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If you are thinking, “I’d like to build X, but I don’t know how to begin,” don’t start by choosing an AI agent. First define the outcome, what good work looks like, and how the task is handled today. Then decide which parts need model judgment, which belong in code or tools, and where people should check the results.
Why the work should come before the agent
An agent is an implementation choice, not a design in itself. A large prompt or a single “magic box” can hide several different responsibilities: interpreting a request, gathering information, making a decision, taking action, and checking the result. If those responsibilities are unclear, adding an agent may simply make the underlying uncertainty harder to see.
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Start with the work instead. Describe the result you need, the quality it must meet, and the steps a capable person would take to produce it. This gives you a basis for deciding where AI helps and where a fixed rule, a conventional tool, or a human decision is more appropriate.
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How to map a task before choosing an architecture
Work through these questions before deciding whether a task needs one model call, several stages, or an agentic workflow:
#1 Best Overall
- What outcome are you trying to create? Describe the finished result in terms a user or downstream process can recognize.
- What does good enough look like? Define the quality bar and any requirements the result must satisfy.
- How would a capable human do the work now? Capture the real process, including review, tools, and recurring corrections—not just the idealized version.
- What distinct jobs are involved? For each job, identify its responsibility, inputs, output, the next person or process that uses that output, and its own quality bar.
- How will you tell whether each job succeeded? Choose a way to inspect or evaluate the output at that stage, rather than relying only on whether the final result looks plausible.
- Which steps are stable, and which need judgment? Put repeatable rules and mechanical operations in code, structured data, or tools where appropriate. Use model reasoning for interpretation and other work that genuinely requires it.
- What state must persist, and how mature is the workflow? Identify information the process must retain between steps or runs, then consider how much reliability you have actually observed.
What makes a useful job boundary
A step is worth separating when it has a clear responsibility, defined inputs, a meaningful output, a consumer for that output, and a quality bar of its own. Naming a step alone does not make it a useful boundary. The split should help someone understand what the step is expected to do and determine whether it did that job well.
These boundaries also make evaluation and debugging more local. If the final result is weak, you can inspect whether the direction was unclear before judging the implementation. That is more informative than treating every failure as a problem with one opaque prompt or one undifferentiated agent.
Example: producing a website page
Levi Kovacs describes using finished marketing content, a design system, existing reference pages, and Claude Code to produce website pages. The first approach generated plausible pages but did not consistently meet the desired quality. The design system constrained implementation, but it did not decide how a particular message should be expressed visually.
Adding direction for individual sections helped, but another responsibility remained: selecting assets and deciding how they should communicate the message. That pointed to art direction as a distinct job before implementation. In Kovacs’s account, this boundary emerged through iteration rather than being settled correctly at the outset. It is a personal example, not a measured comparison or a guarantee that every website workflow needs the same stages.
Keep deterministic work out of model reasoning
Not every part of an AI workflow needs a model. Stable rules and mechanical operations are often easier to make predictable in code, structured data, or tools; reserve reasoning for choices that need interpretation.
For example, in an annotation workflow, a model might decide what should be pointed out. A mapping step can locate the relevant item, and deterministic geometry can draw the pointer. Separating those responsibilities makes it clearer whether a failure came from the judgment about what mattered, the mapping, or the drawing operation.
Improve the workflow through real work and observed failures
Rather than trying to design every capability in advance, use the actual task to reveal what is missing:
- Do the work using the current workflow.
- Notice where the result fails or a person repeatedly has to intervene.
- Identify the missing capability: it may be reasoning, a tool, a rule, stored knowledge, or verification.
- Make that capability explicit in the workflow.
- Try the work again and evaluate the relevant output against its quality bar.
This loop helps distinguish a need for more model reasoning from a need for better inputs, a deterministic operation, or a check that was missing altogether.
Best Value
Increase autonomy only when the evidence supports it
Autonomy is a maturity change to earn through evidence. Observe whether each job repeatedly meets its output and quality bar. A workflow can reduce routine review for stages that perform reliably while keeping human checks on stages—or final results—that still need them.
AI generating work while a person reviews everything and feedback is captured can be a legitimate production workflow. It is not a failed version of autonomy; it is a way to deliver work while building evidence about where review can safely change. As Levi Kovacs puts it, “Autonomy is earned with evidence.”
Source and limits
This practical framing comes from Levi Kovacs’s article for Mobiscroll, “Don’t Start With the Agent. Start With the Work”, published on DEV Community. The page shows an October 5 posting date but does not identify the year in its article header. It offers first-person guidance and a website-production example, not quantitative findings or a comparison of AI frameworks.
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