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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallIf you are thinking, “I’d like to build this with AI, but I don’t know where to begin,” start by describing the work and the result you need—not by choosing an agent architecture. An agent is one possible implementation. Clear outcomes, responsibilities, quality checks, and evidence of reliability should determine whether you need one.
Define the outcome before choosing the architecture
Begin with the result the system must deliver and the standard it must meet. “Generate a web page” is too broad if success also depends on communicating a particular message, choosing suitable imagery, following a design system, and producing code that works.
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Write down what a good result looks like in terms a person can judge. Then describe how a capable human currently produces it: what information they consult, what decisions they make, what tools they use, and where they check or revise the work. This exposes the real responsibilities that a vague request—or a large prompt—can hide.
Break the work into jobs with clear boundaries
A useful job has a specific responsibility, defined inputs, a meaningful output, a recipient for that output, and its own quality bar. For example, a content-to-page workflow might involve separate jobs for preparing the communication direction, selecting or specifying visual assets, implementing the page within a design system, and checking the finished result.
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Do not split work merely to create more agents. Make a boundary explicit when it helps clarify what must be done, what information it needs, or how its result will be judged. Each boundary should make it easier to locate a failure: if the implementation is polished but communicates the wrong idea, you can inspect the direction that preceded it rather than treating the entire workflow as one opaque failure.
Use the website example to see why boundaries emerge
Levi Kovacs describes using finished marketing content, a design system, existing reference pages, and Claude Code to create website pages. In his account, the initial approach could produce plausible pages but did not consistently reach the desired quality. A design system constrained how the page was implemented, but it did not decide how a particular message should be expressed visually.
Adding direction at the section level helped, but the work also required decisions about which assets to use and how those assets should communicate. That pointed to a distinct art-direction responsibility before implementation. Kovacs says this boundary became clear through iteration, rather than being correctly specified at the start. It is a practical example, not a measured comparison or proof that every website workflow needs the same stages.
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Use code, structured data, or tools for stable rules and mechanical operations where they fit. Reserve model reasoning for tasks that actually require interpretation or judgment. This keeps a model from being asked to repeatedly improvise steps that can be made consistent.
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Consider an annotation workflow. A model may need to decide what in an image deserves attention. Once that choice is made, a mapping step can locate the relevant point, and deterministic geometry can draw the pointer. The interpretive decision and the mechanical drawing are different jobs; they do not need to be hidden inside one all-purpose instruction.
Evaluate each job where it happens
For every job, ask what evidence would show that it did its part well. Check the quality of an output against that job’s own bar before using it to judge the next stage. In a page workflow, for instance, review the communication direction before concluding that the implementation stage failed. This makes both evaluation and debugging more local.
When a workflow fails, identify the missing capability rather than automatically adding another agent. The gap may be better reasoning, a tool, an explicit rule, stored knowledge, or a verification step. A useful iteration loop is to do the real work, notice where it fails or repeatedly needs human intervention, make that missing capability explicit, and try again.
Increase autonomy only when the work earns it
“Autonomy is earned with evidence,” Kovacs writes. Observe whether each job repeatedly meets its output requirements and quality bar before reducing routine review. Reliability at one stage does not establish reliability at every stage, and some checks may remain important for the final result even when intermediate work needs less oversight.
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A production workflow in which AI generates work, a person reviews it, and feedback is captured can be a legitimate operating model. The goal is not to remove human involvement by default; it is to place review where evidence and the consequences of mistakes say it is useful.
A practical design checklist
- Outcome: What result must the system create?
- Quality: What does good enough mean, and who can judge it?
- Current work: How does a capable person complete the task today?
- Jobs: What distinct responsibilities exist, and what inputs, outputs, and consumers belong to each?
- Evaluation: What check demonstrates that each job met its own quality bar?
- Mechanism: Which parts are stable rules or mechanical operations, and which need reasoning?
- State and oversight: What information must persist, how reliable is the workflow so far, and where is human review still needed?
These questions help determine where reasoning, tools, deterministic steps, verification, and orchestration belong. Only after the work is clear can you decide whether an agent is useful—or whether a simpler, more explicit workflow is enough.
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