We share what we learn as we build , what works, what doesn't, and what's coming next. Written in plain language for business leaders and technical teams alike.
haal-lab.solutions / blog
Aug 22, 2026
18 min
Learn the 4-layer enterprise AI agent: Memory, Retrieval (RAG/Graph), Tools & LangGraph orchestration. Includes stack, code skeleton and build order.
Aug 21, 2026
14 min
Learn how a 2026 knowledge graph uses Neo4j, nodes and edges to store facts, why entity resolution is hard, and when it beats vector search.
Aug 21, 2026
13 min
Why does AI give half-answers? Learn how a RAG Graph uses LangGraph, decomposition and validation to self-correct and cut hallucinations.
Aug 20, 2026
12 min
RAG, RAG Graph, and Knowledge Graph are three different answers to three different problems, but the industry keeps using the terms interchangeably. This guide breaks each architecture down component by component, traces the same question through all three, and gives you a decision rule you can apply before writing a line of code.
Aug 19, 2026
14 min
Three AI agent tools — LangGraph, DeepSeek Harness, and Claude Cowork — answer different questions about where work happens and who controls the data. A practical guide to picking the right one, with production findings from systems we built ourselves.
Aug 17, 2026
14 min
Agentic workloads multiply inference calls, so per-token economics dominate total cost of ownership. We break down when small language models beat frontier models for agents, with real benchmark numbers, cost math, and the architectures we deploy for clients.
Aug 17, 2026
35 min
An in-depth research-level analysis of DeepSeek Harness architecture, performance benchmarks, ecosystem positioning, and strategic implications for the AI agent infrastructure landscape in 2026.
Aug 17, 2026
45 min
The honest technical comparison engineering teams need before committing to either stack. Real benchmarks, side-by-side code, production considerations, and the uncomfortable truths about both frameworks.
Aug 16, 2026
14 min
In 2026, the number one failure point of agent builds is no longer the model or the prompt — it is choosing the wrong retrieval architecture. We break down pipeline RAG, agentic RAG, and GraphRAG across correctness, latency, cost, and corpus shape, with a decision framework you can apply before writing a single line of code.
Aug 16, 2026
8 min
DeepSeek launched Harness, an open-source rival to Claude Code. If you're not a developer, that sentence tells you almost nothing. Here's what it actually means — and why it matters even if you never write a line of code.
Aug 15, 2026
14 min
Traditional APM tells you your service is up; it cannot tell you your agent started answering the wrong way. LLM observability is the discipline of measuring quality, not just uptime — traces, evals-as-monitoring, and drift detection, wired into the same pipelines you already run.
Aug 14, 2026
14 min
Hybrid local-plus-cloud LLM architectures went from experiment to default enterprise pattern. We share the decision framework, the actual cost model, and the routing designs we deploy — including the failure modes that convinced us to never run single-mode LLM infrastructure again.
Aug 13, 2026
14 min
Once your system makes hundreds of LLM calls per task, the prompt stops being the unit of design — the context window does. We explain the context engineering discipline we apply to production agents: attention budgeting, just-in-time retrieval, compaction, sub-agent isolation, and the failure modes each one introduces.
Aug 12, 2026
14 min
GPU price tags are the least of it. We walk through a complete TCO model for on-prem LLM inference — capex, utilization math, power, staffing, and model refresh — with a worked example and break-even analysis against API pricing.
Aug 11, 2026
14 min
Gemini-class million-token windows were supposed to make retrieval obsolete. After two years of shipping both long-context systems and RAG pipelines, our answer is a firm no — but the boundary moved. Here is the evidence, the cost math, and the decision rule we use.
Aug 10, 2026
14 min
The Model Context Protocol has become the default way AI agents connect to tools and data — and the default way attackers reach your systems through them. This guide maps the real MCP attack surface, from tool poisoning to confused-deputy abuse, and lays out a defense architecture that survives production.
Aug 8, 2026
14 min
Every team building on LLMs hits the same question: should this behavior live in the prompt, the retrieval index, or the weights? The wrong answer costs months. This guide gives you the knowledge-vs-behavior framework, honest cost tables for LoRA-class fine-tuning, and the hybrid stacks that actually ship.
Aug 7, 2026
14 min
Prompt injection was a curiosity when LLMs answered questions; it is an attack surface when they take actions. We cover direct vs indirect injection, why tool-using agents multiply the blast radius, and the layered defense architecture we actually deploy — including its honest limits.
Jul 18, 2026
12 min
An empirical comparison of Standard, SwiGLU, ReGLU, and GiGLU FFNs with a full reproducibility guide. When LLaMA, Mistral, and DeepSeek all use SwiGLU, does it actually matter at tiny scales?
Jul 18, 2026
18 min
An engineering analysis of production-ready runtimes, quantization formats, and retrieval patterns based on modern open-source approaches for organizations deploying open-weight LLMs on private infrastructure.
Jul 18, 2026
14 min
Cross-encoder rerankers are increasingly common in RAG pipelines, but they come with costs. Analysis of latency, recall, and compute tradeoffs across different production scenarios.
Jul 18, 2026
18 min
An engineering framework for treating prompts and model configurations as versioned, tested, and gated artifacts. Practical guidance for implementing automated evaluation pipelines that detect regressions before deployment.
Jul 18, 2026
15 min
A short catalog of agent topologies — router, planner-executor, critic — with notes on which ones held up under real tool-call latency and failure modes.
Jul 18, 2026
18 min
Building AI on your own hardware eliminates some risks and introduces others. A practical threat model for on-prem LLM systems, including model supply chain and prompt-injection surfaces.
No newsletter spam, no growth funnels. Just technical writing on the AI systems we are actually building , sent when there is something worth reading.
Where to find our technical writing and what we cover.
Yes. We publish technical articles on the systems we build , what worked, what didn't, and the reasoning behind the choices. Topics include local LLM inference, reranking tradeoffs, BGE-M3 in production, evaluation-driven CI, agent orchestration patterns, and private AI threat modeling.
Our blog articles are published on the Blog page at haal-lab.solutions/blog. We publish when we have something to say , no newsletter spam, no growth funnels.