Agent: One sentence in, one file out
“Help me write a research proposal”—the same sentence dropped into a chat box usually gets you a block of text; dropped into an agent, it gets you a file you can open directly. The difference isn’t writing style; it’s whether it takes action. This article breaks down the agents in the MuchSci app collection separately: after receiving one sentence, what steps does it actually take, and what does it deliver?
The key to understanding it is separating “answering a question” from “completing a task.” The former is one-off: you get the answer, and everything else is up to you; the latter has intermediate states, tool calls, and an acceptance step, and ultimately has to land on something concrete. An agent is the latter.
01 It’s not the same kind of thing as a chat box
Start with the most direct comparison.
▲ Figure 1 | Both can speak human language; the difference is that one stops when it has said its piece, while the other stops only when the job is done
The chat box’s output is “text,” and whether it gets applied is up to the user; the agent’s output is “files,” and it completes the application step itself. The more substantive differences lie in two other points:it can touch real files—it can read the data you put in the workspace and write the draft you need;it leaves a trace—how the task was broken down, which tools were called, what was checked before the final draft, all can be expanded and viewed.
Only when these two points are combined does the word “delivery” hold. A block of text has no acceptance criteria; a file does: open it, flip through a couple of pages, look at the citations, look at the structure—quality is obvious at a glance. After writing so many tool tests, the criterion I use to judge whether an AI feature “really does work” is this one—whether it leaves behind something that can be checked.
02 In one task, the steps it completes on its own
If you break down one complete execution, it roughly consists of the following steps.
▲ Figure 2 | Only step 1 needs you; the remaining steps can run even when you’re not there
The key lies in steps 3 and 5.Looking at the results means it isn’t a one-shot process—if the retrieved literature isn’t enough, it will run another round; if the structure is missing a section, it will go back and fill it in.Self-check means that before the final draft, a step common in human work is fixed into the process: whether the structure is right, whether citations are in place, which formatting elements are missing. These two steps are the dividing line between “finished running” and “finished answering.”
This process can be seen directly in the interface. Below is the delivery record after the agent finished a research proposal:
▲ Figure 3 | Delivery record: final file, search basis, topic and structure, all in the same record
The table in the screenshot is its acceptance surface: main deliverable, preview file, how many rounds of search, how many items were added to the library, how the topic was narrowed down, and how many sections the report has.The process is not a black box but an attachment provided along with the delivery—I’ll mention this again later when discussing “process traceability.”
03 The deliverable is a package of things
Many people’s impression of AI output is still stuck at “a paragraph.” What an agent delivers is actually three things layered together.
▲ Figure 4 | Final product, basis, and process—only when the three are combined is it a complete delivery
The first layer is the final product. A Word file that can be opened and further edited—not a screenshot, not plain text, but something you can continue writing in after taking it over.The second layer is the basis. Where the literature cited in this draft comes from, how many rounds of search were run, how many items were added to the library, and whether the citation loop is closed—paper writers are most sensitive to this part, because whether the citations are real directly affects whether the draft can be used.The third layer is the process. How the task was broken down and which tools were called can still be opened and reviewed after writing is complete.
This is also why, in the MuchSci line, “delivery” and “output” are two different words. Output gives you a block of text; delivery gives you a package that can be checked.
04 When to hand it over, and when to do it yourself
An agent is not a master key. Match the task form to its capabilities, and it will be much smoother to use.
▲ Figure 5 | When they don’t match, it’s the user who feels awkward using it
If you want a line of thinking, an explanation, or a bit of inspiration, chat-style Q&A is actually lighter and faster—you don’t need to start a whole workflow for a three-line answer. Conversely, if you need a file that can be handed over directly, you should lay out the requirements clearly in one go: what the topic is, how many sections the structure should have, whether citations are needed, and what format to follow.The more specific you are, the less work acceptance will be, because its execution path is determined by your requirements.
A practical criterion: if after finishing this task you need to take a “thing” to meet someone—an advisor, collaborator, or reviewer—then it’s a job for an agent; if you just want to understand something yourself, there’s no need to use it.
05 Boundaries
As usual, let’s put the boundaries on the table.
① This article discusses the feature’s position, not a promise of results. What is broken down above is the agent’s role in task execution and its delivery method; it does not constitute a promise about the quality or efficiency of the final draft. Actual results depend on task complexity, literature scope, and how clearly the requirements are written.
② Being able to run the process does not mean judgment can be outsourced. Whether the structure is reasonable, whether the argument holds up, and whether a given piece of literature should be cited remain the writer’s own judgment. What the agent guarantees is “the process is completed,” not “the conclusion is correct.”
③ The permitted scope of AI participation in paper writing is subject to the current regulations of your school. The ultimate responsibility for the authenticity of literature and data always rests with the author.