<section data-section-id='6e7c72f9-7ce3-401b-9841-2ffcfb496344'><audio url="https://api.productnow-prod.com/storage/getStorageObject?storageObjectId=947f9608-b406-4dcb-8e51-4d6ee8c46fce" /></section> <section data-section-id='d61e7409-1ef3-4ccf-85a1-b86c04c465b2'><p><span style="white-space: pre-wrap;">Shipping a feature is easy to describe and hard to get right. Teams often know just enough to start, but not enough to avoid either kind of waste: rework from vague requirements, or drag from overbuilding for a future that may never arrive.</span></p><p><br></p><p><span style="white-space: pre-wrap;">The old instinct was to solve that gap with more heroics in the handoff. Product set direction, design shaped behavior, and engineering filled in the blanks as code moved toward release. That worked when people were involved and had the time to absorb missing details.</span></p><p><br></p><p><span style="white-space: pre-wrap;">Agents change the equation. They are fast, but they are not good at guessing what the team meant. The same ambiguity that humans could correct informally now shows up as drift, cleanup, and verification work.</span></p><p><br></p><p><span style="white-space: pre-wrap;">The challenge is not to freeze requirements too early or keep them forever loose. It is to preserve enough structure for change while making assumptions, constraints, and tradeoffs explicit enough to build against now.</span></p><p><br></p><p><span style="white-space: pre-wrap;">The best implementation process turns uncertainty into a shared system for deciding what to build, what to defer, and what to keep flexible.</span></p><p><br></p></section> <section data-section-id='4ece3ee3-48c1-461b-ad8d-1b182cd6310f'><h2 id='4ece3ee3-48c1-461b-ad8d-1b182cd6310f'>The old world of implementation</h2><p><img src="https://api.productnow-prod.com/storage/getStorageObject?storageObjectId=1fdffddc-1054-431a-998f-40647917b028" alt="Minimal editorial illustration of three connected implementation stages: unclear requirements, handoffs, build/review. Show a subtle contrast between a sparse spec document and experienced humans filling in gaps. Clean flat style, muted blue/gray palette, white background, simple arrows, no text beyond the stage labels. Suitable as a conceptual companion to a short explanation about humans compensating for missing detail." data-alignment="center" data-card="true" width="594" height="493"></p></section> <section data-section-id='0a514e6f-c359-4395-bfb9-3b644a4550e8'><p><span style="white-space: pre-wrap;">Implementation used to run like a relay race. Product set the direction, design shaped the behavior, and engineering carried the work across the finish line by filling in the gaps with judgment before anything shipped. Strong engineers were expected to absorb edge cases, backend constraints, accessibility, and scope tradeoffs as part of the build, not after it.</span></p><p><br></p><p><span style="white-space: pre-wrap;">That made implementation deeply human-heavy. The work depended on people noticing what the spec left out, translating rough intent into concrete behavior, and making judgment calls under schedule pressure. In practice, the handoff was rarely clean; it was a series of clarifications, compromises, and fixes that happened just before or during build.</span></p><ul><li value="1"><span style="white-space: pre-wrap;">Requirements were often treated as settled too early, which led to rework</span></li><li value="2"><span style="white-space: pre-wrap;">Late handoffs left engineering to discover hidden assumptions and fill in missing behavior</span></li><li value="3"><span style="white-space: pre-wrap;">Teams often built speculative “just in case” work that added cost without solving today’s problem</span></li></ul><p><br></p></section> <section data-section-id='13eb60a5-1e8f-46a0-bca7-efdd14f2bbc7'><h2 id='13eb60a5-1e8f-46a0-bca7-efdd14f2bbc7'>The new world with agents</h2><p><img src="https://api.productnow-prod.com/storage/getStorageObject?storageObjectId=c0cb972f-e076-4f0e-9243-ac43299d3f7c" alt="Editorial illustration for a section titled 'Automation Amplifies Process Fractures'. Show a modern production line/workflow with 5 stages from left to right: spec, design, build, QA verification, release. The first four stages are moving quickly and efficiently with subtle AI/agent symbols, while QA verification is the bottleneck: repeated red circular feedback loops and a growing backlog of rejected work pile up at QA. Make the speed mismatch visually obvious: upstream stages are flowing fast, downstream QA is overwhelmed. Clean flat vector style, muted blue/gray palette with a strong red accent only on the failure loop and backlog, white background, minimal text, conceptual and modern, suitable as a companion illustration about process debt being exposed by automation." data-alignment="center" data-card="true"></p></section> <section data-section-id='90924362-8a40-4bcf-a194-16ed97c3935e'><p><span style="white-space: pre-wrap;">Agents move implementation from writing code to specifying work, packaging context, and verifying outputs. That changes the execution job: humans can fill gaps with shared judgment, but agents drift when they aren't told exactly what to do.</span></p><p><br></p><p><span style="white-space: pre-wrap;">In our experience, these implicit gaps are usually where hallucinations show up first.</span></p><p><br></p><p><span style="white-space: pre-wrap;">And that has a real human cost. When the agents do what they're told and then attempt to fill in the gaps themselves, it often creates more work, not less.</span></p><p><br></p><p><span style="white-space: pre-wrap;">It adds review fatigue, validation steps, extra handoffs, and cleanup that hides the real cost. And often we do not find out until late in the process.</span></p><p><br></p><p><span style="white-space: pre-wrap;">Agents need context, clear instructions, and broken-down steps. Vague prompts and bloated context only make the failures harder to spot.</span></p><p><br></p><p><span style="white-space: pre-wrap;">What we keep finding from talking to customers is that the new world is not “agents replace process.” It is the opposite: process and handoffs matter more, because they are now the main way to keep agents accurate, aligned, and worth trusting.</span></p><p><br></p><p><span style="white-space: pre-wrap;">And the more autonomous parts of the organization become, the more the weakest step in your process gets hit over and over, faster and faster, until that weakness becomes impossible to ignore.</span></p><p><br></p></section> <section data-section-id='f4261fd5-6b37-4025-9673-5d1429248cbc'><h2 id='f4261fd5-6b37-4025-9673-5d1429248cbc'>What must change</h2><p><span style="white-space: pre-wrap;">To us, the goal is more useful work out of agents, with fewer requirements for human oversight. That allows us, as an organization, to allocate human attention to the more pressing problems. To do that, we've found you need the following:</span></p><ol><li value="1"><b><strong style="white-space: pre-wrap;">Make assumptions explicit.</strong></b><span style="white-space: pre-wrap;"> Write down guesses, scope shifts, and open questions as you go, so teams don't treat the first draft as settled.</span></li><li value="2"><b><strong style="white-space: pre-wrap;">Tighten handoffs.</strong></b><span style="white-space: pre-wrap;"> Pass behavior, risks, and edge cases before build; late engineering involvement is where friction and rework compound.</span></li><li value="3"><b><strong style="white-space: pre-wrap;">Attach business context.</strong></b><span style="white-space: pre-wrap;"> Put the why in the document so implementation choices reflect priority, and potential future state, not just instructions.</span></li><li value="4"><b><strong style="white-space: pre-wrap;">Use context efficiently.</strong></b><span style="white-space: pre-wrap;"> Keep prompts and specs tight, specific, and up to date; bloated context makes it harder to see what matters.</span></li></ol><p><br></p><p><span style="white-space: pre-wrap;">That means explicitly refining scope, involving engineers earlier, and giving the team enough context to act without guesswork. AI-heavy workflows need the same discipline: clearer instructions, smaller context, and explicit edge-case handling. The goal is not more process; it is less ambiguity.</span></p><p><br></p><p><web-citation data-url="https://www.pmi.org/learning/library/projects-fail-poor-requirements-management-2484" data-title="PMI requirements management article"></web-citation><web-citation data-url="https://public.dhe.ibm.com/software/emea/de/rational/neu/Achieve_predictability_in_desired_business_outcomes_EN_2009.pdf" data-title="IBM predictability report"></web-citation><web-citation data-url="https://www.nngroup.com/articles/creating-design-specs-for-development" data-title="NN/g design specs for development"></web-citation><web-citation data-url="https://www.nngroup.com/articles/developer-designer-relationship" data-title="NN/g developer-designer relationship"></web-citation><web-citation data-url="https://www.nngroup.com/articles/the-product-triad-designs-role" data-title="NN/g product triad"></web-citation><web-citation data-url="https://martinfowler.com/bliki/Yagni.html" data-title="Martin Fowler YAGNI"></web-citation><web-citation data-url="https://survey.stackoverflow.co/2025/ai" data-title="Stack Overflow AI survey 2025"></web-citation><web-citation data-url="https://cloud.google.com/blog/products/ai-machine-learning/announcing-the-2025-dora-report" data-title="Google Cloud 2025 DORA report"></web-citation><web-citation data-url="https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents" data-title="Anthropic context engineering for AI agents"></web-citation><web-citation data-url="https://openai.com/business/guides-and-resources/a-practical-guide-to-building-ai-agents" data-title="OpenAI practical guide to building AI agents"></web-citation><web-citation data-url="https://arxiv.org/html/2502.13069v1" data-title="Interactive software-engineering agents study"></web-citation><web-citation data-url="https://www.sonarsource.com/company/press-releases/sonar-data-reveals-critical-verification-gap-in-ai-coding" data-title="Sonar verification gap in AI coding"></web-citation></p><p><br></p><p><feedback data-feedbacknodeid="5c2bd010-2691-4935-bf6b-125135de86e9" data-title="What context is most often missing for you?" data-datatype="TEXT" data-metadata="{&quot;type&quot;:&quot;TEXT&quot;}" data-description="What would make your job easier if you had it?"></feedback></p></section> <section data-section-id='169a3791-d6a3-4861-a18d-a1f1f57314cb'><h2 id='169a3791-d6a3-4861-a18d-a1f1f57314cb'>Final thoughts</h2><p><span style="white-space: pre-wrap;">Implementation quality has not gotten easier. The operating model around it has changed. The bar for correctness is still the same, but ambiguity now travels faster through specs, context, scope, and handoffs — so small gaps compound into rework, drift, and slower decisions.</span></p><p><br></p><p><span style="white-space: pre-wrap;">The goal is not more process for its own sake. It is a clearer way to execute.</span></p></section>