The Invisible Decision-Maker
Imagine you are hiring a highly capable, tireless research assistant. You give them a complex task—say, “Find the best travel route for a multi-city trip and book the hotels.” You don’t expect them to guess the answer instantly. You expect them to look at a map, check flight availability, compare hotel prices, and adjust their plan if a flight is sold out.
This is exactly how modern AI agents operate. While a standard Large Language Model (LLM) is like a brilliant writer who answers questions based on what it has already learned, an AI agent is a doer. It doesn’t just generate text; it interacts with the world to achieve a goal. The question is: how does it decide what to do next?
The Core Loop: Reason, Act, Observe
At its core, an AI agent makes decisions through a recurring, iterative loop: it reasons about its current state, plans the next action, selects and calls a tool, observes the result, updates its plan, and repeats the process until the goal is met [1]. This is not a single “thought” but a continuous cycle of interaction with its environment.
Think of it as the difference between taking a single photograph and watching a live video feed. A standard LLM gives you one snapshot—an answer based on what it already knows. An agent watches the feed, reacts to what it sees, and adjusts in real time.
The Chef in the Kitchen
A useful way to picture the agentic loop is to imagine a chef in a professional kitchen. The chef has a recipe (the goal), but they must constantly adapt to what’s happening:
- Observation: They taste the sauce.
- Reasoning: They realize it needs more salt.
- Tool use: They reach for the salt shaker.
- Feedback: They taste it again to check if the adjustment worked.
If the sauce is still wrong, they don’t just keep adding salt blindly—they step back and rethink their approach. Maybe the problem is acidity, not saltiness. This constant cycle of tasting, thinking, acting, and re-tasting is the heartbeat of agentic intelligence.
The Foundation: Chain-of-Thought Reasoning
Before an agent can act effectively, it needs to think in steps. The foundation for this is Chain-of-Thought (CoT) prompting, introduced by Wei et al. in January 2022 [2]. CoT guides models to produce intermediate reasoning steps before arriving at a final answer—like showing your work on a math test rather than jumping straight to the result.
This matters because multi-step problems are where single-pass LLMs tend to stumble. By explicitly writing out each logical step, the model catches errors that mental arithmetic would miss. For agents, CoT is the “thinking” part of the loop that happens before and between every action.
ReAct: Thinking While Doing
Most modern agents are built on the ReAct framework, introduced by Yao et al. at Princeton and Google Research in October 2022 [1]. ReAct (Reason + Act) interleaves reasoning traces with actions in a simple sequence:
- Thought: The agent reasons about what it needs to do next.
- Action: It selects and calls a tool (like a search engine or calculator).
- Observation: It reads the tool’s output.
- Repeat until the goal is reached or a stopping condition is met.
This interleaving is crucial because it grounds the agent’s reasoning in real-world data. Instead of relying solely on its internal predictions—which can sometimes lead to hallucinations—the agent uses actual tool outputs to verify its progress. ReAct outperformed prior approaches on question answering (HotpotQA), fact verification (FEVER), and interactive decision-making benchmarks like ALFWorld and WebShop [1].
Planning: Two Main Approaches
Not all agents plan the same way. The two dominant strategies serve different needs:
Hierarchical planning breaks a high-level goal into smaller sub-goals, each handled by a specialized sub-agent. A “manager” agent delegates work to “worker” agents, each focused on a specific subtask [3]. This works well for complex, multi-step projects where different parts require different expertise—like a project manager coordinating designers, developers, and writers.
Plan-and-execute takes a different approach: the agent creates a complete plan upfront, then executes it step by step [3]. This can be more efficient than a standard ReAct loop because it reduces the number of times the model needs to “stop and think” at every step. Think of the difference between improvising a recipe step-by-step versus writing out the full recipe before you start cooking.
How an Agent Picks Its Tools
How does an agent know which tool to reach for? The LLM acts as the reasoning engine, evaluating which function or API best advances the current goal [4]. Whether it is querying a database, calling a function, running code, or browsing the web, the agent selects the tool most likely to provide the information or action needed.
This is one of the key distinctions between an agent and a simple prompt-engineered chatbot: the agent has access to a toolkit and the reasoning capacity to choose the right tool for each situation, rather than relying on a single predefined response path.
Learning from Mistakes: Reflection and Self-Correction
What happens when an agent makes a mistake? Robust agents use reflection patterns, where they critique their own outputs and re-plan if they encounter an error [3]. This is where recent advances in reinforcement learning have made a significant difference.
DeepSeek-R1, published in January 2025, demonstrated that RL can train models to autonomously recognize and correct their own mistakes mid-reasoning—what researchers call “self-reflection” and “verification” [6]. Instead of blindly continuing down a wrong path, the agent pauses, checks its work, and adjusts. This makes agents significantly more reliable on complex, multi-step tasks, and it’s why reasoning models have become central to the agentic architecture.
Industry Convergence: Everyone Agrees on the Loop
There is a striking consensus among major AI providers on this architectural approach:
- Anthropic’s “Building Effective Agents” (December 19, 2024) defines the agent core as an LLM “using tools based on environmental feedback in a loop” and catalogs production patterns: prompt chaining, routing, parallelization, orchestrator-workers, and the evaluator-optimizer loop [3].
- OpenAI’s agents documentation frames agents as LLMs with tools that execute in a loop with instructions, tools, and guardrails [4].
- Google Cloud’s design pattern guide (updated November 2025) formalizes the ReAct pattern as an iterative thought → action → observation loop with explicit exit conditions, nested under deterministic workflows and dynamic orchestration [5].
The cross-cutting consensus: the outer loop is Reason/Act/Observe, termination conditions are explicit architectural components (not afterthoughts), and production systems layer orchestration and evaluator sub-loops on top of the single-agent loop [3].
The OODA Loop Challenge
However, this decision-making architecture is not without difficulty. Bruce Schneier and Barath Raghavan, in their October 2025 essay “Agentic AI’s OODA Loop Problem” published through Harvard’s Berkman Klein Center, identify a fundamental challenge [7].
The OODA framework—Observe, Orient, Decide, Act—was originally designed for real-time decision-making in adversarial situations. Schneier and Raghavan argue that AI agents face a unique difficulty within this loop: they must compress the complexity of the real world into a format that the model can understand and act upon. This “compression” is where errors can creep in, and adversaries can craft inputs that corrupt future outputs. Their proposed solution: equip AI agents with integrity—not just capability, but the capacity to maintain coherent decision-making under pressure.
Why This Matters for the Agentic Economy
Understanding how agents make decisions is the key to the emerging agentic economy. As these systems move from simple chatbots to autonomous agents capable of managing workflows, coordinating with other agents, and executing transactions, the ability to trust their decision-making process—and understand how they recover from errors—will determine their utility in business, finance, and everyday life.
The decision loop is not just a technical detail; it is the engine that makes the entire agentic economy possible. When an agent negotiates a contract, books a flight, or processes a payment, it is running this loop hundreds or thousands of times. Understanding the loop means understanding what you can trust—and where you should still keep a human in the loop.
Reader Questions
Q: Can an agent learn from its mistakes?
A: Yes, through two mechanisms. First, feedback loops: agents monitor the results of their actions and adjust subsequent decisions based on what happened. Second, reflection patterns: agents can critique their own outputs and re-plan when they detect errors. Advanced models trained with reinforcement learning, like DeepSeek-R1, can even recognize and correct mistakes mid-reasoning without human intervention.
Q: Is an agent just a chatbot with a search engine?
A: Not quite. While a chatbot might use a search engine, an agent is defined by its ability to operate in a recurring loop, using tools to achieve a goal without needing constant human intervention at every step. The key difference is the loop: reason, act, observe, repeat until the goal is met.
Q: What is the difference between ReAct and Plan-and-Execute?
A: ReAct is an iterative loop where the agent thinks, acts, and observes at every single step—it improvises as it goes. Plan-and-execute involves creating a full plan first and then following it, which can be more efficient for predictable tasks but less adaptable when conditions change mid-execution.
Q: Why do agents sometimes fail?
A: Failures often occur when the agent misinterprets its environment or when the “compression” of reality into model-legible data leads to a loss of critical context—a challenge Schneier and Raghavan call the OODA loop problem. Other failure modes include tool errors, ambiguous instructions, and reward hacking in RL-trained models.
Q: Are all agents built the same way?
A: While there is a consensus on the core “Reason-Act-Observe” loop, different systems use different patterns—such as parallelization, routing, hierarchical planning, or evaluator-optimizer loops—to handle more complex, multi-layered tasks. The choice depends on the task’s complexity, latency requirements, and how much human oversight is needed.