before returning anything to the user.
A reflection loop involves at least two passes: one to generate an initial output, and one to critique it against a set of defined criteria. The agent checks its own work before returning a result, which adds at least one additional model call per cycle and increases cost. For tasks where accuracy, tone, or compliance requirements are high, that cost is usually worth it.
- Best for: Content drafting and revision, code review, legal or compliance document generation, and any task where a rough first pass is not an acceptable deliverable.
4. Multi-Agent
The Multi-Agent pattern distributes work across several specialized agents that each handle a distinct part of a larger task, coordinated by an orchestrator or a defined handoff sequence.
Instead of one agent doing everything, a multi-agent system assigns sub-tasks to agents built for specific functions. An orchestrator agent receives a complex request, breaks it into parts, and routes each part to a specialist: one agent for data retrieval, one for analysis, one for formatting the final output. Those agents can run in sequence, each handing off to the next, or in parallel depending on whether the steps depend on each other.
The trade-off is coordination overhead. More agents mean more model calls, more state to manage, and more points where something can go wrong. The pattern earns its complexity on tasks that a single agent cannot handle reliably, not as a default architecture choice.
- Best for: Cross-functional workflows, large-scale research and synthesis tasks, customer journey automation, and processes that currently require several people with different areas of expertise.
5. Tool Use
The Tool Use pattern connects an AI agent to external systems, APIs, databases, and applications so it can retrieve real-time information and take real-world actions.
Without tools, an agent is limited to what the model already knows. With them, it can query a CRM, read a Slack thread, check live inventory data, submit a form, or run a calculation. The Model Context Protocol (MCP) is an open, widely adopted standard that makes it easier to define and expose tools consistently across platforms.
Tool Use is both a standalone pattern and the enabling layer that makes the other patterns work in production. While it is categorized as its own design pattern in every major framework, it also underpins how ReAct, Planning, and Multi-Agent systems interact with the real world. The quality of an agent's tool definitions often shapes its performance as much as the underlying model does.
- Best for: Almost every production use case. Any agent that needs to interact with live data, take actions in external systems, or work across your company's existing tools requires this pattern.
Which pattern do you actually need?
The right pattern depends on the task. Here is how common use cases map to each one:
If you need to... | Pattern to use |
Answer questions from documents or across multiple tools | ReAct |
Generate a structured report or run a repeatable workflow | Planning |
Draft, review, and refine content until it meets a quality bar | Reflection |
Handle complex, cross-functional tasks at scale | Multi-Agent |
Connect agents to live systems like a CRM, Slack, or Notion | Tool Use |
Build a full support, sales, or operations workflow end-to-end | Multi-Agent + Tool Use |
Most production systems combine patterns. Start simple, and add complexity only where a single-pattern approach falls short.
How Dust makes multiplayer AI for your team
Dust is a multiplayer AI workspace where people and agents work as co-contributors from shared context, rather than in separate individual tools.
Most teams run AI in isolation: one person, one session, no shared memory. Dust changes that by giving agents and teammates access to the same knowledge base and tools, so that what one person builds benefits the whole team.
Some key features are:
- No-code agent builder: Build and configure agents through a visual interface, no coding required. Teams can set instructions, connect data sources, and deploy agents easily.
- Model agnostic: Switch between models from OpenAI, Anthropic, Google, and Mistral without rebuilding your agent setup.
- Enterprise security: GDPR compliant and SOC 2 Type II certified. Enables HIPAA compliance. Fully control which data Dust ingests from each source.
- 50+ integrations: Connect agents to your existing stack through native connectors and MCP support, including Slack, Notion, Google Drive, Salesforce, and GitHub.
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Real example: how Assembled built an AI operating system with Dust
Assembled is a customer support platform founded in 2018 by former Stripe engineers. By early 2024, the company had grown to nearly 120 employees and expanded rapidly into a multi-product AI platform, which created a serious internal knowledge problem. Information lived across Notion, Google Drive, Slack, Gong, and Salesforce, with no unified way to find any of it. Teams were using different AI tools individually, but none of that was shared or coordinated across the company.
Assembled deployed Dust across the entire organization. The first unlock was search: employees could query across all their tools from a single place. From there, each team built its own specialized agents:
- Marketing built competitive intelligence and content automation agents
- Sales built account research and follow-up agents
- Customer Success built Zendesk and product feedback agents
- Engineering added code-base search and SQL query agents
- Product automated changelog and release-mapping tasks
The result: 95% internal adoption across 120+ employees without extensive training, and hundreds of hours saved on information retrieval.
"Just in terms of searching and finding company information alone, Dust has saved us hundreds of hours."
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Frequently asked questions (FAQs)
What is the difference between the ReAct pattern and the Planning pattern?
ReAct agents reason and act one step at a time, adjusting their approach based on what they observe after each action. Planning agents create a complete plan first, then execute each step in sequence. The practical difference comes down to flexibility versus efficiency. ReAct handles unexpected situations better because it adapts on the fly, deciding its next step based on each observation. Planning is more predictable and cost-efficient for structured, repeatable workflows because executor calls can be simpler and cheaper, but it performs poorly when conditions change during execution.
Can an AI agent use more than one pattern at the same time?
Yes, and most production AI systems do. Real deployments layer patterns together to handle different parts of a workflow. A customer support system might use ReAct for open-ended question answering, Reflection to review draft responses before sending, Tool Use to query a knowledge base or CRM, and Multi-Agent to route complex issues to specialized handlers. Choosing a single pattern is a useful starting point, but combining patterns as complexity grows is the standard approach in practice.
What is the Multi-Agent pattern and when does it make sense?
The Multi-Agent pattern distributes work across several specialized agents that each handle a distinct sub-task. It makes sense when a task is too complex for one agent to handle, when different parts of the workflow need different types of expertise, or when parallel execution would meaningfully reduce total completion time. A content pipeline might use one agent to research a topic, a second to draft the output, and a third to review it for accuracy. Multi-agent systems add coordination overhead, so they are worth the added complexity only when a single agent cannot handle the task on its own.