How to prevent 70% of technical debt with AI

Chainguard survey reveals that developers spend 84% of their time on maintenance and tech debt, while AI promises relief; however, adoption faces trust gaps and mixed leadership signals. By Darryl Taft feat. Dustin Kirkland

Chainguard survey reveals that developers spend 84% of their time on maintenance and tech debt, while AI promises relief; however, adoption faces trust gaps and mixed leadership signals. By Darryl Taft feat. Dustin Kirkland

An old colleague I spoke to just got out of a three-hour outage call fielding questions from angry customers and from the senior execs. Why? As he puts it: "The execs who aren't close to the code just say 'AI' and then ask, '𝘏𝘰𝘸 𝘮𝘢𝘯𝘺 𝘮𝘰𝘳𝘦 𝘭𝘪𝘯𝘦𝘴 𝘰𝘧 𝘤𝘰𝘥𝘦 𝘢𝘳𝘦 𝘸𝘦 𝘱𝘳𝘰𝘥𝘶𝘤𝘪𝘯𝘨?'" There's immense pressure from the top to leverage AI for 10x development speed. But this excitement is often disconnected from the complex reality on the ground. My colleague's team is dealing with: 1. Lost institutional knowledge (after org changes) 2. A "crazy big monolith" that no one fully understands. 3. Unexpected customer behavior (customers push the system limits) As he said, "It's just slop coming into the system." The core responsibility of an engineering leader doesn't stop at building software. It's owning the E2E development cycle (which also includes maintaining, operating, and fixing said software). While AI is supercharging the "build" phase, it's making the "fix" phase exponentially harder by adding complexity to an already strained operational environment. That's what we're solving for at Bacca.ai.

Since I have been delving in complex systems again, I want to bring you a tidbit of thought: Assume all users want the same thing. One database table. One payment type. One user role. The simplest, most naive model that could possibly work. Then let reality tell you you're wrong. Most systems are simpler than we pretend. But we start with complexity because we're scared of looking stupid when edge cases arrive. So we build for edge cases that never come. I've launched payment systems with twenty payment methods. Two years later, 97% of transactions are credit cards. The rest are a pain in the ass while updating anything. The simple model isn't the final answer. It's a probe. You push it into reality and see where it breaks. Each break teaches you which complexity actually matters versus which complexity you imagined in a conference room. Most organizations do the opposite. They imagine every possible failure, design for all of them, then discover most never happen. But now they're stuck maintaining defenses against imaginary problems forever. A startup builds a feature in days with one database table. An enterprise spends months designing seventeen tables for the same feature. Not because they need them. Because someone asked "what if" and nobody had the courage to say "we'll handle it if it happens." Every abstraction you add before reality demands it becomes debt. Every edge case you handle before it appears becomes maintenance burden. Every complexity you imagine becomes permanent tax. The dumbest possible model reveals what matters. Start with all users identical - reality will show you which differences matter. Start with one workflow - reality will show you which variations are needed. Start with synchronous processing - reality will show you what needs to be async. Complexity should be earned through collision with reality, not imagined in architecture reviews. But that requires admitting you don't know everything upfront. Most organizations would rather be complex than look uncertain. #systemsthinking #agile #digitaltransformation #architecture

Your board asked what the API rewrite will deliver in business value. You said "improved maintainability." Your CFO asked which engineering investments will impact revenue this year. You said "it's hard to quantify technical work." Your CEO asked if the new architecture is worth the 6-month delay. You said "we need to do this right." Here's what you actually said: "I can't defend engineering budgets with numbers." ******** Ship and Hope costs nothing upfront. When the rewrite takes 9 months instead of 6, nobody can prove you were wrong because you never said what "done" meant. Perfect deniability. The thing is, your board heard this: "Engineering can't connect technical decisions to business outcomes". And here's what happened: → R&D budget frozen pending "improved accountability" → Strategic architecture decisions now require business case approval → That platform bet you needed? Killed. ******** The vagueness that protects you from being wrong is the same vagueness that puts engineering decisions under CFO review. You're in the room. Nobody's listening. The CTOs who keep engineering autonomy said THIS instead: "We'll consolidate three customer databases into one by Q2. Target: reduce data sync errors from 2,000/day to under 50. At current support cost, that's €180K annual savings. We’ll consolidate the top 20% of customer data first, measure the error drop, and scale from there". ******** Quantification isn't perfectionism. It's how you keep the CFO out of your tech strategy decisions.

A few weeks ago, I said engineers aren’t going anywhere. And now there’s even more proof. Platforms like Lovable, Replit, and dozens of AI-powered builders were supposed to replace developers overnight. But take a look at the data -> Lovable’s revenues are down sharply after months of exponential growth. It’s the perfect example of what Gartner calls the Hype Cycle. Everyone was so passionate about no-code, vibe-code, and all those tools. People kept subscribing, buying, and investing in more and more products and companies doing vibe coding. Then suddenly, the world woke up and realized that it’s not a magic solution. Now we can see it for what it was: mostly fairy tales. The initial hype cycle is over. First comes the peak of inflated expectations. Then comes the valley of disillusionment. And now we’re entering a phase of steady, sustainable growth again. We’re finally touching the ground again, realizing we still need real engineers, architecture, planning, and code. And my opinion on this topic isn’t going to change, is yours?

Platform engineering is at a crossroads. As infrastructure grows more complex, traditional tools are falling short, turning platform teams into bottlenecks. At Outshift, in collaboration with CAIPE and AGNTCY, we’re addressing this with GraphRAG, a system that combines knowledge graphs with LLMs to provide accurate, explainable answers to complex queries. We’re also automating graph generation and working toward a unified system for structured data and documentation. Read our latest blog for all the details.⬇️ https://cs.co/60477MSK1

One of the most useful starting use cases for #AgenticAI is easy access to #knowledge —found in wikis, playbooks, team chat history, and scattered across various systems. Work led by Shubham and #opensourced under Cloud Native Operational Excellence (CNOE)’s caipe.io (Community AI Platform Engineering) enables combining #RAG, #GraphRAG, and specialized data agents to deliver significant value.                  •              Read the blog.                  •              Check out the short talk at Neo4j #nodes225 at 14:45 UTC today: https://lnkd.in/ewekGBrz.                  •              Try it out as part of https://lnkd.in/eSfDH2ss.

Most people overcomplicate Agents. They’re not magic they’re just scaffolding that helps LLMs do economically valuable work. Here’s what actually happens when you run one 👇 1: The agent builds context (system/user messages, chat history, memory, state) 2: Sends it to the model. 3: The model responds with a message, a tool call, or both. 4: The agent executes tool calls and feeds results back. 5: The loop continues until a final message is produced. 6: The agent returns that response. That’s it. The hard part isn’t this loop it’s running a system of agents in production and building a product around it with great UX. Reliability, durability, state management, and smooth UX across thousands of concurrent sessions that’s where the real engineering happens. These are long-running, stateful processes that need isolation, persistence, and fault tolerance. 💡 My advice: Treat Agent Engineering as Software Engineering. If the tools you’re using don’t align with real software-engineering principles — run. 🚩 If the framework relies on Jupyter notebooks to demo that’s a red flag. 🚩 If there’s no database how will you ensure persistence? The future of agents won’t be built in notebooks. It’ll be built by engineers who know how to ship software.

💸 Technical debt is the cost of shipping fast. In software, technical debt is borrowed time. You deliver features faster today, but pay the interest through slower development tomorrow. 1. Technical debt = shortcuts with consequences Skipped unit tests, tangled dependencies, poorly organized code, missing error handling. Code that works but breaks basic design rules or lacks proper logging. According to McKinsey research, developers spend up to 40% of their time managing technical debt instead of building new features. 2. It's a valid trade-off Early-stage companies prioritize speed over architecture. If you don't know whether customers want your product, spending weeks on database optimization or microservices wastes time. Ship a monolith. Refactor once you know it works. 3. The cost of ignoring it Small problems compound. N+1 queries go unnoticed until traffic spikes. Missing tests mean refactoring takes weeks instead of days. Performance degrades drastically: a query that took milliseconds now takes seconds. Studies show that unmanaged technical debt slows feature development by up to 30%. 4. Manage it consciously Track what matters: test coverage, code complexity, time spent fixing old code. Dedicate sprint time to pay down debt. Measure it: how much developer time goes to maintenance versus new features each month? Ignoring technical debt slowly strangles development velocity, but not having it is nearly impossible. You can choose whether to take it strategically or let it accumulate by accident. 📊 How do you track the cost of technical debt in your team? #TechnicalDebt #BackendDevelopment #Architecture #Streamlogic