Good Code, Wrong Feature: The Handoff Problem

Kadhir Mani

(3.8 minutes)

<audio url="https://api.productnow-prod.com/storage/getStorageObject?storageObjectId=24a75fda-9217-4323-94b6-27d355254add"></audio> Someone sends you a ticket: "Add the export feature—you know what we need." You don't, actually. The edge cases are missing. The error states are undefined. The success criteria exist only in someone's head. This is the default state of most implementation work, and it gets worse as AI coding agents enter the loop. A model that hallucinates the missing details with confidence can ship something that looks done and isn't. The real problem isn't just incomplete requirements, it's the absence of context. Good implementation requires two things working together: crisp, specific details on one side and enough surrounding judgment to make sound trade-offs on the other. The rest of this post unpacks both. <feedback data-feedbacknodeid="a3f7c2e1-84b5-4d91-b3c6-9e0d1f2a5c78" data-title="Where do implementations most often go wrong for you?" data-datatype="MULTIPLE_CHOICE" data-metadata="{"maxSelections":5,"options":["Missing or ambiguous requirements","Undocumented edge cases","No clarity on error states","Unclear success criteria","Context lost between PM and engineering","Spec changes mid-build","Other"],"type":"MULTIPLE_CHOICE"}"></feedback> <h2 id='377fd8cc-b4f5-484a-ae0c-f6b0e99d767e'>What engineers need to implement a feature</h2>Three things an engineer (or coding agent) needs to implement a feature successfully: <ul><li value="1">What you're building — A clear statement of the feature, who it serves, and the problem it solves. Every scope, priority, and trade-off decision traces back here.</li><li value="2">Edge cases & constraints — Error paths, permissions, performance limits, compatibility requirements, and feature-flag/rollout dependencies. These are the conditions that break implementations that otherwise look correct.</li><li value="3">Why you're building it — The business context: what outcome this drives, how it fits into the broader roadmap, and where it's expected to evolve. This is what lets engineers make the right call when the spec has gaps — and it always does.</li></ul> <callout icon="lightbulb" color="#3E63DD">Ambiguous context produces unreliable output — whether the implementer is human or an AI coding agent. Concrete specs and clear intent produce reliable ones.</callout> <h2 id='bb69741a-3ef3-4873-a142-5b3543827c7c'>Why context matters</h2>Requirements are never complete. They describe what to build but rarely explain why this approach, why now, or what comes next. When a builder understands those answers—customer reality, product intent, the business constraint driving the deadline—they can fill gaps with judgment instead of guessing or stalling. The result is fewer rework loops, fewer escalations, and decisions that survive the next sprint. Context also changes how tradeoffs get made. A builder who knows a feature is a short-term workaround will write code that's easy to delete. One who knows it's a foundation for a platform will instrument it, document it, and flag the coupling risks. Same ticket, very different outcome. Neither required a change to the spec—just an understanding of where the work sits in the larger picture. <mermaid data-height="400"> flowchart TD A["Ticket / Spec\n(What to build)"] --> B{Context provided?} B -- "Yes: why, tradeoffs,business intent" --> C["Builder makessound tradeoffs"] B -- "No: gaps inferred or ignored" --> D["Builder guesses or stalls"] C --> E["Feature ships as intended ✅ Fewer rework loops"] D --> F["Implementation drifts ❌ Wrong tradeoffs, legacy debt"] </mermaid> Strong ICs have always done this. They read the room: they ask what success looks like, who the customer is, what the team is worried about. It's the practical habit that separates implementations that age well from ones that become the next team's legacy debt. Whether the builder is a human or an AI coding agent, feeding it the surrounding context is one of the highest-leverage things a product team can do. <h2 id='c23d85f2-3b4a-427e-98b4-9bdc8ff8078c'>The breakdown</h2>When implementation begins from partial context, every handoff in the SDLC becomes a reconstruction exercise. Engineers and coding agents fill gaps with inferred intent — and each one fills them differently. Requirements lose edge-case nuance in design, design assumptions evaporate in code, and review or QA operates against a spec that no longer matches what was originally intended. The cost isn't one bad decision; it's a quiet tax on every decision downstream. The compounding risks are practical:<ul><li value="1">Drift — each handoff introduces small deviations that accumulate into a product that doesn't match the original intent</li><li value="2">Shallow execution — agents and engineers optimize for what's visible in the prompt, not what's implied by deeper product decisions made weeks earlier</li><li value="3">Inconsistent decisions — without shared context, two agents or two engineers working in parallel will resolve the same ambiguity in opposite ways, producing divergent behavior in the same codebase</li><li value="4">Team misalignment — weeks later, no one can reconstruct why a particular tradeoff was made, slowing every future iteration that touches the same area</li></ul> <feedback data-feedbacknodeid="b9e14c72-3f2a-4d68-a1c7-8f0e5d9b2a47" data-title="What's your team's most difficult handoff problem?" data-datatype="TEXT" data-metadata="{"type":"TEXT"}" data-description="Describe the biggest breakdown between product intent and what actually gets built on your team."></feedback> <h2 id='96171479-e93e-46f4-aa75-9ba2d0c958ed'>The ProductNow perspective</h2>The core failure mode in AI-native development isn't the code, it's the handoff. Customer context, OKRs, and the reasoning behind a requirement rarely survive the journey from discovery to implementation. That gap matters whether a human engineer or an AI agent is doing the building. ProductNow is built around that handoff: keeping the original intent, customer needs, business goals, and implementation-ready detail, intact from request to build, so whatever ships actually solves the right problem. And when that context changes, ProductNow pushes the update out to wherever it's needed. The spec, the ticket, and the agent working from them stay in sync. <feedback data-feedbacknodeid="c7ae509a-6cb6-45dc-8395-7aa908f0454a" data-title="Does this sound useful to you?" data-datatype="SINGLE_SELECTION" data-metadata="{"options":["👍","👎"],"type":"SINGLE_SELECTION"}"></feedback>

Published

May 19, 2026

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