Detailed specification matters more, not less, as AI coding agents make implementation cheap and fast. The real cost of a vague prompt shows up later in endless correction loops, while a well-reviewed spec front-loads that cost into something executable and repeatable. Specs themselves need validation before implementation starts, multi-agent pipelines need contract-level rigor between agent handoffs, and specs should shrink over time to avoid context rot and conflicting sources of truth. Well-designed, discoverable APIs let agents treat code itself as a reliable spec. Agile and XP practices like short feedback loops, test-first development, continuous integration, and small releases remain valuable, while status-meeting ceremony fades in relevance.
Table of contents
The bottleneck moved, not disappearedWriting the spec is not enoughWhy multi-agent systems need stronger contractsA spec should have an expiration dateAPIs can make code behave like specWhere to investWhat survives from Agile and XPThe real leverageQuestions this post answers
Why does giving an AI coding agent a vague prompt end up costing more time than writing a detailed spec first?
A vague prompt starts implementation quickly but shifts the cost into repeated correction loops: reviewing output, clarifying intent, requesting changes, and rerunning tests. A human becomes the sole oracle deciding whether results match intent. A detailed spec with acceptance criteria or executable tests front-loads that cost, since a test checks the same condition every time without getting tired or rushed. Teams weighing spec-first versus prompt-and-fix workflows can track these agentic development debates on daily.dev.
Why do multi-agent AI coding pipelines need stronger contracts between agents than a single agent working alone?
Once one agent's output becomes another agent's input, interpretive drift compounds because the receiving agent treats prior output as ground truth without knowing it may contain misunderstandings. A single agent on a bounded task can be steered back by a human, but in multi-agent chains the original mistake can be buried under layers of competent-looking work, so schemas, invariants, validation rules, and contract tests become necessary. Anyone designing multi-agent coding pipelines can follow this contract-testing discussion on daily.dev.
Why can adding more detailed documentation and design notes to an AI coding agent's context actually make its output worse?
Model performance becomes less reliable as input grows, a phenomenon Chroma's research calls context rot, and in coding projects a second issue compounds it: stuffing old design prose, stale tickets, and outdated software design documents into context makes it unclear which parts are active instructions versus historical artifacts, causing the model to average across competing sources of truth. Developers managing long-lived AI coding context windows can track context-rot findings like this on daily.dev.