--- title: "How Does AI Make Decisions? Explained Simply" description: "How does AI make decisions? Training, next-word prediction, temperature and agent loops explained simply, with sources from Anthropic, OpenAI and NIST." slug: "how-does-an-ai-make-decisions-explained-simply" url: "https://catalizadora.ai/blog/how-does-an-ai-make-decisions-explained-simply" cluster: "conceptos-ia-agentes" author: "Pablo Estrada" published_at: "2026-06-20T09:12:47.868+00:00" updated_at: "2026-10-29T00:24:41.229515+00:00" read_minutes: "6" lang: "en" --- # How Does AI Make Decisions? Explained Simply > How does AI make decisions? Training, next-word prediction, temperature and agent loops explained simply, with sources from Anthropic, OpenAI and NIST. How does AI make decisions? A language model like Claude is trained on large amounts of text to predict the next word, and each answer is built from what it learned in training plus the instructions and information in its context. An AI agent adds a loop on top: it picks a tool, reads the result and decides the next step, within limits that the people who build it set. ## How does AI make decisions? It learns from data, then predicts the next word In its research post "Tracing the thoughts of a large language model" (March 27, 2025), Anthropic, the company behind Claude, says language models like Claude "aren't programmed directly by humans" and learn their own strategies to solve problems during training. The first stage is pretraining. Anthropic's glossary, checked on October 28, 2026, describes it as training on a large collection of raw text, and in Claude's case the model is "pretrained to predict the next word, given the previous context of text in the document." The same glossary says these pretrained models "are not inherently good at answering questions or following instructions." Fine-tuning trains the model further on additional data, and RLHF (reinforcement learning from human feedback) has people rank two or more example answers so the model learns to prefer outputs like the higher-ranked ones. ## Each answer is a choice among likely options, and temperature changes it Temperature controls how random a model's choices are, and Anthropic's glossary defines it as a parameter "that controls the randomness of a model's predictions during text generation." Lower temperatures give more conservative answers that stick to the most probable phrasing, and higher temperatures give more creative and diverse ones. Anthropic's glossary calls the context window the amount of text a model "can look back on and reference when generating new text," a "working memory" that is different from the data it was trained on. Our own analogy: training is everything a new hire learned before day one, and the context window is the policy binder you leave open on their desk. ## Language models sometimes guess, so your assistant needs limits In the paper "Why Language Models Hallucinate" (arXiv, submitted September 4, 2025), Kalai and co-authors write that models "sometimes guess when uncertain, producing plausible yet incorrect statements instead of admitting uncertainty." They argue that models guess because "the training and evaluation procedures reward guessing over acknowledging uncertainty." That is why MAGIA Solo sets your assistant to stay within what it knows about your business and hand you anything sensitive or outside its scope. ## NIST proposes four principles for explaining AI decisions For AI systems that are intended or required to be explainable, the US National Institute of Standards and Technology (NIST) proposes four principles in NISTIR 8312 (September 2021). - **Explanation:** the system delivers or contains evidence or reasons for its outputs or processes. - **Meaningful:** its explanations are understandable to the people they are meant for. - **Explanation accuracy:** the explanation correctly reflects the reason for generating the output. - **Knowledge limits:** the system "only operates under conditions for which it was designed and when it reaches sufficient confidence in its output." ## How do AI agents make decisions? In a loop of tools, results and checkpoints An AI agent decides in a loop, and OpenAI's "A practical guide to building agents" defines agents as "systems that independently accomplish tasks on your behalf." An agent uses an LLM (a large language model, the type of model Claude is based on) to manage the workflow and make decisions, and in case of failure "it can halt execution and transfer control back to the user." In Anthropic's documentation, Claude "determines when to call a tool based on the user's request and the tool's description," then asks for that tool, which your own software or Anthropic runs. The instructions you give the model (its system prompt) can steer when it calls a tool and when it answers directly. Anthropic's "Building effective agents" says agents need "ground truth" from the environment at each step, such as what each tool returns, to assess progress, and they "can then pause for human feedback at checkpoints or when encountering blockers." It also mentions stopping conditions, such as a maximum number of rounds, to maintain control. If you are weighing agents for a company, our guide on [how to choose the best autonomous AI agent for enterprises](/blog/best-autonomous-ai-agent-for-business) walks through five checks. ## A customer service agent decides better when you set its limits OpenAI's guide says clear instructions "reduce ambiguity and improve agent decision-making," and recommends building them from your existing operating procedures, support scripts or policy documents. It adds that a robust routine anticipates cases like a customer who gives incomplete information or asks an unexpected question. The same guide names two triggers for handing a case to a person. One is exceeding failure thresholds, for example when the agent fails to understand the customer's intent after multiple attempts. The other is high-risk actions that are "sensitive, irreversible, or have high stakes," which "should trigger human oversight until confidence in the agent's reliability grows," with examples such as canceling orders, authorizing large refunds or making payments. Our summary for an assistant that talks to your customers: rules written from your real prices and policies, answers only from what it knows, and a clear handoff to you for anything delicate. To see how that looks in a sales assistant, read [how to choose the best AI sales bot for WhatsApp](/blog/best-ai-sales-bot-whatsapp-business). ## MAGIA Solo sets the limits for your WhatsApp assistant in 15 days MAGIA Solo is our package for professionals and small businesses. In 15 days we hand you a system that answers, books and gets you paid: it handles WhatsApp day and night with your voice and your prices, learns from your real information, offers your slots, books them, sends the reminder and shares a payment link in the chat. We set the assistant up to stay within what it knows about your business and not invent prices or promise things you don't offer. When a query gets sensitive or falls outside its scope, it hands it to you instead of improvising. During the 6 months by your side, we adjust any answer you don't like. Code, data, domain and accounts are in your name from day one. MAGIA Solo is a one-time payment of 4,500 USD, with three training sessions and 6 months by your side included. Monthly operations (keeping the system online and the AI that answers) are paid at cost, directly to the providers and in your name, and if you want the system to stay hosted and operated by Catalizadora, that requires an active plan, from 99 USD a month. The MAGIA Solo page shows 500+ projects built, 100,000+ clients served by AI and systems running in 5 countries. See everything that is included and how the 15 days work on the [MAGIA Solo page](/en/magia/solo). ## Preguntas frecuentes ### How does AI make decisions? A language model like Claude is pretrained on large amounts of text to predict the next word. It is then refined with fine-tuning (more training on extra data) and RLHF (people ranking example answers), and each answer is built from what it learned plus the text it can look back on in that conversation (its context window). That is how Anthropic's glossary describes it, and Anthropic's research says models like Claude "aren't programmed directly by humans". ### How do AI agents make decisions? An agent uses a large language model to run a task step by step. Anthropic's tool use documentation says Claude decides when to call a tool based on the user's request and the tool's description, and Anthropic's "Building effective agents" says agents check the results at each step and can pause for human feedback. OpenAI's "A practical guide to building agents" adds that, in case of failure, an agent can halt and transfer control back to the user. ### How do you keep an AI customer service agent from making risky decisions? Give the agent clear instructions built from your procedures and policies, and have it hand risky cases to you. OpenAI's "A practical guide to building agents" says sensitive, irreversible or high-stakes actions, such as canceling orders, authorizing large refunds or making payments, should trigger human oversight until confidence in the agent's reliability grows, and that an agent that keeps failing to understand the customer should escalate to a human. --- Source: https://catalizadora.ai/blog/how-does-an-ai-make-decisions-explained-simply Author: Pablo Estrada — AI Catalysts, LLC (catalizadora.ai)