The Three Core Agent Architecture Patterns, Compared
Most production agents are not free-form "chat with tools." They follow a small set of control-flow patterns that decide when the model thinks, when it calls tools, and when work moves to another agent.
This page maps the three patterns that dominate agent design as of 2025-2026: ReAct, Plan-and-Execute, and multi-agent handoff. The rest of this section deep-dives each one and helps you choose.
Summary
- Agent architecture is the control loop that sequences reasoning, tool use, and (optionally) delegation - not the choice of LLM or framework brand.
- Insight: The same tools and model can feel reliable or chaotic depending on whether the loop plans ahead, reacts step by step, or splits work across specialists.
- Key Concepts: ReAct loop, plan-then-execute, orchestrator, specialist handoff, stopping conditions, context budget.
- When to Use: Designing a first agent, rewriting a chat-with-tools prototype into something shippable, or explaining architecture choices in a design review.
- Limitations/Trade-offs: These three patterns blend in hybrids; real systems often plan at the top level and run ReAct sub-loops inside specialists. Pattern names are mental models, not framework APIs.
- Related Topics: agent loops, stopping conditions, context windows, multi-agent communication contracts.
Foundations
An agent architecture is the policy for what happens on the next step after the model produces a thought, a tool call, or a final answer. It answers questions like: Do we invent the next action only after seeing the last tool result? Do we write a full plan first? Can another agent take over the session?
ReAct (Reason + Act) interleaves short reasoning with tool calls. Each cycle looks like: reason about the current state, choose one action, observe the tool result, then reason again until a stop condition fires.
Plan-and-Execute splits the run into two phases. A planner produces an ordered list of steps (often natural language or structured JSON); an executor walks that plan, typically with tools, and may replan only when a step fails or the world changes.
Multi-agent handoff treats the problem as a team workflow. An orchestrator (or a peer agent) routes the user goal to a specialist with a narrower tool set and prompt, then receives a structured result and continues or hands off again.
A useful analogy: ReAct is a skilled generalist solving a ticket one command at a time; Plan-and-Execute is writing a checklist before you open the toolbox; multi-agent handoff is a desk manager assigning the ticket to the right specialist and collecting the write-up.
User goal
|
+-- ReAct ------------> reason -> act -> observe -> ... -> answer
|
+-- Plan-and-Execute -> plan -> execute steps -> (optional replan) -> answer
|
+-- Multi-agent ------> route -> specialist loop -> handoff/return -> answerMechanics & Interactions
The three patterns differ less in "intelligence" and more in when decisions are made and what lives in context.
In ReAct, the decision boundary is every tool observation. The model can adapt after each search, API call, or file read, which is powerful when the next step depends on intermediate data. The cost is that every turn reloads history: tool outputs accumulate, latency stacks, and without hard stopping conditions the loop can thrash.
In Plan-and-Execute, the decision boundary is the plan (plus rare replans). You pay for planning tokens once (or occasionally), then execute steps with less open-ended wandering. This shines when the task decomposes cleanly - research outline, multi-file code change checklist, multi-system ops runbook - and when wasted exploratory tool calls are expensive.
In multi-agent handoff, the decision boundary is who owns the next subgoal. Each specialist keeps a smaller prompt and tool surface, which improves focus and can reduce tool misuse. You pay coordination tax: handoff schemas, context packing, failure routing, and debugging across multiple loops instead of one.
All three still rest on the same agent loop primitives: perceive input, reason, act with tools, observe results. Architecture chooses the shape of that loop - interleaved, phased, or distributed - not whether tools exist.
| Pattern | Control flow | Strength | Weakness | Best fit |
|---|---|---|---|---|
| ReAct | Interleaved reason/act/observe | Adapts to tool results mid-run | Context bloat; easy to loop | Exploratory tasks, unknown tool path |
| Plan-and-Execute | Plan phase then step execution | Fewer wasted calls; clearer audit trail | Brittle if plan is wrong early | Multi-step work with known structure |
| Multi-agent handoff | Route + specialist loops | Narrow tools/prompts per role | Coordination and handoff bugs | Heterogeneous skills or risk isolation |
Advanced Considerations & Applications
Frameworks encode these patterns under different names. LangGraph graphs often implement ReAct-style tool nodes or plan/execute subgraphs; CrewAI hierarchical crews resemble orchestrator + specialists; OpenAI Agents SDK exposes explicit handoffs; Microsoft Agent Framework merges conversational multi-agent patterns with workflow graphs. The portable skill is recognizing the pattern independent of the SDK.
Hybrids are normal in production. A common design is: Plan-and-Execute at the top (stable milestone list), ReAct inside each milestone (flexible tool use), multi-agent only where a specialist needs a different model, tool allowlist, or compliance boundary. See Hybrid Architectures: Combining Planning, Reaction, and Delegation.
Cost and reliability interact with architecture more than model choice alone. ReAct multiplies tokens by turns; Plan-and-Execute multiplies by plan quality and replan frequency; multi-agent multiplies by the number of agent invocations and the size of each handoff payload. Observability should log pattern phase (plan vs execute vs handoff) so incidents are not just "the agent failed."
Security also follows architecture. A single ReAct agent with a wide tool belt is one prompt-injection away from a bad tool call. Specialists with least-privilege tools reduce blast radius if handoffs cannot escalate privileges silently.
Common Misconceptions
- "Multi-agent is always smarter than one agent." Extra agents only help when roles, tools, or risk boundaries are genuinely different; otherwise you add handoff failures without capability.
- "ReAct means free-form chain-of-thought with no structure." Production ReAct still needs max turns, tool allowlists, and explicit done conditions.
- "Plan-and-Execute never replans." Good implementations replan when a step fails verification, when tools return unexpected state, or when the user goal changes.
- "Architecture is the framework you install." Frameworks are implementations; architecture is the control-flow contract your team can draw on a whiteboard.
- "You must pick exactly one pattern forever." Many systems start as ReAct and graduate into planned or multi-agent only for the hard paths.
FAQs
What is an agent architecture in practical terms?
It is the control policy that decides the next step after each model output or tool result - continue, replan, hand off, or stop - plus how context and tools are scoped for that step.
Why are ReAct, Plan-and-Execute, and multi-agent the three to learn first?
They cover the main axes of agent control: step-wise reaction, upfront structure, and role-based delegation. Most framework features are variations or hybrids of these three.
How is ReAct different from a plain chat-with-tools session?
Chat-with-tools can be a single tool call in one turn. ReAct is an explicit multi-turn loop that keeps reasoning and acting until a goal check or max-turn limit stops it.
When does Plan-and-Execute beat ReAct?
When the task has a natural ordered structure and blind exploration wastes money or time - for example multi-source research with a fixed outline, or a code change that must follow a checklist of files and tests.
When does multi-agent handoff beat a single agent?
When specialists need different tools, prompts, models, or trust levels, or when human review gates should sit between roles (for example research vs publishing vs deploy).
Do all three patterns need stopping conditions?
Yes. Max turns, timeouts, goal-completion checks, and budget caps apply to every pattern. Architecture does not replace loop bounds.
Is multi-agent the same as calling multiple tools?
No. Multiple tools can live inside one ReAct agent. Multi-agent means multiple agent instances (or roles) with their own loops, prompts, and usually separate tool surfaces.
Can Plan-and-Execute still use tools during planning?
Yes, light planning-time tools (search for constraints, list files) can improve the plan, but heavy execution usually belongs in the execute phase so the plan stays short and reviewable.
How do I debug which pattern is failing?
Log the phase (reason/act, plan step, or handoff), the decision, tool results, and stop reason. Pattern-level traces beat only dumping raw model messages.
Does a stronger model remove the need for architecture?
No. Better models reduce some reasoning errors but do not remove infinite loops, context overflow, or privilege mistakes. Architecture still bounds cost and failure modes.
Where should a beginner start?
Start with a bounded ReAct loop for one clear tool-using task, then add Plan-and-Execute when tasks get long and structured, then multi-agent only when a second role earns its complexity.
Are these patterns tied to a specific vendor?
No. They are vendor-agnostic control patterns. SDKs differ in APIs, but the whiteboard shapes stay the same.
Related
- Agent Architectures Basics - hands-on first comparison of ReAct vs planned steps
- ReAct: Reasoning and Acting in an Interleaved Loop - deep dive on the interleaved loop
- Plan-and-Execute: Separating Planning from Execution - deep dive on phased planning
- Multi-Agent Handoff: Specialist Agents Passing Work to Each Other - deep dive on orchestration and specialists
- When to Choose ReAct vs Plan-and-Execute vs Multi-Agent - decision cheatsheet
- Inside the Agent Loop: Perceive, Reason, Act, Observe - loop primitives shared by all patterns
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