AI Agent Workflow Patterns (2026): ReAct to Multi-Agent

AI agent workflow patterns explained: ReAct, plan-and-execute, reflection and multi-agent - what each does, when it fits, and how they fail in 2026.

Abstract neural-network visualisation representing AI agent workflows
Updated
Rob
By Rob18 June 2026 · 8 min read
Abstract neural-network visualisation representing AI agent workflows

An AI agent (a language model given tools and a goal, looping until the goal is met) is only as good as the pattern that controls its loop. The same model can be reliable or chaotic depending on how you structure the way it thinks and acts. Those structures have names, and knowing them is the difference between an agent that quietly gets the job done and one that burns tokens going in circles.

This is a plain-English tour of the workflow patterns that matter in 2026, when each one fits, and how they fail. If you are building with Claude Code, the OpenAI Agents SDK or LangGraph, these are the shapes you are really choosing between.

What are the main agent workflow patterns?

Four shapes cover almost everything

PATTERN 1

ReAct (Reason + Act) Most common

The default workhorse loop

  • Tool-heavy tasks
  • Open-ended problems
  • Debuggable agents
  • How it works Think, act, observe, repeat
  • Best for Dynamic, tool-driven tasks
  • Cost / latency Higher (a model call per step)
  • Watch out for Looping without making progress
ReAct is the loop most agents run: the model reasons about what to do next, takes one action (usually a tool call), observes the result, and reasons again. It keeps going until the task is done. The big win is adaptability and transparency - you can read the reasoning trace and see exactly why the agent did each thing. It is what Claude Code and most coding agents use under the hood.

What we liked

  • Adapts to whatever it finds mid-task
  • Reasoning trace makes it debuggable
  • Great for tool-heavy, open-ended work

Watch out for

  • Can loop forever without a step limit
  • A model call per step adds up in cost and latency

PATTERN 2

Plan-and-Execute Lowest cost

Decide everything up front, then run

  • Predictable multi-step jobs
  • Cost-sensitive work
  • Batch pipelines
  • How it works Plan all steps once, execute in order
  • Best for Known, repeatable sequences
  • Cost / latency Lower (one planning call)
  • Watch out for Brittle when reality differs from the plan
Plan-and-execute splits the job in two: a capable model writes the full plan once, then a cheaper, simpler executor runs the steps in order. Because the expensive reasoning happens a single time, it is far cheaper and more predictable than ReAct for well-understood tasks - think data pipelines or a fixed multi-step report. The trade-off is rigidity: if step three surprises the agent, a pure plan has no way to adapt.

What we liked

  • Cheaper - one big reasoning call, not many
  • Predictable, auditable sequence of steps
  • Fast once the plan exists

Watch out for

  • Brittle if the world differs from the plan
  • Needs a re-plan step to handle surprises

PATTERN 3

Reflection Quality boost

The agent reviews its own work

  • Writing and code
  • High-quality output
  • Self-correcting tasks
  • How it works Generate, critique, revise, repeat
  • Best for Quality-critical outputs
  • Cost / latency Higher (extra review passes)
  • Watch out for Endless self-editing without gains
Reflection is the simplest quality loop: the agent produces an output, then evaluates it against criteria - is this correct, complete, well-written? - and revises if it falls short. The agent becomes its own reviewer. It noticeably lifts quality on writing, code and analysis, and it is often bolted onto ReAct rather than used alone. The cost is extra passes, so you cap the number of revision rounds.

What we liked

  • Meaningfully improves output quality
  • Catches its own mistakes before finalising
  • Easy to add to another pattern

Watch out for

  • Each review round costs more tokens and time
  • Can over-edit with diminishing returns

PATTERN 4

Multi-Agent Orchestration Complex goals

Specialists working under a manager

  • Big, divisible goals
  • Distinct skill sets
  • Parallel work
  • How it works Orchestrator splits work to specialist agents
  • Best for Large, decomposable problems
  • Cost / latency Highest (many agents)
  • Watch out for Coordination overhead and dropped context

In multi-agent systems an orchestrator decomposes the goal and hands each piece to a specialist agent with its own tools and instructions - a researcher, a coder, a reviewer. It shines on large problems that split cleanly and benefit from parallel work or genuinely different skill sets. The danger is overhead: more agents mean more coordination, more places to lose context, and more cost, so reach for it only when one agent genuinely cannot hold the whole job.

What we liked

  • Tackles big goals by divide-and-conquer
  • Specialists can run in parallel
  • Each agent stays focused and simple

Watch out for

  • Coordination overhead and context loss
  • Most expensive pattern to run
ReAct
Reason-act loop. Best for dynamic, tool-heavy tasks. Higher cost, very adaptable.
Plan-and-Execute
Plan once, run the steps. Best for predictable sequences. Cheapest, least adaptable.
Reflection
Self-review and revise. Best for quality-critical output. Adds cost, lifts quality.
Multi-Agent
Orchestrator plus specialists. Best for big, divisible goals. Most powerful and most expensive.

Which pattern should you actually use?

Usually more than one

The honest answer is that good production agents rarely use a single pattern. A realistic setup plans the task up front, then drives each step with a ReAct loop, calls tools with validation and retries, reflects on the result before finalising, and gates anything risky behind a human approval. Multi-agent only enters the picture when one agent genuinely cannot hold the whole job.

So the question is not "which pattern" but "which pattern leads." Start with ReAct for anything open-ended and tool-driven. Switch the backbone to plan-and-execute when the steps are known and you care about cost. Add reflection wherever output quality matters. Split into multiple agents only when the problem is too big for one. For how this plays out in a specific tool, see our Claude Code agent patterns deep dive.

What goes wrong with agent workflows?

The failure modes to design against

Every pattern has a signature failure. ReAct agents loop without progress, repeating the same action - always set a step limit and a stop condition. Plan-and-execute agents are brittle: a plan made with bad assumptions runs confidently off a cliff, so build in a re-plan trigger. Reflection can over-edit, polishing forever with diminishing returns, so cap the rounds. Multi-agent systems lose context between agents and rack up cost through coordination.

The deeper, shared failure is context. Long-running agents degrade as their context window fills with stale history - a problem we cover in context rot - and the gap between a demo and a dependable agent is mostly this unglamorous reliability work, which we dig into in building agents that survive production.

Frequently asked questions

Q01What is the difference between ReAct and plan-and-execute?
ReAct interleaves reasoning and action one step at a time, deciding the next move only after seeing the last result, which makes it adaptable but more expensive. Plan-and-execute decides the whole sequence up front and then runs it, which is cheaper and more predictable but brittle if reality differs from the plan. Many agents plan first, then use ReAct to execute each step.
Q02What is the reflection pattern in AI agents?
Reflection is a self-review loop: the agent generates an output, evaluates it against criteria such as correctness or completeness, and revises if it falls short, acting as its own reviewer. It noticeably improves quality on writing, code and analysis, and is usually added on top of another pattern like ReAct, with a cap on the number of revision rounds to control cost.
Q03When should you use a multi-agent system?
Use multiple agents only when a single agent genuinely cannot hold the whole job - when the goal splits cleanly into specialist sub-tasks, benefits from parallel work, or needs distinct skill sets. Multi-agent systems are the most powerful pattern but also the most expensive, and they add coordination overhead and chances to lose context, so do not reach for them by default.
Q04Do these patterns depend on a specific framework?
No. ReAct, plan-and-execute, reflection and multi-agent are patterns, while LangGraph, CrewAI, the OpenAI Agents SDK and Claude Code are frameworks that implement them. Most frameworks support most patterns, so choose the pattern that fits your problem first, then pick a framework that makes it easy to build.

Related reading

Claude Code Skills and Agent Patterns

Claude Code Skills and Agent Patterns

How these patterns show up in one real tool - skills, sub-agents and MCP in Claude Code.
Building AI Agents That Survive Production

Building AI Agents That Survive Production

The unglamorous reliability work that turns an impressive demo into a dependable agent.
Context Rot in Long-Running Agents

Context Rot in Long-Running Agents

Why agents degrade as their context fills up, and what to do about it.

Last reviewed June 2026. Based on published agent-design literature and current framework documentation (LangGraph, CrewAI, the OpenAI Agents SDK and Claude Code). The agent tooling landscape moves fast - treat framework specifics as a snapshot.