Knitting an AI-Native Software Team (Part-2)
Authored By: Sai Sreenivas Kodur
The Compounding Machine - Why Wrong Foundations / Incomplete Thinking Become Fatal at AI Speed
Here's the counterintuitive insight that took me months of painful debugging to understand: when your development speed increases by 50%, your technical debt doesn't increase by 50% - it increases by 200% or more. And if your foundations are wrong, that debt becomes existential within weeks, not years.
I learned this the hard way, when we used AI to rapidly build a new CI/CD pipeline. We leveraged an AI assisted workflow, that involved coming up with a high level plan, absorbing documentation from our third-party vendor, AWS best practices, and Kubernetes principles, generating a seemingly perfect pipeline in just a day that would have several weeks to build manually. But after deployment when it quickly got adopted and used by our team (both the human and AI Engineers), our AWS bills exploded - climbing 120% within a few weeks. It took me several weeks of debugging to discover the root cause: the AI had perfectly implemented resource creation for our development environments but completely missed that they were supposed to only be ephemeral in nature and our workflow required aggressive cleanup strategies for these temporary deployments. The pipeline was creating hundreds of orphaned Kubernetes namespaces, load balancers, and EC2 instances every week, and fixing this "one-day miracle" ultimately cost us months of debugging and rebuilding. In retrospect, it's really about the spec that was given that did not specify the ephemeral nature of the deployment that needed to be set up, this reflects the consequence of incomplete thinking from the developer, though the AI coding assistant did the necessary job.
In another instance, we hit a similar issue with an AI-generated Golang API service. A junior developer leveraged AI coding workflow produced clean, idiomatic code for handling HTTP requests for a key data ingestion system, properly reading request bodies through buffers as recommended by standard Go practices. The service worked beautifully in testing and early production. Quickly many more API endpoints were built using the same pattern. But as traffic scaled to thousands of requests per minute, we started experiencing mysterious memory spikes, crashes and API calls were getting dropped and there was loss of data. After months of service instability, deep memory profiling revealed the culprit: the AI had used a third-party library for buffer management that implemented speculative memory allocation, pre-allocating far more memory than needed for each request in anticipation of large payloads. At high RPM, these oversized buffers were creating massive pressure on Go's garbage collector, causing stop-the-world pauses that cascaded into timeouts and crashes. The AI had followed the library's usage examples perfectly but missed the crucial detail buried in the implementation - that this library was optimized for batch processing, not high-frequency API requests. This is both a detail the developer needs to implicitly understand and provide in the task specification, and also, a simple load test (or) memory profiling if were in-place early on would have avoided this disaster.
I have at least a dozen or more such stories (just from the last 90 days) dealing with the fires caused by the rush of AI development.
This is why I now insist on frontloading platform engineering before any AI acceleration. It sounds backwards - why build elaborate guardrails before you start moving fast? But here's the thing: in AI-native development, your platform isn't supporting your development, it IS your development. The platform becomes the key tool, context and guardrails that makes AI useful.
Let me be specific about what needs to be in place before you let AI loose on your codebase:
Your Non-Negotiable Foundations:
First, you need what I call "context infrastructure." This isn't just documentation - it's machine-readable, semantically rich information about your system architecture, business logic, coding conventions, design decisions, security practices, and PRDs. Every architectural decision, every business rule, every "we do it this way because..." needs to be encoded in a way that can be fed to an AI system. It could be as simple as keeping your README, Claude or Cursor artifacts up to date.
Second, you need guardrails at every level. A few concrete examples include:
Observability in every part of your system from Day-0.
Error and exception handling along with detailed logging.
Unit and integration tests, insist on having close to 100% code coverage at all the times.
Integrity checks especially on the health and quality of data assets (not just infrastructure).
Ability to have canary deployments.
Strictly following Infra as a code practice with zero config drift.
IDE-level health checks that trigger the moment AI generates code
PR-level quality gates for lint, type checks, code health, and architectural violations.
Security-level integration that catches vulnerabilities in real-time
Cloud security and data security practices. Principle of least privilege to be enforced strictly, especially to AI Agents in your company.
Third, and this is critical, make outcomes measurable from day-0. You need business, customer, product, operational, and engineering metrics all in place before you write the first line of code. This isn't just about monitoring - it's about creating the feedback loops that enable AI systems to learn and improve. Once a decision or function can be clearly represented through a metric and you have a way to repeatedly measure its trend over time, you can potentially create a self-improving AI system to optimize that function.
The process of defining metrics and automatically leveraging them in day-to-day coding workflows cultivates something deeper: holistic thinking across your entire stack. When every piece of code generation is automatically evaluated against infrastructure metrics (CPU, memory, latency), business metrics (customer impact, cost), and operational metrics (error rates, deployment frequency), developers naturally start thinking systemically. You're no longer just writing an API endpoint - you're considering its impact on database connection pools, cache hit rates, and customer experience scores. This is the hidden power of platform engineering foundations: they embed system-wide awareness into every AI-generated line of code. When a developer working on the application layer asks AI to optimize a function, the guardrails and metrics automatically ground that optimization in infrastructure realities - memory constraints, network latency, cost per transaction. The AI doesn't need to be explicitly told "don't exceed our memory budget" or "maintain p99 latency under 100ms" because these constraints are baked into the evaluation criteria. Platform engineering foundations don't just prevent disasters; they transform every developer into a systems thinker, whether they realize it or not.
Here's a practical example from our daily workflow: Security package and library upgrades. Every two weeks, we get a handful of vulnerability alerts requiring dependency updates. Initially, developers would manually upgrade packages, run tests, and hope nothing broke in production. Many times, upgrades would introduce subtle functional and performance regressions that we'd only discover weeks later.
We changed this by instrumenting our applications from day-0 with:
Memory usage profiles
Latency profiles (p50, p95, p99 for every endpoint)
CPU utilization patterns
Integration test success rates
Unit test coverage and execution time
Now when a security vulnerability is detected, our upgrade workflow has these well defined steps:
GitHub dependabot creates a PR with the package upgrade
Runs the full test suite
Deploy to a canary environment
Compare memory and latency profiles against the baseline
Flag any degradation beyond 5% threshold
Generate a detailed analysis of what changed and why, decide a final “go” or “nogo”
Currently, a human reviews these metrics and makes the final decision to merge. But because we have clear, measurable outcomes and months of historical data, we're confident that over time this can become a fully automated, AI-driven workflow. We can infuse these patterns into the AI agent - certain packages tend to increase memory usage, others affect startup time, some require configuration changes as the default values have changed, etc. Each cycle, it gets better at predicting and preventing issues.
The key insight: because we had these metrics from day-0, we could create an AI workflow that not only automates the mechanical task of upgrading but also ensures quality through measurable outcomes. Without these early instrumentation decisions, we'd be flying blind, hoping AI-generated upgrades don't break production. With them, we have confidence to progressively hand over more control to the AI system.
When we rebuilt our platform with some of these foundations, something remarkable happened. A recent security audit found that while our development speed had increased 300%, our average defect rate and architectural flaws had actually decreased by over 50%. This aligns with Red Hat's finding that organizations with mature platform engineering see 51% better security and compliance outcomes. Meanwhile, Apiiro's research warns that without proper guardrails, AI-accelerated development can increase certain vulnerabilities like privilege escalation paths by over 300%.
The lesson? In AI-native development, the platform engineering work you do upfront doesn't slow you down - it's the only thing that makes ever growing AI-speed reliable and sustainable.
