Stop Using ReAct: Why Planning Agents Are the Future of AI
Elijah TobsBy Elijah Tobs
Tech
May 30, 2026 • 8:11 PM
8m8 min read
Verified
Source: Unsplash
The Core Insight
This guide explores the transition from reactive AI agent patterns (ReAct) to proactive Planning patterns. It explains why complex, multi-step tasks require a global strategy rather than step-by-step improvisation, and outlines the architectural shift needed to build agents that can formulate roadmaps before execution.
As the founder and primary investigative voice at Kodawire, Elijah Tobs brings over 15 years of experience in dissecting complex geopolitical and financial systems. His work is centered on the ethical governance of emerging technologies, the shifting architectures of global finance, and the future of pedagogy in a digital-first world. A staunch advocate for high-fidelity journalism, he established Kodawire to be a sanctuary for deep-dive intelligence. Moving away from the ephemeral nature of modern headlines, Kodawire delivers permanent, verified insights that challenge the status quo and empower the global reader.
In AI development, we often start with the basics. For many, that means the ReAct pattern, a method where an agent interleaves reasoning and acting in a continuous loop. It’s intuitive: the agent thinks, acts, observes the result, and adjusts. It’s a solid approach for simple, uncertain tasks, like finding a pen on a cluttered desk. But as I’ve found in my own work building autonomous systems, the moment you move from "find a pen" to "manage a project," the reactive loop starts to fray.
The Bottom Line
ReAct is for reaction: Use it for simple, single-step, or highly uncertain tasks where you don't know the next move until you see the result.
Planning is for strategy: Use it for complex, multi-step workflows where foresight prevents redundant work and "meandering."
The Lifecycle: A robust planning agent follows a three-stage loop: Plan, Execute, and Adjust.
Dynamic Replanning: Always build in the ability to update the roadmap mid-execution, if you run out of milk, you need to add a "buy milk" step to the plan.
The limitation of purely reactive loops is their lack of global perspective. They are tactical, not strategic. This is where the Planning pattern enters the frame. Instead of diving headfirst into a task, a Planning agent takes a step back to formulate a high-level roadmap. It’s the difference between a person wandering through a forest hoping to find the exit and a hiker checking a map before setting off. For those managing complex data, understanding AI memory architecture is essential to maintaining that global perspective.
Strategic planning allows AI agents to navigate complex tasks without losing their objective. (Credit: Vitaly Gariev via Unsplash)
Why Planning Beats Reacting for Complex Tasks
The cognitive load of multi-step problem solving is high, even for advanced models. If you ask an agent to make a flat white coffee, it isn't just one action. It’s a sequence: boil water, grind beans, froth milk, and assemble. A reactive agent might try to froth the milk before the water is even hot, or worse, repeat the same search query three times because it lacks a memory of its own global objective. Mastering context engineering can help mitigate these common pitfalls.
By forcing the agent to outline its approach first, you eliminate the "meandering" that plagues reactive systems. When an agent has a plan, it has a yardstick to measure its progress. If it deviates, it knows it’s off-track. This isn't just about efficiency; it’s about reliability. In my experience, the most common point of failure in agentic workflows is the agent losing the "thread" of the original request. Planning keeps that thread taut.
The Other Side of the Story
Most developers are obsessed with "agentic autonomy," believing that the more an agent can "think on its feet," the better. I disagree. In production environments, unconstrained autonomy is a liability. By forcing an agent to plan, you are actually restricting its freedom, which is exactly what you want. You don't want an agent to be "creative" about how it handles your database; you want it to follow a verified, logical plan. Sometimes, less "intelligence" and more "structure" is the key to a stable system.
A true Planning agent operates on a three-stage lifecycle: Plan, Execute, and Adjust.
First, the agent generates a sequence of steps. Second, it executes those steps using its available tools. Third, and most importantly, it adjusts. If the agent reaches the "froth milk" step and realizes the fridge is empty, it doesn't just fail. It triggers a replanning phase, inserting a "buy milk" step into the roadmap. This mirrors human project management, we rarely execute a plan exactly as written, but having the plan allows us to pivot intelligently rather than panicking.
A structured plan acts as a roadmap for AI agents to follow during execution. (Credit: Kvalifik via Unsplash)
The Hands-On Experience
When I set up my own planning agents, I typically use Python as the orchestration layer. Whether you are hitting the OpenAI API or running a local Llama3 model via Ollama, the logic remains the same. You need a prompt that explicitly asks the model to output a JSON-formatted plan before it touches any tools. I’ve found that using a structured output format (like Pydantic models in Python) is the only way to ensure the agent doesn't hallucinate its own task list. For those scaling these systems, consider the strategic deployment of LLMs to ensure performance.
The Long-Term Verdict
Is the Planning pattern here to stay? Absolutely. While ReAct is great for simple chatbots, any agent that interacts with real-world APIs or complex data pipelines will eventually require a planning layer. We are seeing this in frameworks like CrewAI, which bake internal planning into their multi-agent orchestration. If you are building for the long term, don't just build a reactive loop, build a planner.
The Decision Matrix
Not sure which pattern to use? Use this simple guide:
Task has < 3 steps? Use ReAct. It’s faster and cheaper.
Task requires external resources (files, databases, web)? Use Planning.
Task is highly unpredictable? Use ReAct, but add a "check-in" step.
Task is a multi-stage project? Use Planning.
My Recommended Setup
Orchestration: Python (standard library + Pydantic for schema enforcement).
Local Inference: Ollama (for running Llama3 or Mistral locally).
Debugging: LangSmith or simple JSON logging to track the "Plan" vs. "Action" state transitions.
The Practical Verdict
The shift from ReAct to Planning is a shift from "doing" to "managing." It requires more upfront work, more complex prompt engineering, and a more robust loop. However, the payoff is an agent that doesn't just guess its way through a problem, but solves it with the deliberate, step-by-step logic of a human expert. If you want to move beyond simple demos and into production-grade AI, start planning.
Moving to production-grade AI requires moving from reactive loops to deliberate planning. (Credit: Glenn Carstens-Peters via Unsplash)
What Do You Think?
Have you found that your agents get "lost" in long tasks, or have you successfully implemented a planning layer that keeps them on track? I’m curious to hear about your experiences with dynamic replanning. I’ll be in the comments for the next 24 hours to discuss your implementation hurdles.
ReAct is a reactive loop where an agent thinks and acts in real-time without a global roadmap, whereas Planning involves formulating a high-level strategy before execution to prevent meandering.
ReAct is best suited for simple, single-step tasks or highly unpredictable scenarios where the next move cannot be determined until the result of the previous action is observed.
A planning agent follows a three-stage loop: Plan (formulating the roadmap), Execute (performing the steps), and Adjust (updating the plan based on real-world feedback).
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Editorial Team • Question of the Day
"Do you think AI agents should be allowed to "replan" without human oversight, or is that a security risk?"
