Production-grade systems and the full picture. RAG, evals, cost, security, and long-running agents.
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13 min readA working architect's view of the 2026 LLM stack — the model tiers, inference providers, orchestration layers, evaluation tooling, and the trade-offs that actually matter when shipping production AI. Everything you wish someone had laid out before you started.
Use the article as decision context for adoption, risk, governance, or investment choices.
11 min readThe agent framework landscape in 2026 is more mature but no clearer. LangGraph, CrewAI, Pydantic AI, OpenAI Agents SDK, and direct API — each fits some teams and projects, none fits all. An honest comparison and a decision framework.
Evaluate the implementation pattern, failure modes, and guardrails before building.
13 min readThe most common production agent failure is infinite or pseudo-infinite loops — agents that retry, branch, and burn through tokens without making progress. The architectural patterns that prevent this and produce agents that finish, even on hard tasks.
Evaluate the implementation pattern, failure modes, and guardrails before building.
9 min readCursor, Copilot, Claude Code, and repository-aware agents change software work only when teams add boundaries. A practical workflow for codebase context, planning, tests, review, secrets, and production safety.
Design a repository-aware AI coding workflow that improves delivery speed without weakening review, security, tests, or ownership.
9 min readAI adoption should not be measured by how many people tried ChatGPT. A practical framework for measuring workflow ROI, quality, risk, maturity, and scale-readiness.
Measure AI adoption using workflow ROI, quality, risk controls, and maturity levels instead of tool usage vanity metrics.
9 min readMost teams should buy before they build, but not always. A decision framework for AI tooling, workflow automation, RAG, agents, privacy, integration depth, total cost, and strategic differentiation.
Decide when to buy, configure, extend, or build an AI system based on workflow fit, data control, cost, capability, and strategic value.
12 min readUse AGENTS.md, CLAUDE.md, Cursor rules, and CLI print modes so Codex designs, Claude reviews, and Cursor implements—without a custom orchestration platform.
Set up a portable Codex + Claude Code + Cursor workflow where design, review, and implementation hand off through markdown contracts and CLI runs.
10 min readA company knowledge assistant is only safe if retrieval respects permissions. How to design RAG source boundaries, ACL filtering, document ownership, logging, stale-source handling, and refusal behavior.
Design a company knowledge RAG with permission-aware retrieval, source ownership, leakage controls, and refusal behavior.
12 min readComputer use and browser agents have demos that go viral. Production deployments at scale have a different shape — narrow scoping, heavy guardrails, careful UX. The patterns that work, the failures we keep seeing, and the honest economics.
Evaluate the implementation pattern, failure modes, and guardrails before building.
12 min read1M-token context windows exist, but quality degrades long before that limit. Context engineering is the discipline of using context windows effectively — what to include, what to summarize, what to retrieve fresh, and the patterns that keep quality high as context grows.
Evaluate the implementation pattern, failure modes, and guardrails before building.
12 min readLLM inference costs are 60-90% reducible with the right techniques. Prompt caching, model routing, output control, batching, and a few less-known patterns. The numbers, the patterns, and the production discipline that distinguishes well-run inference from a runaway bill.
Use the article as decision context for adoption, risk, governance, or investment choices.
9 min readThe EU AI Act is not just a legal problem for large vendors. A practical SME plan for inventory, risk classification, human oversight, transparency, vendor records, and rollout discipline.
Create a practical AI governance baseline for an SME using AI tools, automations, or customer-facing systems in the EU.
13 min readMost eval suites look impressive but miss real regressions. Building evals that catch what matters requires careful dataset construction, sensitive metrics, judge calibration, and a culture of trust. The patterns from teams that get this right.
Evaluate the implementation pattern, failure modes, and guardrails before building.
13 min readLoRA fine-tuning has become accessible — you can run real fine-tunes on a laptop or rent a GPU for an hour. The patterns that work, the cases where fine-tuning beats RAG, and a practical end-to-end workflow from data prep to deployment.
Evaluate the implementation pattern, failure modes, and guardrails before building.
15 min readRun Claude Code, Cursor, and Codex against the same Linear project like a real engineering team: claim work, mark progress, hand off review, fix findings, and close issues without stepping on each other.
Set up a Linear-backed multi-agent coding workflow where Claude, Cursor, and Codex claim issues, work in parallel, review each other, and close work with an auditable trail.
14 min readBuilding a production Model Context Protocol server requires more than wiring up a few tools. The patterns for schema design, auth, error handling, streaming, observability, and the production realities that make MCP servers useful at scale.
Evaluate the implementation pattern, failure modes, and guardrails before building.
12 min readMost MCP tools we see are technically correct and practically useless. LLMs ignore them, misuse them, or call them in unhelpful ways. The principles for designing tools LLMs adopt naturally, with examples of common failures and their fixes.
Evaluate the implementation pattern, failure modes, and guardrails before building.
12 min readAgents need memory beyond the context window. Long-term memory architecture — what to store, when to retrieve, how to forget — determines whether agents feel like they 'know' you or start fresh every conversation. The patterns and the production trade-offs.
Evaluate the implementation pattern, failure modes, and guardrails before building.
12 min readLLM applications fail in unique ways that traditional observability misses. The patterns for tracing multi-step flows, tracking costs that vary 100x per call, monitoring quality drift, and debugging hallucinations at production scale.
Evaluate the implementation pattern, failure modes, and guardrails before building.
10 min readPrivate AI is not one architecture. A practical comparison of local models, enterprise SaaS, VPC deployments, self-hosted inference, and hybrid patterns for SMEs that care about privacy and control.
Choose a private AI deployment pattern based on data sensitivity, capability needs, cost, latency, and operational capacity.
10 min readAI systems usually fail in predictable ways: hallucination, stale context, sycophancy, prompt injection, unsafe tool use, schema drift, and weak fallbacks. A production failure-mode register for teams shipping real workflows.
Build a production AI failure-mode register with controls for hallucination, stale context, prompt injection, unsafe tool use, and weak fallbacks.
12 min readA production RAG pipeline is six stages, each with specific patterns that determine quality. The architecture, the choices at each stage, and the iterative evaluation discipline that distinguishes RAG that works from RAG that disappoints.
Evaluate the implementation pattern, failure modes, and guardrails before building.
14 min readPrompt injection is a permanent LLM security class, not a prompt-writing mistake. A production guide to threat models, data boundaries, tool permissions, regression tests, monitoring, and incident response.
Threat-model an LLM workflow and add concrete controls for untrusted content, retrieval, tool calls, authorization, monitoring, and incident response.
12 min readPrompting, RAG, and fine-tuning are the three big levers for adapting LLMs to your problem. Each is right for some problems and wrong for others. A framework for choosing, the realistic costs of each, and the production patterns where combining them shines.
Use the article as decision context for adoption, risk, governance, or investment choices.
12 min readProduction prompts are not 'tell the AI what you want'. They are a layered system — stable instructions, dynamic context, per-call variables — managed like code. The architecture, the patterns, and the discipline that distinguishes production from prototype.
Separate system, developer, and user instructions and test production prompts as versioned system components.
12 min readClassic chunk-based RAG has limits. Graph RAG, agentic RAG, and long-context RAG each break those limits in different ways. When each is the right tool, how they actually work, and the production trade-offs that matter.
Evaluate the implementation pattern, failure modes, and guardrails before building.
11 min readRAG quality starts before retrieval. A secure ingestion guide for PDFs, OCR, metadata, permissions, source freshness, deletion, malware risk, and operational ownership.
Design a secure document-ingestion pipeline for RAG with permission metadata, OCR quality checks, source freshness, retention rules, deletion behavior, and ingestion tests.
11 min readAt what scale does self-hosting beat API calls? The actual math, the operational realities, and the patterns that distinguish teams who should self-host from teams who should keep paying for managed inference.
Use the article as decision context for adoption, risk, governance, or investment choices.
13 min readLLM-powered products face economics that are harder than traditional SaaS. Variable costs that scale with usage, margins squeezed by inference, commoditization risk, and competitors with the same foundation models. How to build a product that's actually defensible — and the patterns that lead to LLM startups that disappear.
Use the article as decision context for adoption, risk, governance, or investment choices.
13 min readStructured outputs and function calling are the bridge from 'LLM that generates text' to 'system that does work'. In production, the patterns that matter are about schemas, error handling, idempotency, and graceful degradation — not just JSON mode.
Evaluate the implementation pattern, failure modes, and guardrails before building.
9 min readVoice agents are useful when the flow is bounded, the data is available, and the fallback is clean. A practical decision framework for Twilio/Retell-style systems, disclosure, handoff, testing, and rollout.
Decide whether a customer voice agent is appropriate and design the first rollout with disclosure, escalation, testing, and monitoring.
