Feedback Loops in DevOps

Picture this workflow: we commit code, it goes straight to trunk, gets built and tested in multiple ways, deploys to production, undergoes more validation, and if everything's green, we release. We can start with canary users and gradually expand, always ready to roll back if needed. This isn't just about speed. Short feedback loops transform how we work. They sharpen our ability to respond to change and push teams to communicate better, creating patterns that deliver quality safely. When I share this vision, I often hear "that would never work in the real world, especially in regulated environments." Here's the thing: I've spent my entire career in regulated contexts, sometimes working on mission-critical software. And I can tell you that frequent, fast feedback loops are exactly how we make things safe and reduce risk. Long processes, isolated coding, and manual validation don't create security. They create a false sense of it. Real safety comes from embracing the right practices: continuous integration through trunk-based development, continuous delivery (or better yet, deployment), and social development where engineers, product managers, UX experts, and QA professionals work together from the start. The irony is that the environments that need safety most are often the ones resisting the very practices that would give it to them. But those of us who've done this work know better. Safety isn't about moving slowly. It's about moving deliberately, with constant validation, and the confidence that comes from knowing exactly what's happening in your system at all times. #softwaredevelopment #softwareengineering

Feedback loops determine how fast organizations improve Improvement speed is rarely limited by talent. It is limited by feedback quality and timing. Research shows that organizations with tight, accurate feedback loops correct faster, make fewer repeated mistakes, and adapt more effectively than those relying on periodic reviews or delayed reporting. Slow feedback equals slow learning. What research shows Studies in organizational learning and performance management indicate that rapid feedback significantly improves accuracy and execution. Delayed or indirect feedback weakens cause-and-effect understanding, making it harder to know what actually worked. Research also shows that feedback loses effectiveness as time passes. The longer the gap between action and feedback, the lower the learning value. Study-based situations Situation 1: Product development Research found that teams receiving immediate user feedback iterated more effectively and avoided costly late-stage changes. Teams relying on quarterly reviews accumulated errors. Situation 2: Performance management Studies on employee performance show that real-time feedback improved outcomes more than annual or semiannual reviews. Frequent, specific feedback reduced repeated mistakes. Situation 3: Strategic execution Research on execution systems shows that organizations reviewing leading indicators weekly corrected course earlier than those reviewing lagging indicators monthly. How effective leaders strengthen feedback loops They shorten time between action and review They focus feedback on specific behaviors and metrics They prioritize leading indicators They remove intermediaries that distort information Organizations do not improve by intention. They improve by feedback.

I’ll be sharing a few lessons from challenges we’ve faced at Cafeto and how we’ve addressed them. On this occasion, I want to talk about our experience adopting PHVA (Plan Do Check Act) in our Agile development process. Because we primarily build for customers, service quality and predictability matter a lot, so optimizing how we work is core to delivering better outcomes. As we adopted PHVA, our process became lighter, clearer, and more consistent across projects, helping teams move faster while improving quality. For small and mid size projects, this structure has been especially effective because planning stays lightweight while design and QA get dedicated runway. We typically run two week sprints with UX and Architecture one sprint ahead and QA in a parallel hardening sprint. We also noticed a common anti pattern where planning defaults to coding, leaving design and testing squeezed. We address this by giving each phase its own lane and cadence: Plan (Design first): Plan is not a coding meeting. We emphasize design and discovery, user flows, states, accessibility, API contracts, and clear acceptance criteria. UX and architecture run one sprint ahead, so delivery starts with clarity. For small and mid projects, we keep slices to about 1 to 2 weeks of effort and limit WIP. Do (Build and Reviews): Implement the slice incrementally with feature flags as guardrails, conduct code reviews as part of the work, pair on complex pieces, keep PRs small, write test cases, and maintain steady flow. Check (Validate and Test): Ensure the increment is properly validated with testing suites for functional and non functional checks such as performance, security, and accessibility. QA works a parallel sprint, hardening the increment to be delivered and preparing test assets for what is next. Act (User Acceptance): Secure customer blessing on the delivered increment through UAT. Capture feedback, update the backlog, and roll learnings into the next Plan phase. Working this way has meant fewer handoffs, cleaner releases, and faster feedback loops, which adds up to better results for customers. Not perfection, just steady improvements that compound every sprint. On larger programs, we scale the same loop across multiple teams, and the fundamentals do not change. #Agile #DevOps #QA #UX #ContinuousImprovement #PHVA #PDCA #Cafeto #Product #Delivery #CustomerExperience #Nearshoring

𝗚𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀 aren't an afterthought or extra credit anymore - they're core architectural patterns that determine whether your agentic system is safe to deploy. So here are four different workflow patterns that we've seen implemented in production systems: 1️⃣ 𝗔𝗱𝗮𝗽𝘁𝗶𝘃𝗲 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗟𝗼𝗼𝗽𝘀 Worker agents execute tasks → Supervisor evaluates → Rewards Service updates policies → Guidelines adjust → Workers improve over time. This creates a continuous learning cycle where the system reinforces effective behaviors and discourages risky ones. It's reward-driven learning that improves with iteration. 2️⃣ 𝗖𝗼𝗿𝗿𝗲𝗰𝘁𝗶𝘃𝗲 𝗔𝗰𝘁𝗶𝗼𝗻 The centralized Supervisor assigns tasks, compares outputs against application guidelines, and if errors are detected, engages alternative workers. The best validated result gets returned. This prevents bad outputs from ever reaching users. 3️⃣ 𝗛𝘂𝗺𝗮𝗻 𝗶𝗻 𝘁𝗵𝗲 𝗟𝗼𝗼𝗽 For sensitive domains (medical diagnosis, legal review, financial approvals), agents generate preliminary responses but humans validate before execution. The workflow automatically pauses for expert review, then resumes once approved. 4️⃣ 𝗘𝗺𝗲𝗿𝗴𝗲𝗻𝗰𝘆 𝗦𝘁𝗼𝗽 Critical for high-risk environments like trading systems. Agent 1 collects market data → LLM processes signals → Agent 2 evaluates conditions → if anomalies or risks detected, execution halts immediately. Consider a trading bot with access to a volatility API showing VIX at 42 (extreme market stress). Even if the bot generates an aggressive trade recommendation, the evaluator independently verifies: "Given current volatility, does this make sense?" If not, it blocks the action entirely. 𝗕𝗲𝗵𝗮𝘃𝗶𝗼𝗿 𝗦𝗵𝗮𝗽𝗶𝗻𝗴 is the underlying philosophy here - a three-step loop of scoring, feedback, and correction. The evaluator doesn't just measure performance after the fact. It actively intervenes: triggering rollbacks for bad transactions, halting workflows propagating incorrect data, or routing edge cases to human reviewers. This is especially important when agents interact with volatile external states - market conditions, API health, system load. The evaluator provides a sanity check to ensure the model correctly interpreted the signals it was given, not just that it generated understandable text. The goal isn't catching every possible failure upfront (impossible). It's building systems that detect problems as they happen, understand what went wrong, and automatically correct course before damage propagates. Inspired by our most recent ebook we did with StackAI and Weaviate: https://lnkd.in/dKt9SVya

CI/CD is more than automation — it’s a mindset shift in how modern teams deliver software. A well-designed pipeline transforms raw code into reliable production releases through continuous validation, automation, and feedback. The goal isn’t just speed — it’s predictable, measurable delivery. Here’s how the flow typically works: 🔹 Source Control – Every change starts with version control. Git becomes the single source of collaboration and traceability. 🔹 Build Stage – Applications are compiled, dependencies resolved, artifacts packaged, and containers created. 🔹 Testing Phase – Unit tests and automated checks ensure code quality before it moves forward. 🔹 Deployment & Automated Validation – Infrastructure as Code and test automation guarantee consistency across environments. 🔹 Production Release – Continuous delivery strategies enable safe deployments with rollback capabilities. 🔹 Monitoring & Feedback – Metrics, logs, and traces provide real-time insight, closing the loop for continuous improvement. The real power? Feedback from production feeds directly into the next iteration. No matter the toolset — Azure DevOps, Jenkins, GitHub Actions, GitLab, or any other platform — the philosophy stays the same: 👉 Every commit should be production-ready. 👉 Every deployment should be observable. That’s how teams build confidence, reduce risk, and move faster without sacrificing quality. #DevOps #CICD #Automation #CloudEngineering #AzureDevOps #GitHubActions #ContinuousIntegration #ContinuousDelivery #SRE #Observability #DevSecOps #InfrastructureAsCode #GitOps #Kubernetes #Terraform #AWS #GCP #PlatformEngineering #CloudNative #ShiftLeft #SecurityByDesign

Your AI keeps failing for ONE reason… Not because AI is “bad.” Not because your team did it wrong….But because most leaders skip the system that makes AI useful. ↳ I call it The AI Improvement Loop. No fluff. No hype. Just how AI actually works inside real businesses. Here’s the step-by-step: ↳ First, start with a HYPOTHESIS: Before launching any AI tool, ask one question: → What could go wrong in a real customer situation? Define success before speed. AI without expectations creates chaos. ↳ Next, DOCUMENT the test: Decide exactly: → What the AI handles → What it escalates → How quality is measured (not just response time) → When humans step in If it’s not written down, you can’t improve it. Then, build review CHECKPOINTS: This is where most companies fail. → AI reviews AI first (cheap, fast quality gate). → Humans spot-check patterns, not perfection. → Weekly team reviews update training and rules. AI doesn’t learn through hope. It learns through FEEDBACK. ↳ After that, ITERATE using real data: Every week, review: → Where AI performs well → Where humans override decisions → Which situations confuse it → What edge cases keep appearing Each insight becomes training. Each mistake becomes leverage. ↳ Finally, document what “GOOD” looks like: Share wins. Capture examples. UPDATE systems. This improves AI, onboarding, and team confidence at the same time. The result? ✅ AI stops being a risk. ✅ Teams stop firefighting. ✅ Leadership becomes lighter, not heavier. That’s the AI Improvement Loop. → If you want help building this loop inside your organization, DM me “AI LOOP” and let’s talk through your system. I help healthcare and eldercare leaders design human-first AI systems, build feedback loops that prevent costly mistakes, and turn technology into a reliable teammate instead of a liability. #systems #leadership #business #strategy #ProcessImprovement

We often say '𝐃𝐞𝐯𝐎𝐩𝐬' like it is a single thing. But if you have ever built or scaled a real software system, you know the truth: DevOps is an ecosystem. A full-on atmosphere. And every part plays a role in keeping the system breathing. I was going through this visual the other day and it nails something most people overlook: -> DevOps is not just about pipelines or automation. -> It is about how everything talks to everything else. -> From planning to deployment. -> From monitoring to feedback. Let’s break it down. At the centre is Collaboration This is not a soft word. It is the engine. Developers, ops engineers, QA, security, product they all have to share ownership. Not just of code, but of quality, uptime, and delivery. Surrounding that are the core stages: 𝟏. 𝐏𝐥𝐚𝐧 This is where it starts. Tools like Jira, Trello, or Azure Boards. But more than tools, this is about setting clarity. What are we building and why? 𝟐. 𝐃𝐞𝐯𝐞𝐥𝐨𝐩 Here, code gets written. VS Code, IntelliJ, GitHub. Dev environments matter. But so does code quality. This is where peer reviews and branch strategies shape long-term velocity. 𝟑. 𝐁𝐮𝐢𝐥𝐝 𝐚𝐧𝐝 𝐓𝐞𝐬𝐭 CI kicks in. Build pipelines validate every change. Unit tests, integration tests, static analysis all this is where you stop problems early. Fast feedback loops are gold here. 𝟒. 𝐑𝐞𝐥𝐞𝐚𝐬𝐞 𝐚𝐧𝐝 𝐃𝐞𝐩𝐥𝐨𝐲 Now you are into CD. Staging environments. Rollout strategies. Manual approvals if needed. This is where confidence matters more than speed. 𝟓. 𝐎𝐩𝐞𝐫𝐚𝐭𝐞 𝐚𝐧𝐝 𝐌𝐨𝐧𝐢𝐭𝐨𝐫 Once it is live, your job is not done. You watch it. Azure Monitor, Prometheus, Grafana or whatever your stack, visibility is non-negotiable. This is where ops earns its name. 𝟔. 𝐂𝐨𝐧𝐭𝐢𝐧𝐮𝐨𝐮𝐬 𝐅𝐞𝐞𝐝𝐛𝐚𝐜𝐤 And then all loops back. From logs, metrics, user feedback, postmortems. The system learns. The team improves. DevOps is not a job title or a toolchain. It is this atmosphere. A culture of shared ownership, fast iteration, and tight feedback loops. If you get it right, your product feels alive. If you do not, you are flying blind. -> So here is a question for you: Which part of this DevOps atmosphere is your team strongest in? And which part still feels like a bottleneck? Drop it in the comments. Let’s compare notes.

Most embedded teams talk about CI/CD pipelines. That’s the obvious part. But almost no one talks about the feedback pipeline. You see, a company’s relationship with its customers isn’t just about delivering raw value. We also need to deliver the *right* value. That’s why we need to create a tight Value Feedback Loop between the company and customers. How? By using a feedback pipeline. Without a feedback pipeline, teams move fast in the wrong direction. They ship features, but not improvements. They measure output, but not outcome. So, maybe CI/CD should actually be CI/CD/CF: Continuous Integration, Continuous Delivery, Continuous Feedback. That last part closes the loop. It connects what we build to how customers experience it. It tells us what’s working (and what’s breaking) before the customers even submit the support tickets. That’s what observability really is. And it might be the most underrated practice in embedded systems today. How does your team build feedback into your development cycle?

Fear of deployment is the largest source of tech debt. That’s just one of the many sharp insights from my recent conversation with Charity Majors, co-founder and CTO of honeycomb.io We covered a lot around what makes modern engineering teams truly effective, and where many go wrong. A few takeaways that stuck with me: - Shipping is the heartbeat of your engineering organization. It should be regular. It should be boring. It should happen so often that it's just the normal state of things. - Shorten the feedback loops. Keep the interval between writing the code and using it short because you will never know more about the change than you do right in that moment. - It's not about deploying on Fridays. It’s about expecting your code to go out immediately when you merge it. - Developers should own their code in production. Just because someone isn't pushing the button doesn't mean a developer shouldn't be aware. You don't merge and walk out the door. - Observability is not optional with AI. We're moving away from static dashboards towards workflows. Tight feedback loops are a necessity for building good AI software. Tune in to the complete episode on Aviator, Spotify, Apple Podcasts, or YouTube: https://lnkd.in/gCvjBDw6 #DevEx #Observability #ContinuousDeployments