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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →When a coding agent produces a mess, the model may not be the only thing to examine. Ashley Childress’s practical workflow guide argues that unclear tasks, scattered instructions, missing context, and weak validation can all contribute to disappointing results. Her advice is experience-based, not a controlled comparison proving that setup matters more than model capability.
Childress published “AI Isn’t Stupid. Your Setup Is. 🛠️” on DEV Community on May 2, 2026, and edited it May 7, 2026. Her concise summary: “The agent isn’t the problem—the setup is.” That is a useful prompt to inspect your process, not a universal verdict about every failed AI coding task.
1. Match the model to the task and specification
Childress’s starting point is to consider both task complexity and how clearly you can describe the work. A small, well-defined change may be a better fit for a less capable model; a tangled task may justify trying a more capable one. She uses Haiku, Sonnet, and Opus as examples, but her article does not provide benchmarks, a price comparison, or an assessment of current product availability. Treat those names and suitability judgments as her May 2026 examples, not a universal ranking.
Before switching models, ask whether the task is genuinely complex or simply underspecified. Specify what should change, what must remain unchanged, and how you will judge the result. If the task is risky or a mistake would be costly, plan for stronger review and validation regardless of model choice.
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2. Plan the work in chat before changing code
Use an initial conversation to make the request concrete before asking the agent to implement it. Childress recommends discussing the desired outcome, relevant technology choices, acceptance criteria, edge cases, and explicit non-goals. This can reveal assumptions while they are still easy to correct.
A useful planning exchange should establish:
- Outcome: What should a user or system be able to do afterward?
- Scope: Which files or behaviors may change, and what is out of bounds?
- Acceptance criteria: What observable results count as success?
- Cases to handle: What should happen for normal inputs, invalid inputs, failures, and boundary conditions?
- Constraints: Which stack choices, conventions, or compatibility requirements matter?
When the plan is ambiguous, clarify it before implementation rather than relying on the agent to infer your intent.
3. Keep project instructions coherent
Childress prefers a single AGENTS.md source of truth, with short references from tool-specific instruction files instead of multiple copies of the same rules. The practical aim is to reduce conflicting or stale guidance.
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This is a workflow preference, not a convention that applies to every coding agent. Check the instruction files and loading behavior supported by the particular tool or team. Whatever structure you choose, make clear which file is authoritative and avoid duplicating rules that will drift apart.
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4. Write instructions for how the agent actually uses them
Keep project rules concise, explicit, and non-duplicative. Instructions that are loaded repeatedly should prioritize actionable constraints over lengthy introductions aimed at a human reader. If a rule is essential, state it directly and preserve its meaning when revising the file.
For example, a rule such as “Run the relevant tests after changing application code” is easier to act on than a vague reminder to “be careful.” Put project-specific conventions where the agent will actually receive them, and remove obsolete guidance so it does not compete with current instructions.
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5. Explicitly invoke skills that are required
If a particular skill or procedure is essential to a task, Childress recommends naming it rather than assuming the agent will select it automatically. This is practical advice, not a claim that all agents discover or load skills in the same way. Follow the mechanism supported by your tool and confirm that the required capability is available before depending on it.
6. Limit MCP integrations to projects that need them
Childress argues against enabling integrations globally when a project does not use them. Unneeded integrations can add clutter and expose context the agent does not need. Scope them to the projects where they provide a concrete benefit, and review what information and actions each integration makes available.
Her article does not establish a measured token cost for a particular number of integrations. Decide based on relevance, context, access, and project needs rather than treating an illustrative count as a performance metric.
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7. Test the result—and validate it independently
Despite the punchy “Don’t review” framing in Childress’s headline, her practical advice is to test generated work repeatedly and to verify it outside the agent’s own loop. Automated checks are useful, but they do not transfer responsibility for judging whether a change is safe and correct.
Choose checks that fit the change and its risk:
- Unit and integration tests for the affected behavior.
- End-to-end checks for important user flows.
- Performance testing when speed or resource use could be affected.
- Accessibility checks for user-facing changes.
- Static analysis and security analysis where relevant.
Test the acceptance criteria, normal and negative cases, errors, and edge conditions. Then independently inspect the diff and run or verify the application in the way the project requires. A passing test suite is evidence about the cases it covers, not proof that every requirement has been met.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.8. Forbid shortcuts selectively
Childress describes banning quick fixes and temporary solutions in her personal projects. The goal is to prevent a superficially working change from leaving behind a known workaround or unfinished debt. If you use such a rule, make it specific enough that the agent understands what counts as a shortcut and what standard to meet instead.
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She also discusses backward compatibility, but explicitly says a blanket ban is harsh for live production code and should likely be removed there. Production systems may need compatibility to protect existing users and integrations. Make that decision according to the project’s risk and requirements rather than copying a personal-project rule unchanged.
9. Start a new chat when corrections stop helping
If repeated instructions are not moving the work forward, Childress recommends starting a fresh conversation with a clearer account of what has been learned. Carry over the relevant goal, constraints, current state, and failed approaches, rather than expecting the new chat to know what happened in the old one.
A reset is a troubleshooting tactic, not a guarantee. If the underlying task remains unclear, the code or tests are misleading, or the required capability is unavailable, a new conversation alone will not resolve that issue.
10. Adjust the setup to the work
Childress’s overall approach is to treat a coding-agent workflow as adjustable: choose tools and practices for the task, provide useful context, set clear constraints, and validate the result. Task clarity, complexity, project risk, the context the agent needs, the cost of mistakes, and the strength of independent checks all affect what a sensible setup looks like.
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Her article offers practical recommendations grounded in her experience, not experimental evidence that workflow changes reliably outperform choosing a different model. Use the advice as a way to diagnose and improve your own process while retaining responsibility for the code that ships.
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