#production-ai
22 posts tagged with #production-ai
Every article below is hand-written, technically reviewed, and focused on production-ai. Posts cover real-world architecture decisions, code-level implementation patterns, and trade-offs you'll only discover after shipping production systems.
AI and Machine Learning AI Engineering Evals: Regression Gates for Prompts, Tools, RAG [2026]
Stop letting prompt tweaks and model upgrades silently break production. Here’s a CI-style regression gate system for prompts, tool calling, and RAG with golden sets, schemas, shadow evals, and failure budgets.
AI and Machine Learning AI Agent Control Flow Patterns [2026]: Retries, HITL, Checkpoints
A practical catalog of ai agent control flow patterns that actually ship: bounded retries, idempotent tools, durable checkpoints, human approvals, and debuggable replays.
![developer monitor terminal logs tracing — illustration for article on OpenTelemetry Instrumentation for AI Agents [2026]:](https://img.kunalganglani.com/images/vzekdneq/production/f126a9eb93656ea60adeae440cf4b74ed0b5fb93-1200x675.webp?auto=format&fit=max&q=75&w=600)
AI and Machine Learning OpenTelemetry Instrumentation for AI Agents [2026]: Ship It
A vendor-neutral tracing schema for AI agents: model LLM calls, retrieval, tool runs, retries, and token cost as spans. Then dashboard latency, error tax, and cost per successful task.
AI and Machine Learning Agent Per-Task Cost Calculation [2026]: Retries, Tools, Caching
A spreadsheet-ready expected-cost model for agent workflows that includes retries, tool-call fanout, context growth, and caching. Plus hard budgets you can actually enforce.
AI and Machine Learning Gemma 4 26B CPU Inference Benchmark: 5 tok/s Production Math [2026]
A $300 Xeon from 2013 runs Gemma 4 26B at 5 tok/s with no GPU. Here's the memory bandwidth math, quantization tradeoffs, and production decision framework nobody else is covering.
AI and Machine Learning RAG Context Window Limits: Why Bigger Is Not Better [2026]
Expanding your context window from 4K to 128K tokens doesn't fix RAG — it masks retrieval failures with coherent-sounding hallucinations. Here's the measurement framework that actually works.
Developer Tools AI Agent Cost Per Task [2026]: Token Budgets & Break-Even Math
Concrete per-task cost breakdown for Aider, Claude Code, and OpenHands — covering token overhead per PR, monthly burn at team scale, and the break-even formula for hosted APIs vs local models.
AI and Machine Learning Reduce LLM API Costs 60%: 6 Techniques [2026]
A technique-by-technique playbook with real cost math for cutting LLM API bills in production — covering semantic caching, prompt compression, model routing, batch APIs, and context tiering with 2026 pricing.
AI and Machine Learning Evaluate AI Agents in Production: 3-Level Framework [2026]
Most AI agent failures trace back to missing evals. Here's the 3-level framework — unit tests, LLM-as-judge, and online evaluation — that actually works in production.
AI and Machine Learning Weaviate vs Chroma 2026: Production Power or Local-First Speed?
I'd pick Weaviate for any production RAG system serving more than a handful of users, and Chroma for rapid local prototyping where zero-config setup matters more than scale. The fault line isn't features — it's operational maturity versus developer ergonomics.
AI and Machine Learning Fine-Tuning vs RAG vs Prompt Engineering: Decision Framework [2026]
Stop guessing which LLM technique to use. A 2026-updated decision matrix with real cost figures, concrete examples, and a clear flowchart for when fine-tuning beats RAG, when RAG beats both, and when prompt engineering alone is the right answer.
AI and Machine Learning AI Agent Latency Budgets: Performance Guide [2026]
Single-model TTFT benchmarks lie to agent builders. Here's the 6-tier latency budget framework for production AI agents in 2026, with real math for multi-hop tool calls.
AI and Machine Learning AI Agent Memory State Management Guide [2026]
Production AI agents fail silently without proper memory and state management. Here's the four-tier memory architecture, durable resumption patterns, and framework implementations in LangGraph, CrewAI, and raw Python.
AI and Machine Learning LLM Latency Benchmarks 2026: 6 Levers to Hit Sub-500ms TTFT
Real TTFT and throughput data across 10+ models, where latency breaks user experience, and 6 architectural levers to hit sub-500ms budgets in production without sacrificing quality.
![WhatsApp AI Agent: 5 Production Walls Beyond the Tutorial [2026]](https://img.kunalganglani.com/images/vzekdneq/production/a8e1fff5b7219759d1c9d5e9516ebd9af3c3e1c0-1200x675.webp?auto=format&fit=max&q=75&w=600)
AI and Machine Learning WhatsApp AI Agent: 5 Production Walls Beyond the Tutorial [2026]
The 30-minute WhatsApp AI agent tutorial gets 2,757 views/day. Here's what happens after the demo: rate limits, API costs, conversation state, ban risk, and the architecture that actually survives production.
![Context Engineering for AI Agents: 4 Pillars That Replace Prompt Engineering [2026]](https://img.kunalganglani.com/images/vzekdneq/production/47a44f2d48b832b9e464a06f3b5ef90e0e75e404-1200x675.webp?auto=format&fit=max&q=75&w=600)
AI and Machine Learning Context Engineering for AI Agents: 4 Pillars That Replace Prompt Engineering [2026]
Context engineering — the systematic management of what an AI agent knows, remembers, and can access at each step — is the discipline replacing ad-hoc prompt engineering in 2026. Here are the four pillars that make or break production agents.
![Generative AI vs Agentic AI vs AI Agents [2026 Compared]](https://img.kunalganglani.com/images/vzekdneq/production/63df1a29144d654964f01534f321758ae96835a7-1200x675.webp?auto=format&fit=max&q=75&w=600)
AI and Machine Learning Generative AI vs Agentic AI vs AI Agents [2026 Compared]
Everyone's told to 'go agentic' in 2026, but nobody agrees on what that means. Here's the concrete architectural breakdown — with a production decision framework.
AI and Machine Learning Google ADK Tutorial: Build Your First AI Agent [2026]
Google's ADK Python 2.0 is GA with 20K+ GitHub stars — here's how to build, harden, and deploy your first AI agent beyond the happy path.
AI and Machine Learning Netflix Headroom: How to Cut AI Agent Costs 10x in Production [2026]
Netflix open-sourced Headroom — a context optimization layer that slashes LLM inference costs by up to 10x. Here's how the architecture works and how any team can apply the same patterns.
AI and Machine Learning Building an AI Agent With OpenAI + LangChain: What the freeCodeCamp Course Teaches and What It Misses [2026]
The freeCodeCamp AI agent course is pulling 15,000+ views/day. Here are the 3 production gaps it leaves wide open — and how to close them before your agent burns $108/hour.
AI and Machine Learning Milvus vs Qdrant 2026: Which Vector DB Wins for Production RAG?
Qdrant wins for lean, fast RAG deployments where simplicity and filtering speed matter most; Milvus wins for large-scale enterprise workloads demanding billion-vector search and deep ecosystem integrations. Your stack size and ops maturity should make this an easy call.
AI and Machine Learning AutoGen vs CrewAI 2026: Which Multi-Agent Framework Actually Ships?
AutoGen wins for research-grade, dynamic multi-agent conversations and Microsoft ecosystem teams; CrewAI wins for structured, role-based pipelines that need to reach production fast. Here's the full breakdown.