Good code, Wrong feature: The handoff problem

Kadhir Mani argues that incomplete requirements and missing context cause feature implementations to fail, a problem exacerbated by AI coding agents that confidently hallucinate missing details. Engineers need clear specifications, edge cases, and business context to build reliable features, whether human or AI.

Good Code, Wrong Feature: The Handoff Problem by Kadhir Mani 3.8 minutes <section data-section-id='1f07a485-101e-44fe-a150-a52d3071ab21' <audio url="https://api.productnow-prod.com/storage/getStorageObject?storageObjectId=24a75fda-9217-4323-94b6-27d355254add" </audio </section <section data-section-id='ffadd3ae-7cf4-4362-9d80-71ab5ed4cb24' <p <br </p <p <span style="white-space: pre-wrap;" Someone sends you a ticket: </span <i <em style="white-space: pre-wrap;" "Add the export feature—you know what we need."</em </i <span style="white-space: pre-wrap;" You don't, actually. The edge cases are missing. The error states are undefined. The success criteria exist only in someone's head.</span </p <p <br </p <p <span style="white-space: pre-wrap;" 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.</span </p <p <br </p <p <span style="white-space: pre-wrap;" 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.</span </p <p <br </p <p <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 </p <p <br </p </section <section data-section-id='377fd8cc-b4f5-484a-ae0c-f6b0e99d767e' <h2 id='377fd8cc-b4f5-484a-ae0c-f6b0e99d767e' What engineers need to implement a feature</h2 <p <span style="white-space: pre-wrap;" Three things an engineer or coding agent needs to implement a feature successfully:</span </p <p <br </p <ul <li value="1" <b <strong style="white-space: pre-wrap;" What you're building</strong </b <span style="white-space: pre-wrap;" — A clear statement of the feature, who it serves, and the problem it solves. Every scope, priority, and trade-off decision traces back here.</span </li <li value="2" <b <strong style="white-space: pre-wrap;" Edge cases & constraints</strong </b <span style="white-space: pre-wrap;" — Error paths, permissions, performance limits, compatibility requirements, and feature-flag/rollout dependencies. These are the conditions that break implementations that otherwise look correct.</span </li <li value="3" <b <strong style="white-space: pre-wrap;" Why you're building it</strong </b <span style="white-space: pre-wrap;" — 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.</span </li </ul <p <br </p <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 </section <section data-section-id='bb69741a-3ef3-4873-a142-5b3543827c7c' <h2 id='bb69741a-3ef3-4873-a142-5b3543827c7c' Why context matters</h2 <p <span style="white-space: pre-wrap;" Requirements are never complete. They describe </span <i <em style="white-space: pre-wrap;" what</em </i <span style="white-space: pre-wrap;" to build but rarely explain </span <i <em style="white-space: pre-wrap;" why</em </i <span style="white-space: pre-wrap;" this approach, </span <i <em style="white-space: pre-wrap;" why now</em </i <span style="white-space: pre-wrap;" , or </span <i <em style="white-space: pre-wrap;" what comes next</em </i <span style="white-space: pre-wrap;" . 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.</span </p <p <br </p <p <span style="white-space: pre-wrap;" 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.</span </p <p <br </p <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 <p <br </p <p <span style="white-space: pre-wrap;" 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.</span </p <p <br </p </section <section data-section-id='c23d85f2-3b4a-427e-98b4-9bdc8ff8078c' <h2 id='c23d85f2-3b4a-427e-98b4-9bdc8ff8078c' The breakdown</h2 <p <span style="white-space: pre-wrap;" When implementation begins from partial context, every handoff in the SDLC becomes a reconstruction exercise.</span </p <p <br </p <p <span style="white-space: pre-wrap;" 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.</span </p <p <br </p <p <span style="white-space: pre-wrap;" The compounding risks are practical:</span </p <ul <li value="1" <b <strong style="white-space: pre-wrap;" Drift</strong </b <span style="white-space: pre-wrap;" — each handoff introduces small deviations that accumulate into a product that doesn't match the original intent</span </li <li value="2" <b <strong style="white-space: pre-wrap;" Shallow execution</strong </b <span style="white-space: pre-wrap;" — agents and engineers optimize for what's visible in the prompt, not what's implied by deeper product decisions made weeks earlier</span </li <li value="3" <b <strong style="white-space: pre-wrap;" Inconsistent decisions</strong </b <span style="white-space: pre-wrap;" — 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</span </li <li value="4" <b <strong style="white-space: pre-wrap;" Team misalignment</strong </b <span style="white-space: pre-wrap;" — weeks later, no one can reconstruct why a particular tradeoff was made, slowing every future iteration that touches the same area</span </li </ul <p <br </p <p <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 </p </section <section data-section-id='96171479-e93e-46f4-aa75-9ba2d0c958ed' <h2 id='96171479-e93e-46f4-aa75-9ba2d0c958ed' The ProductNow perspective</h2 <p <span style="white-space: pre-wrap;" 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.</span </p <p <br </p <p <span style="white-space: pre-wrap;" 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.</span </p <p <br </p <p <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 </p <p <br </p </section