AI Engineering
12 posts tagged AI Engineering — engineering notes and field reports from the TechKis team.
Posts in AI Engineering
12 posts
- 9 min read·AI EngineeringArchitectureAPIs
MCP vs A2A vs HTTP APIs — what's actually different, and when each one wins
MCP, A2A and plain HTTP APIs solve overlapping but distinct problems. Here's what each protocol is really for, how they compose, and how to pick without cargo-culting the newest acronym.
Read post - 9 min read·AI EngineeringProductSaaS
Why every SaaS will have AI agents by 2027 — and what that changes about how you build
Agents are moving from bolt-on chat widgets to the primary way users get work done inside software. Here's the shift that's driving it, what an agent-native SaaS actually looks like, and what it means for your architecture and moat.
Read post - 9 min read·AI EngineeringArchitectureDeveloper Experience
Building AI features without LangChain — the raw-SDK path most teams should take first
You don't need a framework to ship an AI feature. Here's what the raw provider SDK actually gives you, the small amount of glue you write yourself, and when a framework finally earns its place.
Read post - 10 min read·AI EngineeringRAGRetrieval
RAG is not dead — you're just building it wrong
"Just use a bigger context window" doesn't kill RAG. Bad chunking, no reranking, and retrieval you never evaluate kill RAG. Here's what a retrieval pipeline that actually works looks like.
Read post - 9 min read·AI EngineeringMemoryArchitecture
AI memory explained — short-term vs long-term, and why the difference matters
"Memory" in AI apps means two very different mechanisms with different costs and failure modes. Here's what short-term (context) and long-term (retrieval/state) memory actually are, and how to design both.
Read post - 9 min read·AI EngineeringPrompt EngineeringTesting
Prompt engineering is becoming a software engineering problem
Clever wording was phase one. Production prompts now need versioning, tests, evals, regression tracking and CI — the same discipline as any other code. Here's what that looks like in practice.
Read post - 10 min read·AI EngineeringArchitectureBackend
How AI changes backend architecture — the parts that are genuinely different
Adding an LLM to your backend breaks assumptions that held for a decade: latency, determinism, cost per request, statefulness. Here's what actually changes and how to design for it.
Read post - 10 min read·AI EngineeringArchitectureEnterprise
The AI gateway pattern for enterprise applications
Letting every service call model providers directly is how enterprises lose control of cost, security and compliance. An AI gateway is the single seam where routing, auth, budgets, logging and guardrails live. Here's how to build one.
Read post - 10 min read·AI EngineeringObservabilityArchitecture
AI observability — logs, traces, cost and hallucinations
You can't run an LLM system on hope. Traditional observability misses the things that actually go wrong with AI: silent quality drift, runaway token cost, and confident wrong answers. Here's the observability stack an AI system needs.
Read post - 9 min read·AI EngineeringCostArchitecture
AI rate limiting and cost control — before the bill surprises you
One buggy loop or abusive user can turn an LLM feature into a five-figure invoice overnight. Rate limiting and cost control for AI aren't the same as for a normal API — here's how to cap spend without breaking the product.
Read post - 8 min read·Google I/OGeminiAI Engineering
Google I/O 2026 — what stood out to our team
Our take on the Google I/O 2026 announcements that actually matter for shipping AI-first software in production — Gemini, Android, web platform and developer tooling.
Read post - 9 min read·MobileReact NativeExpo
React Native vs Expo vs Flutter for AI apps in 2026
An opinionated comparison of React Native, Expo and Flutter through the lens of shipping AI-first mobile apps — streaming UIs, on-device inference, native bridges and the team-velocity trade-offs that actually decide it.
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