TL;DR Key Takeaways

• ReAct is best for uncertainty. Use it when the agent needs to continuously evaluate new information and decide what to do next.
• Plan-and-Execute is best for structure. It works well when a task has identifiable stages that can be mapped before execution begins.
• The wrong architecture creates real production costs. Excessive model calls, higher token usage, latency, debugging complexity, and failure points can quickly turn an efficient prototype into an expensive production system.
• Hybrid architectures often provide the best balance. They combine high-level planning with adaptive reasoning where individual stages require flexibility.
• Choose the architecture based on the workflow, not the framework. The key question is where uncertainty exists and how much autonomy the agent actually needs.

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Introduction

Building an AI agent is no longer just about selecting a capable language model and connecting it to a few tools. The harder engineering decision is determining how that agent should reason, plan, act, recover from failures, and manage information throughout execution.

As agentic systems move from prototypes into production, architecture matters as much as model selection. Yet many AI/ML teams make the same mistake: they choose a framework first and define the agent’s execution model later.

Two architectures frequently surface in these discussions: ReAct and Plan-and-Execute. Both are useful, but neither is universally better. ReAct enables iterative reasoning and action, while Plan-and-Execute separates planning from execution.

That distinction carries real production consequences. Choosing the wrong architecture can lead to unnecessary model calls, inflated inference costs, increased latency, difficult debugging, and unreliable behavior.

The goal, therefore, is not to find the "best" agentic framework. It is to choose an architecture that matches the work the agent actually needs to perform.

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Start With the Workflow, Not the Framework

Traditional software generally follows a predictable execution path. Developers define the logic, the application processes inputs, calls functions, and produces an output.

Agentic systems introduce another layer: the system can decide what to do next based on the situation.

That flexibility is valuable, but it also introduces architectural complexity. An agent may need to select a tool, interpret its output, determine whether additional information is needed, and decide what action should follow.

Before selecting an architecture, engineering teams should understand where uncertainty exists in the workflow by asking:

• Is the workflow predictable or exploratory?
• Can most steps be defined before execution?
• Does each action depend on the previous result?
• How expensive are additional model calls?
• How much execution control is required?
• What happens when an action fails?

These questions help determine whether the system needs continuous decision-making, structured planning, or both.

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How ReAct Works

ReAct, short for Reasoning and Acting, follows an iterative loop. The agent evaluates the current state, chooses an action, receives an observation, and uses that new information to determine what to do next.

The key advantage is that the entire workflow does not need to be known upfront.

Consider an agent investigating a production application experiencing a sudden surge in errors. It might begin by analyzing application logs. The logs could indicate database timeouts, prompting the agent to inspect database metrics. If those metrics look normal, the agent could investigate a recent deployment instead.

The path changes as new information becomes available.

This makes ReAct particularly useful for exploratory workflows where the next decision cannot reliably be determined in advance. Troubleshooting, research, investigation, and open-ended information gathering are examples where adaptive reasoning proves invaluable.

However, that flexibility comes at a cost.

Each reasoning cycle can require another model call. Tool execution adds latency, and long workflows can create a heavy chain of model interactions.

A task requiring ten or twenty meaningful decisions can quickly become expensive and slow if every decision passes through a model.

Where Plan-and-Execute Fits

Plan-and-Execute takes a more structured approach. Instead of determining every action independently, the system first creates a plan and then passes that plan to an execution layer.

Imagine an agent supporting a cloud migration. The workflow might involve reviewing dependencies, preparing infrastructure, migrating data, updating application configurations, testing services, and validating the environment.

If those stages are reasonably predictable, creating the plan upfront is more efficient than repeatedly asking the model to determine the overall direction.

The separation between planning and execution also creates a clean engineering boundary. The planner determines what needs to happen; the executor focuses on carrying out those actions. This improves observability. Engineers can inspect the generated plan, understand the intended sequence, and identify where execution diverged from expectations.

Nevertheless, Plan-and-Execute introduces a distinct risk: a flawed plan produces a flawed execution path.

If the planner misunderstands a dependency or makes an incorrect assumption, the executor will continue following that plan unless the architecture includes validation and replanning mechanisms. Planning provides structure, but it does not eliminate uncertainty.

The Production Cost of the Wrong Architecture

The weaknesses of an architecture often remain invisible during a proof of concept. A prototype typically involves a handful of users, limited workloads, and engineers monitoring the system manually, making an inefficient reasoning loop seem harmless.

Production completely changes the equation.

‍Latency

Sequential model calls and tool executions quickly compound end-to-end response time. This is critical for applications where users expect near-real-time responses. If the agent repeatedly reasons about decisions that could have been established earlier, users wait for unnecessary computation.

Inference Cost

More reasoning generally means more model usage. A workflow that costs pennies during testing scales up rapidly when run thousands or millions of times. If a decision can be handled through deterministic application logic or a predefined execution path, utilizing a model adds cost without adding value.

Debugging Complexity

Agent failures are far more complicated than traditional application errors. When an agent produces an incorrect result, engineers must determine whether the issue stems from the model, prompt, tool selection, tool output, context, planning logic, or an earlier decision. Dynamic ReAct workflows complicate this further because execution paths vary between runs.

Reliability

Every additional reasoning step creates another opportunity for failure. A model may select the wrong tool, misinterpret an observation, or execute an unnecessary action. The longer the workflow runs, the more points of failure it introduces. Architecture directly dictates reliability, not just performance and cost.

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ReAct vs. Plan-and-Execute: Match the Architecture to the Work

The most effective way to compare these approaches is to examine the workflow rather than the technologies themselves.

ReAct shines when:

• The next action depends heavily on new information.
• The workflow is exploratory.
• Tool results can significantly shift the direction of execution.
• The system needs to investigate unknown conditions.
• Flexibility outweighs strict sequencing.

A troubleshooting agent serves as a prime example. It cannot know whether to inspect logs, metrics, configurations, or deployments until it evaluates the initial investigation results.

Plan-and-Execute fits best when:

• The task contains several identifiable stages.
• The overall workflow can be mapped before execution.
• Multiple steps follow a logical sequence.
• Execution demands stronger structure and observability.
• Planning eliminates repeated decision-making.

For instance, an agent generating a structured report from several known data sources benefits from creating a rigid workflow first and executing it afterward.

These are guidelines, not rigid rules. Many production workloads contain elements of both architectures.

Why Hybrid Architectures Often Win

Production systems do not have to choose between pure ReAct and pure Plan-and-Execute.

A hybrid architecture provides high-level planning while allowing adaptive reasoning within individual stages. This approach is particularly useful as agentic systems move into production. IBM Research’s 2026 study of 306 practitioners across 26 domains found that 68% of production agents execute 10 steps or fewer before human intervention, while reliability remains the top development challenge. The findings reinforce the value of keeping agent workflows structured and controllable rather than relying entirely on open-ended autonomy.

Consider a security investigation agent. The system could first create a high-level plan: establish scope, inspect relevant events, identify suspicious activity, validate findings, and produce a report.

During the investigation stage, however, the agent may need to dynamically decide which logs or events to inspect based on what it discovers. A ReAct loop can handle that uncertainty without turning the entire workflow into an open-ended reasoning process.

This creates a practical balance: structure where structure helps, flexibility where flexibility is necessary. Hybrid architectures also support replanning. If execution surfaces unexpected data, the system can return to the planning layer, adjust its assumptions, and generate a revised path.

That flexibility matters because real-world agent tasks rarely remain completely predictable from start to finish. A 2026 survey of agent systems highlights the same challenge, identifying non-determinism, tool variability, retries, and growing context as important factors when evaluating production architectures.

The result is an architecture that can follow a deliberate path when the workflow is known while still adapting when new information changes what should happen next.

Designing for System Resilience and Failure

A frequent pitfall in agent development is evaluating performance exclusively against ideal conditions. One of the most common agent-development mistakes is testing only the happy path.

An architecture may perform flawlessly when APIs respond instantly, tools return complete data, and the model behaves rationally. Production environments are rarely that forgiving.

A production-ready agent must account for scenarios where:

• A tool times out.
• An API returns partial or incomplete information.
• The wrong tool gets selected.
• A step requires a retry.
• The current plan becomes invalid.
• A high-impact action demands human approval.

These contingencies should be baked into the architecture rather than patched together after production failures occur.

ReAct adapts naturally when new observations shift the landscape, but it still requires strict boundaries around retries, tool usage, and model behavior. Plan-and-Execute offers robust structure, but requires mechanisms to detect when the original plan breaks down.

Neither approach eliminates failure entirely. The objective is to make failure predictable, observable, and recoverable.

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A Better Architecture Selection Process

Instead of picking a framework blindly, start by mapping the workflow.

Break the task into its major stages. Identify which steps are deterministic, which require model reasoning, and which depend on information discovered during execution. This matters as AI adoption grows. Google’s 2025 DORA research found that 90% of technology professionals use AI at work, and more than 80% report productivity gains. However, higher AI adoption is also associated with increased delivery instability, making productivity alone insufficient for evaluating an AI architecture. (Google)

Next, evaluate the architecture against measurable requirements:

• Model-call count: How many model invocations does each task require?
• Latency: How long does the complete workflow take, including tool execution?
• Token usage: How much context is consumed, and what does it mean for cost at scale?
• Completion quality: Does it consistently produce the expected result?
• Failure recovery: Can it recover when a model call, tool, or step fails?
• Observability: Can engineers trace decisions and identify where execution went wrong?

Test these metrics across representative workloads rather than relying on a single successful demonstration. An architecture that delivers accurate results but requires excessive model calls may add unnecessary cost and latency, while one with fewer calls may struggle with recovery or changing information.

Observability is especially important in agentic workflows. The OpenAI Agents SDK, for example, provides tracing for LLM generations, tool calls, handoffs, and guardrails to help teams debug and monitor execution. (OpenAI Agents SDK)

Testing these metrics across realistic workloads provides a stronger basis for choosing an architecture than selecting a framework simply because it is popular or easy to prototype. The goal is to find the right balance of quality, latency, cost, reliability, and control for the task.

The Framework Should Serve the Architecture

Another frequent misstep is treating a framework and an architecture as the same thing. A framework supplies implementation capabilities, whereas architecture dictates how those capabilities are organized.

An exceptional framework cannot rescue a flawed execution model. Conversely, a reliable agent can be built using relatively simple components when the architecture aligns with the workflow.

This distinction grows increasingly critical as systems mature. Once an application accumulates tools, prompts, state management, integrations, and production dependencies, overhauling the underlying architecture becomes exponentially more expensive.

The earlier teams define their execution model, the easier it is to scale and adapt later.

Architecture as a Strategic Business Choice

Selecting an agent architecture might seem like a purely technical endeavor, but its downstream consequences directly affect core business outcomes.

Architectural decisions filter down into several critical business metrics:

• Financial Sustainability: Excessive model consumption and inefficient reasoning loops rapidly inflate operational expenses.
• User Experience: High latency and prolonged execution times frustrate users who depend on timely responses.
• Trust and Compliance: Unpredictable agent actions undermine reliability, making over-flexible systems unsuited for strictly regulated environments where auditability and control are mandatory.
• Engineering Efficiency: Convoluted system designs complicate debugging, demanding significant developer cycles to resolve failures.

Ultimately, a sophisticated technical design offers little value if it is not commercially viable. An agent consuming substantial compute resources over minutes cannot serve workflows requiring near-instant turnaround times.

This architectural impact grows exponentially as systems mature. As applications accumulate integrations, toolsets, state management logic, and production dependencies, refactoring the foundational architecture becomes drastically more costly. Establishing the execution model early ensures the system can scale smoothly and adapt to future demands.

Fixing an Agent Architecture That Is Already Wrong

If an existing agent suffers from high latency, unpredictable behavior, or rising costs, rebuilding the entire system from scratch is rarely necessary.

Start with the execution trace.

Look for redundant reasoning cycles, unnecessary tool calls, deterministic operations handled by the model, and decisions repeatedly rediscovered.

A practical redesign may involve shifting predictable operations into standard application logic, introducing a planning layer for structured workflows, restricting ReAct strictly to scenarios where decisions depend on observations, validating high-impact actions, and implementing dynamic replanning when execution invalidates original assumptions.

The goal is not to make the entire system aggressively "agentic," but rather to ensure each component employs the right level of intelligence.

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The Right Agentic Architecture Fits the Work

ReAct and Plan-and-Execute are not competing technologies where one must ultimately win. They solve fundamentally different architectural problems.

ReAct works best when an agent must continuously react to discoveries. Plan-and-Execute excels when a meaningful sequence can be established ahead of time. Hybrid systems succeed when production workflows demand both structured direction and adaptive reasoning.

The costly mistake is not choosing ReAct over Plan-and-Execute, or vice versa.

The expensive mistake is choosing either without first understanding the workflow.

For AI/ML engineers, architecture selection begins with a single question: Where does the uncertainty actually live in this task?

If uncertainty lies in discovering the next action, iterative reasoning is likely the right path. If uncertainty can be largely resolved during task decomposition, planning offers superior control. If both exist, a hybrid architecture delivers structure without sacrificing adaptability.

The strongest agentic systems are not those that reason at every single step, but those that know precisely when reasoning is necessary, when a plan is sufficient, and when the system should simply execute.

Conclusion

The right agentic architecture is not about choosing the most popular framework, it is about matching the architecture to the work. ReAct fits dynamic, uncertain workflows, while Plan-and-Execute works best for structured tasks. Hybrid approaches can combine both where needed.

Ultimately, successful agents reason only when necessary, execute predictably when possible, and adapt when conditions change.

The future of AI is not just about building agents. It is about building them right.

Explore our Agentic AI course and gain hands-on skills to design, deploy, and monitor production-ready AI agents.

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FAQS

1. What is the difference between ReAct and Plan-and-Execute?

ReAct uses an iterative reasoning-and-action loop, where the agent decides what to do next based on new observations. Plan-and-Execute separates planning from execution by creating a structured plan before carrying out the individual steps. ReAct is better suited to uncertain, changing workflows, while Plan-and-Execute works well when the task can be mapped out in advance.

2. When should I use ReAct for an AI agent?

ReAct is a strong choice when the next action depends on information discovered during execution. It works particularly well for troubleshooting, investigation, research, and other workflows where conditions can change and the agent needs to continuously reassess its next step.

3. When is Plan-and-Execute a better approach?

Plan-and-Execute is generally more effective when a task has clearly defined stages and the overall workflow can be determined before execution. It can reduce unnecessary decision-making during execution while providing better structure, traceability, and control over complex multi-step workflows.

4. Why can choosing the wrong agent architecture increase costs?

An architecture that requires unnecessary model calls can increase token consumption, inference costs, and latency. Long reasoning loops can also introduce more tool calls and failure points. At production scale, these inefficiencies can significantly increase operating costs compared with an architecture that uses model reasoning only where it adds value.

5. Can ReAct and Plan-and-Execute be used together?

Yes. A hybrid architecture can use Plan-and-Execute for high-level workflow structure while using ReAct within stages that require adaptive reasoning. This approach allows teams to maintain control over predictable processes without sacrificing flexibility when new information changes the execution path.

6. How should teams choose the right agentic architecture?

Teams should evaluate the workflow before selecting a framework or architecture. Key considerations include how predictable the task is, where uncertainty occurs, the number of model calls required, latency, token usage, failure recovery, observability, and the level of execution control required. The goal is to use the simplest architecture that provides the reasoning and adaptability the workflow actually needs.

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