{"slug": "whose-taste-is-shaping-your-agentic-sdlc", "title": "Whose taste is shaping your agentic SDLC?", "summary": "Agentic software development workflows risk eroding engineers' technical judgment as AI agents deliver changes with implementation, tests, and explanations already prepared, according to an analysis of how agents shape the software development life cycle. The piece argues that agents draw on existing repository patterns, so an initial unexamined design choice can harden into a convention, and that engineers who spend their time accepting or correcting proposals lose the opportunities to explore alternatives that build engineering taste. It recommends teams preserve room to question an approach, try another, and learn from how people use the result.", "body_md": "Engineers develop taste by building software, seeing how people use it, and living with the consequences of technical decisions. That experience shapes what they notice, which approaches they favor, and what they believe deserves to become part of a system.\n\nAgentic workflows put pressure on that judgment. A proposed change can arrive with its implementation, tests, and explanation already prepared, while the engineers responsible are still establishing what it does. As the queue grows, they have less time to examine the choices behind each implementation.\n\nAn accepted approach can also influence subsequent work. Agents draw on patterns already in the repository, so later changes may repeat a design the team gave little thought to when it first appeared. Over time, that choice can become a convention.\n\nAs more design choices arrive already implemented, engineers can spend more of their time accepting or correcting proposals. They get fewer opportunities to explore alternatives, explain their preferences, and see how their own choices work in practice. Over time, that weakens the habits through which engineering taste develops. Teams can preserve those opportunities by making room to question an approach, try another, and learn from how people use the result.\n\n## How implementation choices shape the product\n\nAgents make choices about scope, structure, and behavior as they implement, test, and revise software. A request leaves room for interpretation. Engineers inherit decisions about what the software should do alongside the code that makes it work.\n\nConsider an agent building a command-line tool for investigating failed deployments. It produces a detailed report with status summaries, configuration values, and logs. Everything is organized, and the requested information is present.\n\nAn engineer who regularly handles deployment failures tries it and imagines a different experience. The output would begin with what changed before the failure, connect possible causes to evidence, and provide a command to investigate each one. The full logs would remain available when needed.\n\nThat preference comes from remembering what it feels like to investigate a failure under pressure. The engineer has spent time scrolling through output, following misleading clues, and deciding which command to run next. Those experiences influence what they put first, what they leave available in the background, and how much explanation they give the user.\n\nAnother engineer might favor the comprehensive report because it makes unexpected details easier to discover. Discussing those choices brings their assumptions into view. Trying the designs during an investigation gives the team a way to judge them.\n\nTaste develops through that combination of experience, a point of view, and a willingness to see how an idea works in practice.\n\n## How an initial choice becomes a convention\n\nThe agent’s original report can become the foundation for further development before the team has considered those alternatives.\n\nAs the team adds fields and filters, the report becomes more detailed, and tests start to assume that format. If someone says the output is confusing, an agent might respond by adding an explanation, giving the engineer investigating the failure even more to read.\n\nEach addition looks reasonable in isolation, while the person investigating a failure has more information to interpret. The team could spend several iterations refining the report before someone asks whether the tool should organize the investigation differently.\n\nAn engineer’s taste can help them recognize that opportunity. They may see that the tool needs a clearer sequence, a more useful default, or a simpler way to move from evidence to action. Explaining that preference gives colleagues something to examine and improve.\n\nThis is one reason design discussions during code review are valuable. Engineers learn how others see the same problem. A colleague may recognize a maintenance cost they overlooked or suggest an unfamiliar approach that better serves the user. Over time, those exchanges broaden the experience each person brings to the next decision.\n\n## What engineers bring to an agent’s proposal\n\nModels can help engineers explore alternatives and articulate their tradeoffs. Given different priorities, the same model can make a reasonable case for a comprehensive report or a focused diagnostic next step.\n\nThe engineers closest to the work bring experience that helps them evaluate those proposals. Someone who has watched a confident diagnosis send an investigation in the wrong direction may want the tool to make uncertainty visible. Someone who has maintained a similar tool may recognize how quickly a growing collection of filters becomes difficult to use.\n\nThose experiences can lead to a distinctive design. When two causes remain plausible, the tool might present both with supporting evidence and a command that helps distinguish between them. The engineer is making a choice about how the software should help someone think through a failure.\n\nThat choice deserves scrutiny too. Experience can produce strong instincts, and a different setting may call for a different approach. Watching another engineer use the tool could reveal that the proposed sequence is confusing or that a detail moved into the background was essential.\n\nDeveloping taste involves making an informed choice and remaining curious about its consequences. The team learns through the gap between the experience it intended to create and the experience someone actually has.\n\n## Give engineering taste room to develop\n\nAs agents produce more changes, teams need to choose where to spend time on these discussions. Changes to how customers use the product, new abstractions, and decisions that are expensive to reverse are useful places to start.\n\nAsk engineers to explain what they noticed and which experience informed their recommendation. Make room for a colleague to suggest another approach. When possible, try the result with someone who will use it and bring what you learn back into the review.\n\nFor the deployment tool, that could mean having an engineer use it during a practice investigation. Watch where they hesitate, what they read first, and whether the suggested commands help them make progress. Those observations give the team more experience to draw on when choosing what to change.\n\nRecording the reasoning helps future contributors understand the design. Guidance and evaluation cases can preserve specific lessons, such as showing competing explanations when the evidence is inconclusive. Engineers still have to recognize when a new situation deserves a different choice.\n\n[CodeRabbit’s Agentic Change Management platform](https://www.coderabbit.ai/blog/introducing-agentic-change-management) supports these discussions through independent review, prioritization, and explanations of how changes affect the wider codebase. That context helps engineers examine the choices behind an implementation and direct their attention to work that needs their experience.\n\nIn your next design review, ask a colleague which part of the proposed change they would approach differently and what led them to that view. Follow the discussion through to how someone uses the software. Those exchanges give a team’s taste room to develop, and give future agent work a more considered example to follow.", "url": "https://wpnews.pro/news/whose-taste-is-shaping-your-agentic-sdlc", "canonical_source": "https://coderabbit.ai/blog/taste-decides-what-should-ship", "published_at": "2026-09-09 13:00:00+00:00", "updated_at": "2026-09-10 02:21:29.024229+00:00", "lang": "en", "topics": ["ai-agents", "ai-tools", "developer-tools", "ai-products"], "entities": [], "alternates": {"html": "https://wpnews.pro/news/whose-taste-is-shaping-your-agentic-sdlc", "markdown": "https://wpnews.pro/news/whose-taste-is-shaping-your-agentic-sdlc.md", "text": "https://wpnews.pro/news/whose-taste-is-shaping-your-agentic-sdlc.txt", "jsonld": "https://wpnews.pro/news/whose-taste-is-shaping-your-agentic-sdlc.jsonld"}}