We explore the core operating principles and structure of 'AI Agents' that go beyond simple answers to autonomously formulate plans and use tools to complete complex tasks.
Imagine this. It’s early morning, you’re brewing a warm cup of coffee, and you casually say to the AI on your computer screen: “Please write a draft for the new product marketing plan we discussed in last week’s planning meeting. Find the related meeting minutes file in a folder on my desktop, and search the internet for the latest data to supplement any missing market statistics.”
What would have happened with a standard conversational AI from the past? Nine times out of ten, it would have continuously demanded the next action and instructions from the user, saying, “Please upload the file directly to me,” or “Please tell me the specific keywords one by one for the statistical data you want.” It was merely a passive tool that couldn’t do anything unless we held its hand and guided it every step of the way.
However, the recent trend in software and artificial intelligence development has entered a completely new phase. Moving beyond the form of simply answering user questions with plausible sentences, an incredible system has emerged that autonomously understands large, complex missions given by the user, and uses tools to complete them to the end How to Build AI Agents from Scratch in 2025. We call this self-acting, independent artificial intelligence an ‘AI Agent’. Simply put, it has evolved from a passive robot waiting for commands into a smart assistant that autonomously finds and completes tasks.
Today at MindTickleBytes, we will easily explore the principles of ‘Long Task Planning’, which is the secret behind how these smart AI agents avoid getting lost in the middle of processing complex and tedious tasks, and how developers build these amazing systems from scratch. Please set aside complex coding knowledge for a moment, and follow along comfortably, as if listening to a story over a cup of coffee with a smart friend.
Why It Matters
Until very recently, the popular center of AI technology remained in the structure of conversational chatbots that “answer with text when asked a question.” However, the tech industry has been paying attention to this massive shift, calling 2025 the inaugural year of so-called ‘Agentic AI’ The Agent Execution Loop: How to Build an AI Agent From Scratch. Currently widely used tools such as the Google Gemini CLI, Claude Code, GitHub Copilot agent mode, and the development tool Cursor all take the form of these ‘agents’.
So, what exactly is an agent, and why is it fundamentally different from traditional AI? Simply put, an agent is an autonomous system equipped with a Large Language Model (LLM, the brain of the AI that learns from vast amounts of text to understand context and generate sentences like a human) that judges and acts on its own.
They are endowed with three core and powerful abilities that traditional chatbots lacked Build an AI Agent From Scratch in 2026 (Python Tutorial ….
- Perceive: They proactively gather information on their own from user commands, application programming interfaces (APIs, the communication channels for exchanging data between programs), or massive external databases.
- Reason: They break down overwhelmingly large and daunting problems into several manageable small steps to logically find a solution on their own.
- Act: Going beyond simply writing text, they take physical and virtual actions by utilizing various tools such as mouse clicks, file searches, and internet browsing to solve the given problem.
What does this change mean for our ordinary daily lives and work? It goes beyond abstract concepts like “writing documents faster” or “increasing productivity”; it signifies a fundamental shift in the very way humans work. For instance, just by giving it a topic for an article, methods for developing AI agent programs that autonomously scour the internet for related materials, establish the overall structure of the text, and write a completed blog post from start to finish all by themselves have already been published on the internet and are widely utilized Build AI Agents from Scratch — Complete Guide - LinkedIn.
Moving beyond a tiresome tool that I have to instruct and coddle at every moment, “my very own tireless digital intern” that thinks for itself and brings back fully completed results has been born. This is exactly why we need to understand the inner principles of how they comprehend the world and solve tasks step by step.
The Explainer
Now, the most fundamental curiosity arises. Even humans easily lose their way when handling complex and long tasks, wondering, “Where was I?” or “What should I do next?” When attention is scattered, one might even fall into tasks completely unrelated to the original intention. How, then, can AI, which is software, manage to complete complex and exhausting tasks involving dozens of steps without forgetting or giving up halfway?
That core secret weapon is the very subject of today’s story: the technology called ‘Long Task Planning’.
| When engineers first build an AI agent, they explain in great detail and clarity how to use this long task planning within the model’s ‘system prompt’ (the fundamental personality, rules, and operational guidelines bestowed upon the AI) [Build A Basic AI Agent From Scratch: Long Task Planning | by Roger Oriol | Jun, 2026 | Medium](https://medium.com/@rogi23696/build-a-basic-ai-agent-from-scratch-long-task-planning-14e803f9bd6d). While the operating principle of this feature itself is incredibly intuitive and simple, its results are unimaginably powerful. |
Fundamentally, we are providing the AI model with a virtual memo space where it can jot down its thoughts and current task status with a pen, and read them again later after time has passed and other tasks have been completed.
Having this memo space brings a tremendous advantage. It prevents the AI model from recklessly starting to write code or text as soon as it hears the user’s command, and ‘forces’ it to think deeply through to the final goal, then meticulously design and plan the overall approach before beginning the actual work Build A Basic AI Agent From Scratch: Long Task Planning.
To use an analogy, it’s like this. A beginner who has just started learning to cook (traditional chatbot AI) looks at a recipe, says “It says to chop onions first,” and immediately chops the onions. Only when reading the next line, “Stir-fry carrots and meat together over high heat,” do they frantically open the refrigerator door to look for carrots. In the meantime, the frying pan left on the fire ends up burning. In contrast, a veteran chef with decades of experience (AI Agent) perfectly simulates the entire recipe in their head before starting to cook. Only after prepping all the necessary ingredients and arranging them neatly in order on the cutting board do they finally turn on the stove. The AI’s long task planning feature is perfectly identical to this veteran chef’s thorough preparation process.
| Developers commonly call the memo space the AI uses during this planning process a ‘Scratchpad’ (a notepad or scrap paper for scribbling). This scratchpad tool doesn’t heavily store the work details in permanent files on a hard disk or a massive database, but lightly jots them down only in temporary memory. This is because there is absolutely no need to share and carry over the detailed memo plan of the current task being processed with one user to a completely new conversation session with another user the next day [Build A Basic AI Agent From Scratch: Long Task Planning | by Roger Oriol | Jun, 2026 | Medium](https://medium.com/@rogi23696/build-a-basic-ai-agent-from-scratch-long-task-planning-14e803f9bd6d). It’s like tearing up the practice paper once the current assignment is finished. |
The method of making plans itself is largely divided into two branches. The first is ‘Implicit Planning’. This is a method where the model deduces by stepping through logical stages on its own within its ‘Context Window’—the range of text it can read and remember at once—just as a person would ponder deeply in their mind. The second is ‘Explicit Planning’. This method pulls what is only thought of in the head out into the open, actually generating a highly structured and clear schedule in text, and then executing it step by step Building an AI Agent from Scratch: A Step-by-Step Developer Guide (2026) - Blog | TechPaathshala. The more complex the task, the more this explicit planning shines.
The method most widely adopted by developers in the field is a simple tool utilizing this explicit planning. This tool breaks down a massive and daunting user request into several manageable sub-‘Tasks’. It then meticulously records this entire task list within the AI’s conversation context Build an AI Agent (From Scratch) - manning.com. Next to each task item, its current progress status is displayed like a tag.
For example, it attaches labels such as ‘Pending’ for a task like “1. Search for 2025 population data from the National Statistical Office” because it hasn’t been touched yet, ‘In Progress’ for “2. Organize the searched data into Excel” since it just started, and ‘Completed’ for “3. Generate summary report file” because it has already finished.
Every moment the AI finishes one sub-task and needs to decide what on earth to do next, it looks back at this giant sticky-note-like schedule to grasp where it stands in the overall journey Build an AI Agent From Scratch in 2026 (Python Tutorial …. Anyone who has used a smartphone ‘To-do’ app and felt the pleasure of checking things off one by one can intuitively understand this process.
The Magic Created by Simplicity: The Agent Execution Loop
So, what does the engine that looks at these sticky notes and makes actual actions happen look like? The core engine that enables all these intelligent miracles has its secret in the ‘Agent Execution Loop’, which is surprisingly simple enough in its framework to be summarized in just a few lines of code.
A ‘Loop’ means going round and round like a hamster wheel. Given an open-ended, boundless task where the answer isn’t clear-cut, the agent makes the aforementioned plan, takes action, verifies the results of that action and reflects, continuously repeating this process round and round until the final mission is accomplished The Agent Execution Loop: How to Build an AI Agent From Scratch.
Let’s look at this by comparing it to the process of the previously mentioned chef flavoring a broth:
- The chef receives the goal of “making a delicious broth.” (State where the task is not yet finished)
- They take a little taste of the broth. (Checking and perceiving the current state)
- They judge, “Hmm, it lacks saltiness, so it needs more salt,” and add a pinch of salt. (Using tools and acting)
- They taste it again to check if the seasoning is right. (Verifying the results of the action and reflecting)
- They endlessly repeat steps 1 through 4 until it tastes perfect.
The logic of the development code that actually operates the AI is also perfectly identical to this chef’s behavior How to Build an AI Agent from Scratch: A Step-by-Step Guide | Claude Code Playbooks Blog.
- Is the task not done yet? (
while not done): If there are still plans left to complete, it keeps making the AI model think about the next step. - Is a tool needed? (
if response.has_tool_call): If the AI looks at the memo on the scratchpad and responds, “I need to find missing data now, so I must use the internet search tool,” it clicks and executes the previously connected search tool. - Reporting the results (
messages.append(result)): It quietly inserts the useful information scraped from the internet back into the AI’s conversation history, helping the AI read and judge it with its own eyes. - Declaring completion (
done = True): If there are no more tools needed and all items on the schedule have been crossed off as ‘Completed’, it proudly submits the final result to the user, declaring “All tasks are finished!”
| In fact, the complex memory systems or planning abilities we view with marvel, and even the ‘multi-agent orchestration’ systems where several AIs cooperate as if brainstorming in a real company conference room, are all merely variations dressing up this fundamental loop pattern in fancy clothes [How to Build an AI Agent from Scratch: A Step-by-Step Guide | Claude Code Playbooks Blog](https://www.claudecodehq.com/blog/how-to-build-an-ai-agent-from-scratch). Behind the seemingly complex technology breathes such transparent and concise logic. |
Where We Stand
With advanced technology possessing such tremendous logical power and meticulous planning ability, it seems like it would take dozens of genius Silicon Valley engineers working day and night just to barely create it, but the pace of technological advancement far exceeds our assumptions. Currently, this technology is walking the path of popularization at a frightening speed.
Surprisingly, for anyone with a bit of basic knowledge about software development, it takes a mere 2 to 3 days, spanning a weekend, to build their own Basic Agent like this from scratch starting from a blank screen. Of course, to meticulously catch errors and solidly refine it enough to be deployed in a company’s real business environment—beyond simply being a toy used alone—requires about 2 to 4 weeks of time and persistence Build an AI Agent From Scratch in 2026 (Python Tutorial …. Friendly guides on how to build an AI agent step by step from a completely blank slate using widely used programming languages like Python already overflow on the internet How to Build an AI Agent From Scratch With Python in 2025 ….
Developers in the field do not build a massive castle all at once when first designing an AI agent. They start from what is called the ‘smallest useful loop’ and gradually flesh it out, almost like assembling toy Lego blocks. Initially, they set up a very simple framework that just lightly exchanges text with the computer, and then progressively hand it useful tools one by one. Next, they introduce a ‘Plugin’ structure that can be easily slotted in and out when new features are desired. After that, they attach technology that scours through countless past documents to pinpoint and extract only the necessary information, bestowing it with a solid memory. Finally, they complete it by layering on routing capabilities to smartly distribute various tasks alongside today’s core topic, the ‘Planning’ system Building an AI Agent from Scratch: The Smallest Useful Loop.
Here, the role of the ‘Memory’ device, which helps the agent tenaciously maintain its state without losing the flow of conversation with the user, is exceedingly important. Memory is broadly divided into two types and systematically managed Building an AI Agent from Scratch: A Step-by-Step Developer Guide (2026) - Blog | TechPaathshala.
- Short-term memory: In human terms, it’s like the working memory of memorizing a phone number for a brief moment. It stores the context of the current conversation and the schedule just created within the ‘context window’, which is the field of vision the artificial intelligence can see and process at once. This memory cleanly vanishes when the conversation ends or the system is shut down.
- Long-term memory: It is like a massive library full of accumulated knowledge. It’s a technology that permanently preserves the meaning of texts by converting them into numbers in a special external database space (such as a Vector database) so that even if the agent starts a completely new conversation session a few days or a month later, it can remember the conversations we had in the past.
And out of all these components, the most dramatic and dazzling magic emanates precisely from the ‘utilization of tools’. Through coding, we can attach physical limbs that can interact with the real world to the AI that was merely trapped in a text box on the screen. It can autonomously find Excel files in specific folders within your computer, visually read the complex numbers inside, revise the content on its own, save it again, and even boldly execute commands that control the computer system itself. We can even connect tools that open a web browser, just as we do every day, to scrape the latest news or stock data from the internet space. By giving it just these essential four or five tools, you will face an exceedingly capable and marvelous agent right before your eyes that completes days’ worth of all-nighters in an instant and autonomously presents the results Build A Basic AI Agent From Scratch: Long Task Planning.
What’s Next
Where will our lives and technology head in the future? The intelligence of the AI model itself—the heart that acts as the agent’s brain—is now also evolving by leaps and bounds day by day, to the point where it’s tough for humans to catch up.
| Just a year ago, developers had to fret and constantly remind the artificial intelligence of its schedule or throw nagging warnings to prevent it from going off track or doing something bizarre. However, the excellent state-of-art models that have emerged in the world today exhibit a terrifying concentration, advancing steadily and accurately toward their goal step by step without any wavering or hesitation, even when just tossed a crudely written text schedule [Build a Basic AI Agent from Scratch: Long Task Planning | Hacker News](https://news.ycombinator.com/item?id=48461635). |
| Especially for complex tasks that require reading and understanding vast documents equivalent to dozens of thick books all at once, or that demand extremely deep logical reasoning like advanced mathematical proofs, Large Language Models boasting world-class performance (such as Claude Opus, Gemini Advanced, etc.) are reliably deployed as the agent’s brain, serving as mighty problem-solvers that untangle any conundrum [How to Build an AI Agent from Scratch: A Step-by-Step Guide | Claude Code Playbooks Blog](https://www.claudecodehq.com/blog/how-to-build-an-ai-agent-from-scratch). |
From a long-term perspective, having such a smart personal assistant by your side is no longer the exclusive property of a few experts typing lines of English code onto an incomprehensible black screen. ‘No-code’ platforms are springing up everywhere, significantly lowering the barrier to entry by allowing users to intuitively build systems in a snap—much like dragging and dropping pretty shapes with a mouse in PowerPoint—without having to write a single line of code themselves. Perfect, user-friendly guides are already widespread across the internet, helping ordinary people with zero knowledge of programming to acquire customized AI agent assistants that perceive their work environment, reason, and take action How to Build an AI Agent? A Complete Step-by-Step Guide.
In the not-so-distant future, a magical era will fully unfold where, instead of studying complex computer languages, anyone can easily produce the explosive results of a one-person enterprise by leading dozens of personal agent interns specialized in their respective fields of expertise, relying solely on the planning ability to set clear ‘goals’ for the artificial intelligence.
AI’s Take
A Perspective from MindTickleBytes’ AI Reporter: When facing complex, massive projects that are overwhelming for humans to handle, what is the best way to overcome the fear? It is to open a diary, jot down pieces of a ‘To-do List’, and move forward step by step by crossing them out one by one with a highlighter. Surprisingly, cutting-edge AI, which is said to be acquiring advanced intelligence, is also finally achieving perfect independence free from human intervention as it learns how to grasp its own small virtual notepad and cross off plans by itself.
Perhaps the essence of technology has always been a mirror of humanity. After all, it is incredibly fascinating and somewhat strangely relieving that the ultimate destination of the most cutting-edge, advanced software technology is not some complex alien mathematical formula, but simply evolving to elaborately mimic humanity’s most universal and simplest pattern of silently working and thinking from a very long time ago—”Plan, Execute, and Reflect.” What kind of schedule is lying on your desk today? Just like you, artificial intelligence is also planning its next step to change the world on a small notepad.
References
-
[Build A Basic AI Agent From Scratch: Long Task Planning by Roger Oriol Jun, 2026 Medium](https://medium.com/@rogi23696/build-a-basic-ai-agent-from-scratch-long-task-planning-14e803f9bd6d) -
[Build a Basic AI Agent from Scratch: Long Task Planning Hacker News](https://news.ycombinator.com/item?id=48461635) - Build A Basic AI Agent From Scratch: Long Task Planning
- Building an AI Agent from Scratch: The Smallest Useful Loop
-
[Building an AI Agent from Scratch: A Step-by-Step Developer Guide (2026) - Blog TechPaathshala](https://techpaathshala.com/blog/building-an-ai-agent-from-scratch-a-step-by-step-developer-guide-2026/) -
[How to Build an AI Agent from Scratch: A Step-by-Step Guide Claude Code Playbooks Blog](https://www.claudecodehq.com/blog/how-to-build-an-ai-agent-from-scratch) - Build A Basic AI Agent From Scratch: Long Task Planning
- Build AI Agents from Scratch — Complete Guide - LinkedIn
- Build an AI Agent (From Scratch) - manning.com
- Build an AI Agent From Scratch in 2026 (Python Tutorial …
- How to Build AI Agents from Scratch in 2025
- The Agent Execution Loop: How to Build an AI Agent From Scratch
- How to Build an AI Agent from Scratch: Complete Developer …
- How to Build an AI Agent? A Complete Step-by-Step Guide
- How to Build an AI Agent From Scratch With Python in 2025 …
- Permanent Database
- Scratchpad
- Web Browser
- Implicit Planning
- Short-term Memory
- Explicit Planning
- Short-term Context Window
- Scratchpad Memory
- Long-term Memory and Vector Database