[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"guide-what-is-an-ai-agent::en":3,"guide-related-what-is-an-ai-agent::en":18},{"slug":4,"title":5,"excerpt":6,"body":7,"meta_title":8,"meta_description":9,"keywords":10,"category":16,"published_at":17,"updated_at":17},"what-is-an-ai-agent","What Is an AI Agent? And When You Actually Need One","An AI agent decides its own next step instead of following a script. This guide covers what that means mechanically, the costs it introduces, and the honest test for whether your problem needs one.","\u003Ch2>The one-sentence definition\u003C\u002Fh2>\n\u003Cp>An AI agent is a language model placed in a loop where it chooses its own next action. Give it a goal and a set of tools — search the web, query a database, send a request, read a file — and it decides which tool to use, sees the result, and decides again, continuing until it judges the goal met or a limit stops it. The difference from ordinary AI usage is not intelligence but control flow: in a normal feature you decide the steps, and in an agent the model does.\u003C\u002Fp>\n\u003Ch2>What the loop actually looks like\u003C\u002Fh2>\n\u003Cp>Mechanically it is unglamorous. Your code sends the model the goal, the conversation so far, and a description of the available tools. The model replies either with a final answer or with a request to call a tool and some arguments. Your code executes that call, appends the result to the conversation, and sends it back. Repeat. Everything people call agentic — planning, reflection, self-correction, multi-agent delegation — is a variation on what you put in that loop and what you let the model call.\u003C\u002Fp>\n\u003Ch2>Why the loop is the whole problem\u003C\u002Fh2>\n\u003Cp>Handing control to the model buys flexibility and costs predictability. A scripted pipeline with five steps runs five steps. An agent with the same tools might take three steps or forty, cost ten cents or nine dollars, and take two seconds or four minutes — on the same input, on two different days. It can also loop: two agents politely deferring to each other, or one retrying a failing call indefinitely because retrying seemed reasonable. This is why every serious agent implementation has hard caps on steps, tokens, time, and spend, and why those caps are not optional polish.\u003C\u002Fp>\n\u003Ch2>Where agents genuinely earn their keep\u003C\u002Fh2>\n\u003Cp>Agents pay off when the number and order of steps cannot be known in advance. Investigating a support ticket where the next question depends on the last answer. Researching a topic where you cannot say beforehand which sources will matter. Working through a codebase where the fix location is unknown. In each case a script would need a branch for every eventuality, and enumerating them is harder than letting the model decide.\u003C\u002Fp>\n\u003Ch2>Where they are the wrong tool\u003C\u002Fh2>\n\u003Cp>If you can write the steps down, write them down. A pipeline that extracts fields from an invoice, validates them, and writes a row is cheaper, faster, testable, debuggable, and identical on every run. Wrapping that in an agent adds cost and variance and removes your ability to reason about what happened. The most common expensive mistake in this space is an agent doing a job a deterministic pipeline with one model call in the middle would have done better.\u003C\u002Fp>\n\u003Ch2>The things that go wrong in production\u003C\u002Fh2>\n\u003Cp>Three failures recur. Context growth: every tool result is appended, so a long run gets slower and more expensive with each step, and important early instructions drift toward the middle of the window where models attend to them least. Compounding error: a wrong step is not corrected, it is built upon. And silent tool failure: an API returns an empty result, the model treats absence as an answer, and reports something confident and wrong. All three are why observability — logging every step, every tool call, every result — is the first thing to build, not the last.\u003C\u002Fp>\n\u003Ch2>Deciding whether you need one\u003C\u002Fh2>\n\u003Cp>Two questions settle it. Can you enumerate the steps? If yes, build a pipeline. If no, can you tolerate a variable number of model calls, a variable bill, and a variable answer? If also no, then the honest conclusion is that the problem is not ready for an agent, and narrowing the scope until the steps are enumerable is the cheaper path.\u003C\u002Fp>\n\u003Ch2>A reasonable starting point\u003C\u002Fh2>\n\u003Cp>Begin with the smallest agent that could work: one model, three or four tools, a hard step limit, full logging, and a human reviewing the output before it acts on anything. Add autonomy only where the logs show it is warranted. Almost every team that starts at the other end — many agents, many tools, no limits — spends its first month removing capability rather than adding it.\u003C\u002Fp>","What Is an AI Agent?","What an AI agent is: a model that chooses its own next action in a loop. How agents work, what they cost, and when a plain scripted pipeline is the better answer.",[11,12,13,14,15],"ai agent","agentic ai","agent loop","tool use","ai automation","fundamentals","2026-08-07T03:45:02+00:00",[19,24,28,32,37,42],{"slug":20,"title":21,"excerpt":22,"updated_at":23},"ai-tool-pricing-models-seat-vs-usage-vs-credits","AI Tool Pricing Models: Seat-Based vs Usage-Based vs Credits","The three common ways AI tools charge — per seat, per usage, and by credits — and how to reason about which one will actually be cheaper for the way your team works.","2026-08-05T14:32:26+00:00",{"slug":25,"title":26,"excerpt":27,"updated_at":23},"how-ai-image-generators-differ-diffusion-vs-the-rest","How AI Image Generators Differ: Diffusion vs the Rest, in Plain Terms","A non-technical explanation of how AI image generators work, why the diffusion approach became dominant, and what practical differences to expect between tools.",{"slug":29,"title":30,"excerpt":31,"updated_at":23},"how-to-automate-your-workflow-without-code","How to Automate Your Workflow Without Code","A practical sequence for building automations that survive: picking the right process, mapping it before touching a tool, and handling the failure cases that break most first attempts.",{"slug":33,"title":34,"excerpt":35,"updated_at":36},"how-to-build-a-chatbot-without-coding","How to Build a Chatbot Without Coding","A practical route to a working chatbot using no-code tools: deciding scope, connecting your own content, handling the questions it cannot answer, and knowing what it will cost.","2026-08-05T14:32:27+00:00",{"slug":38,"title":39,"excerpt":40,"updated_at":41},"how-to-change-a-prompt-without-breaking-production","How to Change a Prompt Without Breaking Production","Prompts get edited in a text box and shipped in seconds, which is why they break things quietly: no compiler, no stack trace, no obvious moment of failure. Give them the release discipline code gets.","2026-08-24T03:30:02+00:00",{"slug":43,"title":44,"excerpt":45,"updated_at":23},"how-to-choose-an-ai-writing-assistant","How to Choose an AI Writing Assistant","A practical framework for picking an AI writing tool — matching it to the kind of writing you actually do, checking editing controls, and avoiding tools that produce confident but generic copy."]