AI Infrastructure
AI infrastructure is platform engineering with a new failure surface: non-deterministic models, unbounded token spend, tool access that can exfiltrate data, and latency profiles that break synchronous API assumptions. I treat inference like any other production dependency - routed, rate-limited, observed, and isolated by tenant.
These articles cover LLM gateway architecture, reliable AI API design on AWS, agent failure modes in production, prompt injection defenses for internal agents, and evaluating coding agents in CI without turning every PR into an unbounded cost center.
If you are standing up internal copilots, agent runtimes, or customer-facing AI features, read the pillar posts first. They emphasize blast-radius containment and operability over demo polish.
- LLM Gateway ArchitectureAn LLM gateway is the control plane between your applications and model providers. Here is the architecture I use to centralize routing, rate limiting, cost accounting, and observability for every inference call.
- AI Agent Infrastructure and Failure ModesAI agents combine LLM unpredictability with tool execution and long-running state - which creates failure modes traditional APIs never had. These are the ones I have seen in production and how I design around them.
- Prompt Injection Defenses for Internal AgentsSSO does not stop an injected Jira ticket from triggering a production runbook. Defense layers for internal AI agents-tool allowlists, context separation, approval gates, and blast radius containment.
Related writing
AI Infrastructure
AI Agent Infrastructure and Failure Modes
AI agents combine LLM unpredictability with tool execution and long-running state - which creates failure modes traditional APIs never had. These are the ones I have seen in production and how I design around them.
- AI Agents
- Reliability
- Security
Benchmarking
API Load Benchmarking for Platform Teams
Load tests that only report RPS miss the point. Platform teams need benchmarks that answer capacity questions, find breaking points before users do, and produce numbers engineering leadership can act on.
- Benchmarking
- APIs
- Performance
System Design
Designing a Highly Available API Platform
High availability is explicit failure modes with mitigations-multi-AZ compute, RDS failover, graceful degradation, and a failure matrix you actually game-day.
- AWS
- System Design
- APIs
Platform Engineering
Designing an Internal Developer Platform
An IDP is golden paths and self-service guardrails-not just a portal. Map developer journeys, ship templates first, and measure DORA metrics plus developer satisfaction.
- Platform Engineering
- Developer Experience
- CI/CD
AI Infrastructure
Designing Reliable AI API Infrastructure
AI APIs fail differently from CRUD APIs - provider rate limits, token budgets, and unpredictable latency need a dedicated reliability layer. Here is how I design inference infrastructure on AWS that survives real traffic.
- AI
- APIs
- AWS
Reliability
Designing Zero-Downtime Deployments
Zero downtime needs compatible migrations, readiness probes, and graceful shutdown-then choose rolling, blue-green, or canary based on blast radius and rollback needs.
- CI/CD
- Kubernetes
- Reliability
AI Infrastructure
Evaluating AI Coding Agents in CI
HumanEval scores do not predict whether an agent will break your migrations. A fixture-based eval harness, autonomy tiers, and CI gates that treat agent patches with the same distrust as human ones.
- AI Agents
- CI/CD
- GitHub Actions
Observability
How to Debug a Slow API in Production
When P99 spikes but errors stay flat, binary-search the request path with metrics and traces-starting at the edge, through the app, and into MySQL, Redis, and downstream dependencies.
- Observability
- SRE
- APIs
AI Infrastructure
LLM Gateway Architecture
An LLM gateway is the control plane between your applications and model providers. Here is the architecture I use to centralize routing, rate limiting, cost accounting, and observability for every inference call.
- AI
- Architecture
- APIs
Architecture
Multi-Tenant SaaS Isolation Patterns
White-label platforms like Edmingle sit on an isolation spectrum from shared RLS to dedicated clusters. A production-oriented guide to data boundaries, Kubernetes tenancy, noisy-neighbor controls, and tier migrations.
- SaaS
- Architecture
- Security
Observability
Observability for LLM Applications
Standard APM tells you the request was slow - it does not tell you why the model hallucinated or which prompt version caused a quality regression. Here is the observability stack I use for LLM applications in production.
- Observability
- AI
- OpenTelemetry
Platform Engineering
Platform Engineering in the Age of AI Agents
AI agents change what platform teams must provide - not just CI/CD and observability, but guardrails for autonomous, non-deterministic workloads. Here is how I expect platform engineering to evolve and what I am building toward today.
- Platform Engineering
- AI Agents
- Governance
Security
Policy as Code for Kubernetes Gatekeeping
Wiki standards do not stop :latest tags and root containers. A Gatekeeper and Kyverno playbook for admission control, audit-then-enforce rollouts, and CI integration that actually changes developer behavior.
- Kubernetes
- Security
- Policy
AI Security
Prompt Injection Defenses for Internal Agents
SSO does not stop an injected Jira ticket from triggering a production runbook. Defense layers for internal AI agents-tool allowlists, context separation, approval gates, and blast radius containment.
- AI Agents
- Security
- Platform Engineering
Security
Secrets Management for Platform Teams
Hardcoded API keys in .env files do not survive a team of ten, three environments, and a compliance audit. Here is the secrets management approach I implement for platform teams on AWS.
- Security
- AWS
- Platform Engineering
Networking
Service Mesh: When You Need It and When You Do Not
Istio demos look effortless at fifteen services. A production decision framework for mesh adoption-signals that justify sidecars, alternatives that do not, and incremental rollout without cluster-wide regret.
- Kubernetes
- Networking
- Architecture
Reliability
Webhook Idempotency in Production
Payment providers like Razorpay, Stripe, and PayU will deliver the same webhook more than once. If your handler is not idempotent, you will double-charge, double-enroll, or double-ship. Here is the pattern I use in production.
- APIs
- Reliability
- Payments
CI/CD
Why Do We Need Argo CD If We Already Have Jenkins?
Jenkins runs CI workflows; Argo CD continuously reconciles Kubernetes from Git. They complement each other-confusing them leads to drift, risky credentials, and painful rollbacks.
- GitOps
- Kubernetes
- CI/CD