How to use feature flags with AI systems
Feature flags are a standard tool for gradual software rollouts, but AI systems introduce dimensions that standard feature flag patterns do not handle well. Prompts, models, and inference configurations need their own flagging approaches.
How to handle rate limits in production AI systems
Rate limits are the constraint that most AI applications eventually run into. Building systems that handle them gracefully, rather than breaking when they appear, is a core production engineering concern.
Streaming AI responses: what changes in your architecture
Streaming AI responses (receiving output token by token rather than waiting for the complete response) changes the perceived performance of AI features dramatically. It also introduces architectural challenges that do not exist in standard request-response systems.
How to manage AI model upgrades without breaking production
Model providers update their underlying models regularly, sometimes without announcement and without changing the API version. The same endpoint that returned reliable outputs last month may behave differently today. Managing this risk requires different practices than managing software library upgrades.
How to build fallback chains in AI systems
AI systems fail in ways that traditional software does not. Model APIs go down, outputs fail validation, latency spikes, and costs spike. Fallback chains are the engineering pattern that makes AI-powered features resilient to these failure modes without requiring constant human intervention.
How AI systems accumulate technical debt differently
AI systems accumulate technical debt through mechanisms that traditional software engineering does not prepare you for. The debt is real, it compounds, and it shows up in ways that are harder to detect and more expensive to resolve than the technical debt most engineers are used to managing.
How to think about testing AI systems
Testing AI systems requires a different mental model than testing traditional software. The outputs are probabilistic, the failure modes are subtle, and the standard unit-test-to-integration-test pyramid does not map cleanly onto AI. Here is what a more useful testing framework looks like.
How to design AI systems that degrade gracefully
AI systems fail in ways that traditional software does not. The outputs are probabilistic, the failure modes are subtle, and the degradation is often invisible until it becomes a problem. Designing for graceful degradation is not optional for AI systems in production.
Why AI systems need version control for prompts
Prompts are the most frequently changed component of most AI systems, and most teams track them worse than any other code. No history, no rollback, no understanding of what changed between the version that worked and the version that does not. This is a solvable problem with known solutions.
How to run an AI proof of concept that actually transfers to production
Most AI proofs of concept succeed and most AI production deployments disappoint. The gap is not a mystery: POCs and production systems are built under different conditions, measured by different criteria, and staffed by different people. Closing the gap requires designing the POC differently from the start.
The AI infrastructure decisions that age poorly
Some infrastructure choices that seem reasonable when you are building your first AI feature become serious liabilities as the system matures. The teams that have been building production AI long enough have learned which decisions these are. Most teams learn them the hard way.
How to think about AI latency in product design
AI latency is not a single number and it does not behave like traditional API latency. The teams that design good AI-powered products understand what makes latency feel acceptable, what makes it feel broken, and how to design around the constraints that cannot be engineered away.
What good AI observability looks like
Traditional observability tells you if your system is up and how fast it is. AI systems need a second layer: is the output quality good, is it degrading, and why? The teams shipping reliable AI have built this layer. Most have not.
The AI skeptic's guide to getting value anyway
Healthy skepticism about AI is well-founded. A lot of what gets claimed about AI does not hold up. But wholesale skepticism is also a trap: a few specific AI applications genuinely change what is possible, and dismissing everything because some things are overhyped means missing those.
AI in regulated industries: what actually changes
The conversation about AI in regulated industries is usually framed as a conflict between innovation and compliance. That framing is wrong. The real constraint is not regulation but the specific requirements that regulation imposes, which are more tractable than they appear and sometimes work in AI's favor.
What makes an AI integration actually stick
Most AI integrations get adopted initially and abandoned quietly. The ones that stick share a set of properties that have less to do with AI quality and more to do with how the integration fits the workflow, builds trust, and earns a place in how people actually work.
How to evaluate AI tools before you buy
AI tools perform well in vendor demos. They perform less well when you run them on your actual data, your actual use cases, and your actual failure modes. The gap between demo quality and production quality is where most regrettable AI purchases originate.
The AI product manager: a new role taking shape
Building products with AI components requires product managers to develop new skills, own new responsibilities, and apply different judgment than traditional software PM work demands. The role is evolving faster than most PM playbooks have caught up.
How to sustain AI momentum after the first win
The first AI project is usually the easiest. It is cherry-picked, high-visibility, and benefits from novelty. What happens next is where most organizations stall. Sustaining momentum requires a different approach than generating it.
How to build an AI-ready data culture
Organizations that struggle with AI adoption often discover their real problem is data: not enough of it, not clean enough, not accessible enough, not understood well enough. The technical problems are usually solvable. The cultural ones are harder.
What the best AI teams actually do differently
Most organizations that struggle with AI adoption are doing the obvious things. They have access to the same models, the same tools, and the same information. The differences that matter are almost never the ones that get written about.
Why AI teams need a culture of evaluation
Evaluation infrastructure is a tooling problem. Evaluation culture is an organizational problem. Teams that build the tooling without changing how they make decisions discover that the tooling goes unused. The harder work is building the norms.
How to think about AI risk in your organization
Most organizations either overestimate AI risk (paralysis) or underestimate it (blind deployment). A calibrated approach to AI risk is not about building compliance frameworks. It is about understanding which failures actually matter and designing proportionate mitigations.
How to run an AI retrospective
Standard retrospective formats were designed for software development cycles, not AI systems. An effective AI retrospective reviews different dimensions, requires different data, and produces different outputs than a typical sprint retro.
The real cost of AI technical debt
AI technical debt accumulates differently than traditional technical debt and is harder to see until the costs become unavoidable. The shortcuts that look expedient in early AI deployments create compounding costs that most organizations are underestimating.
The second act of enterprise AI: what separates pilots from platforms
Most organizations have successfully run AI pilots. Far fewer have converted them into production platforms that deliver compounding value. The gap between pilot success and platform capability is not a technology problem.
The AI vendor due diligence checklist
Evaluating AI vendors with traditional software procurement criteria misses the risks that matter most. Here is what to ask about production reliability, data handling, model versioning, and vendor lock-in before you commit.
The case for boring AI
The organizations getting the most value from AI are not the ones deploying the most sophisticated systems. They are the ones deploying narrow, reliable systems that handle specific tasks predictably and at scale.
What AI systems need from product managers
AI features have requirements that traditional product management frameworks do not address well. The gap between PM practice and AI system needs produces poorly specified features, misaligned success metrics, and avoidable production failures.
Prompt engineering is not a substitute for system design
Prompt engineering is a real skill with real leverage. It is also the most commonly misused tool in AI development: applied to problems that require system redesign, not better prompts.
How to measure AI feature success
AI features fail in ways that standard product metrics miss. The user can be unhappy without the error rate going up. Quality can degrade without session length changing. Measuring AI feature success requires a different instrumentation strategy.
Evaluating LLMs for production: what benchmarks don't tell you
Public benchmarks measure what models can do under controlled conditions. Production performance depends on how models behave on your data, in your context, against your quality criteria. Here is how to build an evaluation that actually predicts production outcomes.
What to ask before buying an AI platform
Most AI platform evaluations focus on benchmark scores and feature checklists. The questions that predict whether a platform will work in production are different ones.
The hidden cost of context switching in AI workflows
Multi-step AI workflows lose information at every boundary. The handoff between steps is where accuracy degrades, latency compounds, and cost accumulates. Most teams do not measure it.
What separates AI teams that ship from teams that stay in pilot
Most AI pilots succeed. Most AI products don't. The gap is not technical capability. It is a set of organizational and process decisions that teams make before the pilot ends.
Why AI systems drift without contracts
AI systems degrade silently over time. Not because the model changes, but because the assumptions baked into the system (about inputs, outputs, and behavior) are never made explicit enough to enforce.
Per-tenant AI cost attribution: why aggregate dashboards are not enough
Aggregate AI spend tells you what you're paying. Per-tenant attribution tells you who's driving it, what you can charge for it, and which tenants are profitable. Building it is harder than it looks.
The observability debt in AI systems
AI systems accumulate observability debt faster than traditional software because the failures are probabilistic, latent, and compound before they surface. Here is what that costs when you actually pay it.
RAG vs fine-tuning: why the comparison mostly doesn't make sense
RAG and fine-tuning solve different problems. Treating them as cost-quality tradeoffs for the same job leads to expensive models that still don't know what they need to know.
Why your AI proof of concept works but your product doesn't
AI proofs of concept are optimized to demonstrate capability under conditions that don't hold in production. Here is what changes when the demo environment goes away.
The token budget problem: why your production agents run out of room
Context windows are finite. Production workloads are not. Here is what actually breaks when an agent exhausts its budget, and three patterns that prevent it.
AI workflow automation vs RPA: what actually changes
RPA automates what a human clicks. AI automation handles what a human decides. The distinction determines which workflows you can hand off and which ones will break.
Why long-running AI agents fail silently
Agents degrade before they break. Context pressure accumulates, attention patterns shift, and outputs drift, all without an error code. Here's how to detect it and what to do about it.
The case for structured outputs in production AI
Most AI systems in production are parsing prose from LLMs when they should be requesting structured JSON. The cost and reliability gap is larger than teams expect.
Multi-tenant AI: what you can't fake when you have 50 customers
Single-tenant AI hides bad architecture. Multi-tenant AI exposes it. Six things that compound across a tenant set and cannot be deferred.
Financial services AI: four constraints that reshape the architecture
Generic AI patterns break in financial services. Four constraints (audit, residency, adversarial input, risk asymmetry) reshape architecture from day one.
Most multi-agent systems are sequential pipelines wearing a costume
Most 'multi-agent' systems are sequential pipelines with role-play prompts. Three diagnostic questions to tell the difference.
What to instrument when your AI degrades in production
Most AI systems fail silently. Latency dashboards say 200 OK while quality drifts. Here is the four-layer telemetry stack that catches it.
Beyond Demos: Building AI Systems That Actually Work
Most AI projects fail in production. Here's why the gap between demo and deployment is where real engineering begins, and what production AI actually requires.
The AI Observability Gap: What You Can't See Is Costing You
Most AI systems run without meaningful monitoring. Learn the four dimensions of AI observability and how to build the monitoring infrastructure that makes optimization possible.