Using Feedback in Development

“Loop engineering” is a hot buzzphrase after mentions of it by Boris Cherny (Claude Code’s creator) and Peter Steinberger (OpenClaw's creator) went viral on social media. Loops are now a key part of how we get AI agents to iterate at length to build software. In this letter, I’d like to share my 3 key loops, shown in the image below, for building 0-to-1 products. These loops guide not just how I build software, but also how I decide what software to build. Agentic coding loop: Given a product specification and optionally a set of evals (that is, a dataset against which to measure performance), we can have an AI agent write code, test its work, and keep iterating until the code is bug-free and meets its specification. This idea of closing the loop took off around the end of last year, and it has been a game changer in enabling coding agents to work longer productively without human intervention. For example, over the weekend, I was building an app for my daughter to practice typing, and my coding agent could easily work for around an hour, using a web browser to check what it had built multiple times before getting back to me, without needing my intervention. The engineering loop executes quickly. Every few minutes, the coding agent might build and test a new version of the software. I hear frequently from developers who are finding new ways to engineer more effective engineering loops. This is an active area of invention! Developer feedback loop: In this loop, a developer examines the current product and steers the coding agent to improve it. Last year, a lot of developers (including me) were acting as the QA (quality assurance) function for our coding agents, manually finding bugs and then asking the agent to fix them. But with coding agents much more able to test their own code, the amount of time we need to spend on this function has decreased significantly. This allows us to make higher-level product decisions, such as what key features to offer, where the UI needs improvement, and so on. The developer-feedback loop operates over time intervals between tens of minutes and hours — that's how frequently a developer might review a product and give feedback. In the case of the typing app, I changed my mind a few times about the visual design, what cat costumes she can unlock as she learns (she loves cats), and the user flow for a grown-up to log in and steer the child's learning experience. When a developer has a clear vision for what to build, it is still a lot of work to translate that vision into a specification for a coding agent to implement. Further, after the developer has seen an implementation, they might update (or perhaps clarify) the spec to steer it toward what they want. If you find that the system repeatedly runs into certain problems, building a set of evals for the agent becomes useful. [Truncated for length. Full text: https://lnkd.in/gKDQ6H9s]

"Feedback is a gift. It's an opportunity to learn and grow" At Google, we believe in the power of feedback to drive improvement. Sometimes feedback can be tough to hear. But taking the time to unpack it, understand the perspective, and reflect on it is crucial. Why feedback matters: - It reveals blind spots we cannot see ourselves - It accelerates learning by shortcutting trial and error - It demonstrates that others are invested in your success - It creates alignment between perception and reality How to receive feedback effectively: 1. Approach with curiosity, not defensiveness When receiving feedback, your first reaction might be to justify or explain. Instead, listen deeply and ask clarifying questions: "Can you give me a specific example?" or "What would success look like to you?" 2. Separate intention from impact Remember that well-intentioned actions can still have unintended consequences. Focus on understanding the impact rather than defending your intentions. 3. Look for patterns across multiple sources Individual feedback may reflect personal preferences, but patterns across multiple sources often reveal genuine opportunities for growth. 4. Prioritize actionable insights Not all feedback requires action. Evaluate which points will have the greatest impact on your effectiveness and focus your energy there. 5. Follow up and close the loop Demonstrate your commitment by acknowledging the feedback, sharing your action plan, and following up on your progress. Creating a feedback-rich environment: - Model vulnerability by asking for feedback yourself - Recognize and celebrate when people implement feedback successfully - Make it routine through structured check-ins rather than waiting for formal reviews At Google, we've learned that organizations with robust feedback cultures innovate faster, adapt more quickly to market changes, and build more inclusive workplaces. Let's commit to seeing feedback not as criticism but as a valuable investment in our collective future. The discomfort is temporary, but the growth is lasting. #motivation #productivity #mindset

The Paradox of Growth: The Bigger You Get, the Less You Know I came across something that stuck with me: When companies scale, they gain users — but lose understanding. Not because they stop caring, but because their customer feedback starts living everywhere — support tickets, sales calls, forums, surveys, social media, and app store reviews. That thought really made me pause. I’ve seen this firsthand. When a company is small, every piece of feedback feels personal — every bug report or review has a face behind it. But as you grow, those voices scatter across platforms and departments. Support sees the frustration, sales hears the hesitation, leadership sees the numbers — and somehow, everyone’s looking at the same customers, but no one’s hearing them anymore. That, in my opinion, is the quiet cost of growth. This is the problem Enterpret is solving — by helping teams stay in tune with their customers even as they scale. Here’s how it works: → It collects real-time customer feedback from 55+ channels — support tickets, sales calls, social media (X, Reddit, Instagram, Facebook), app store reviews, community forums, surveys, Slack, and more. → It analyzes all that feedback using AI and tells you exactly what to fix or build next. → It maps everything through a customer knowledge graph that connects feedback, complaints, and requests by channel, user, and payment data. → It even provides a chat interface where you can directly ask questions, and AI agents that flag bugs or issues automatically. That’s why teams like Notion, Perplexity, Canva, Chipotle, and The Farmer’s Dog use it — to make sure customer voices never get lost in the noise. In my view, the real lesson here isn’t about using more tools — it’s about staying close to the people you build for. Here’s how I’d approach it: ✅ Centralize every piece of feedback — even if it’s messy. ✅ Look for patterns instead of isolated complaints. ✅ Use AI systems like Enterpret to uncover the “why” behind what customers say. Because in the end, growth shouldn’t make you deaf. It should make you listen better — just faster. How does your team make sure you’re hearing what customers really mean, not just what they say? #CustomerFeedback #AIProducts #ProductStrategy #VoiceOfCustomer #Enterpret #Leadership

Following user feedback is a Product Management virtue. Is there an actual way to implement it, between all the noise, bugs, and stakeholder requests? Well… Most teams claim they are customer-driven. Yet the moment you open Zendesk, App Store reviews, survey results, and Slack threads, you instantly remember why everyone quietly avoids this work. Feedback is everywhere, contradictory, emotional, duplicated, and nearly impossible to turn into decisions.  It is chaos disguised as “insights.” This is why the new Amplitude AI Feedback release caught my attention and made it all the easier to decide to partner with them on this update. It successfully connects what users say with what they actually do, in one workflow. No extra tools.  No extra tabs. You see their words, frustrations, and praise. You see their behavior. And AI transforms it into ranked themes, rising trends, top requests, and complaints. Noise turns into clarity. Opinions turn into patterns. Patterns turn into action. And because it is native inside Amplitude, it kills the biggest problem in feedback work: Fragmentation. Everything flows into analytics, session replay, and cohorts, creating a full loop from insight to fix. You can trace why an issue matters, how many users care, how it impacts behavior, and which actions you should take. Finally, a single source of truth for PMs, UX, CX, and marketing. I’m also genuinely impressed with the supported sources of feedback: App Store, Google Play, Zendesk, Intercom, Freshdesk, Salesforce Service, Gong, Trustpilot, G2, Reddit, Discord, and X. Slack arrives in Q1, and there will be more! If you ever felt overwhelmed by feedback, this is one of the first attempts I have seen that genuinely solves the operational pain, not just the reporting part. It launches… Today! Take a look: https://lnkd.in/dAJKeTez What was the most successful update you know that came from the product’s users? Let me know in the comments. #productmanagement #productmanager #userfeedback

At Ramp we ship a new major feature every day - it's impossible for leaders stay up to speed. To keep a high bar without slowing folks down, teams can ship to early access tier whenever they want but need review for general release. Crazy fact: 10% of customers opt into early access because they can't get enough. That's 5000+ businesses. Plenty to work with. To release to general public, teams need to prove this product works and get sign off for heads of eng, product and design. Here is our template: 1. What did we build and why 2. What's the demo in < 3mn (loom) 3. Did we meet our goals in early access (hex dash) 4. Are customers raving about this (LLM on Zendesk tickets, Sprig surveys, and Gong transcripts) 5. Will customers easily discover and start using it (first time user journey) 6. Is sales ready to sell, AM ready to activate, and support ready to troubleshoot 7. Do we have a clear rollout plan (launch tier, pricing, coms) This helps us document decisions, serves as a strong checklist, and feeds our release notes. Most importantly, it keeps the bar high (we expect at least 1 rev of feedback). The best part: most of this template is automated using AI connected to the rest of our business systems. Leadership has 48h to review or it ships. We think it's a great way to balance speed & empowerment with visibility & quality.

In September, we interviewed 80+ candidates. We hired a few candidates and rejected others. We wrote an email to all rejected candidates, where we mentioned: - Reason for rejection - Room for improvement We also mentioned their strengths: - The best part of their interview Additionally, we shared documents related to their field and a list of questions an interviewer asked. Finally, we thanked all the rejected candidates for taking time from their schedule for the interview. Dear Companies, Rejection is not easy to handle. Giving feedback to rejected candidates increases their morale to perform best in the next interview. Agree?

As you start working with more and more stakeholders, there is a natural tendency to try accomodate every bit of feedback received. This is something we refer to as "Design by committee". It's also a surefire way to build subpar experiences by combining multiple irrelevant ideas into a single solution, rather than thinking deeply about the problem being solved and what the right solution is. Here is what the situation usually looks like: - Stakeholder A: "This competitor app is doing it that way." - Stakeholder B: "I showed this to my partner, and they didn't like it." - Stakeholder C: "Let's rethink this as it won't be clear to users." Some of the feedback above is valid, whereas other pieces are purely opinion-based, with no particular evidence or logical argument. It's your role as a designer to cut through the noise, eliminate pure opinion, debate where needed, and ultimately arrive at a solution that addresses the original problem, both for the business and the user. I have a simple decision tree I've used throughout my career as a thought process when dealing with feedback from multiple stakeholders. It boils down to four questions: 🟢 Is it clear and specific? ↳ If not, clarify it. 🟢 Is it supported by evidence or logic? ↳ If not, debate it. 🟢 Will it help us meet the objective? ↳ If not, kindly disregard. 🟢 Is it feasible? ↳ If not, save it as a fast-follow or future idea. If all the checks above are met, it's worth actioning the feedback. It still doesn't mean you have to act on every single suggestion, but it does mean you can quickly narrow down to a much smaller pool of items to consider. -- If you found this useful, consider reposting ♻️ What else have you found helpful in dealing with feedback from multiple stakeholders? Let me know in the comments 👇 PS: I'm working on a larger content piece on managing and working with stakeholders, dropping in the next few weeks. Find the link to the newsletter in the first comment.

I have been developing Agentic Systems for more than two years now and the same patterns keep emerging. 👇 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 𝗗𝗿𝗶𝘃𝗲𝗻 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 is the only way how you can be successful in building your 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 - here is my template. Let’s zoom in: 𝟭. Define a problem you want to solve: is GenAI even needed? 𝟮. Build a Prototype: figure out if the solution is feasible. 𝟯. Define Performance Metrics: you must have output metrics defined for how you will measure success of your application. 𝟰. Define Evals: split the above into smaller input metrics that can move the key metrics forward. Decompose them into tasks that could be automated and move the given input metrics. Define Evals for each. Store the Evals in your Observability Platform. ℹ️ Steps 𝟭. - 𝟰. are where AI Product Managers can help, but can also be handled by AI Engineers. 𝟱. Build a PoC: it can be simple (excel sheet) or more complex (user facing UI). Regardless of what it is, expose it to the users for feedback as soon as possible. 𝟲. Instrument your application: gather traces and human feedback and store it in an Observability Platform next to previously stored Evals. 𝟳. Run Evals on traced data: traces contain inputs and outputs of your application, run evals on top of them. 𝟴. Analyse Failing Evals and negative user feedback: this data is gold as it specifically pinpoints where the Agentic System needs improvement. 𝟵. Use data from the previous step to improve your application - prompt engineer, improve AI system topology, finetune models etc. Make sure that the changes move Evals into the right direction. 𝟭𝟬. Build and expose the improved application to the users. 𝟭𝟭. Monitor the application in production: this comes out of the box - you have implemented evaluations and traces for development purposes, they can be reused for monitoring. Configure specific alerting thresholds and enjoy the peace of mind. Learn all of this hands-on in my End-to-End AI Engineering Bootcamp starting in 2 weeks (10% off this week): https://lnkd.in/djvtszk5 ✅ 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗼𝗳 𝘆𝗼𝘂𝗿 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻: ➡️ Run steps 𝟲. - 𝟭𝟬. to continuously improve and evolve your application. ➡️ As you build up in complexity, new requirements can be added to the same application, this includes running steps 𝟭. - 𝟱. and attaching the new logic as routes to your Agentic System. ➡️ You start off with a simple Chatbot and add a route that can classify user intent to take action (e.g. add items to a shopping cart). What is your experience in evolving Agentic Systems? Let me know in the comments 👇

We all know that feedback and advice aren’t exactly the most popular things in the world. People might come to you asking for help, but then they turn around and ignore your advice. And do you know why? Because advice or feedback can feel like a gut punch to their sense of self-efficacy. But here’s the thing, it’s all about HOW you deliver that feedback. If you want to deliver feedback that sticks and has an impact, start with offering validation. Validate some element of their behaviour - their enthusiasm, their grit, their patience or their creativity. This way, they’ll feel like you’re on their side. Then, frame things from the perspective of growth. “You know what would make you even more effective? What would take you to the next level?” Because here’s the truth: people want to grow. And when you approach feedback with the intention of wanting to see someone succeed (rather than just pointing out their faults), it changes how their respond to it. As the OG Dale Carnegie said: “Give them a fine reputation to live up to, and they will make prodigious efforts rather than see you disillusioned.” P.S. When you receive feedback, are you more likely to follow-through when it’s framed around growth? #feedback #growth

Honest feedback is oxygen for organisations. Without it, learning slows, trust erodes, and culture weakens. In many companies people hesitate to share giving negative feedback. It feels uncomfortable to tell a colleague or a senior what is not working. The result is backbiting and gossip where people talk behind their backs rather tuan giving direct feedback. Over time this silence damages both relationships and performance. At Marico Limited we worked hard to build a culture where people could speak their mind openly. Feedback was encouraged in both directions. If I disagreed with someone, I would say it directly and respectfully. If someone had an issue with me, they were expected to tell me on my face rather than behind my back. The how mattered as much as the what. Focus on the specific incident, the impact on the business, and the way forward. When honesty becomes part of the culture, people feel safe to speak up. Problems are surfaced earlier. Solutions are found faster. Trust deepens. And the organisation becomes stronger. Feedback is not a threat. It is a gift that allows people and businesses to grow. #leadership #culture #growth #team #success