Agentic RAG Architecture Patterns: When Plain RAG Is Not Enough
Agentic RAG architecture patterns for 2026: planner-executor retrieval loops, tool-augmented search, and when plain RAG breaks down.
Google Gemini 3.5 Flash Explained: Architecture, Benchmarks, and Deployment (2026)
Google Gemini 3.5 Flash explained: the MoE multimodal architecture, context window, real 2026 benchmarks, pricing, latency, and how it compares to GPT and Claude.
Agent Benchmarks in 2026: SWE-bench Verified, GAIA, and tau-bench
A deep dive into 2026 AI agent benchmarks: SWE-bench Verified, GAIA, and tau-bench — what they measure, how they leak, and how to read agent leaderboards honestly.
AI-Native PLM: How LLMs Are Reshaping Engineering Data
Why AI-native PLM is emerging in 2026: LLM copilots for BOM cleansing, requirements, and engineering search - and the data architecture that makes it work.
Fine-Tuning vs RAG vs Long-Context: A 2026 Cost/Quality Decision
A 2026 cost and quality decision record for fine-tuning vs RAG vs long-context LLMs: token economics, latency, accuracy trade-offs, and a decision matrix.
Agentic AI Security: Defeating Prompt Injection in 2026
An applied defense-in-depth pattern for agentic AI security: the indirect prompt injection kill-chain, OWASP LLM/Agentic Top 10, and layered mitigations.
The June 2026 Open-Weight Model Flood, Explained
In two weeks of June 2026, ~12 frontier open-weight models shipped — GLM-5.2, MiniMax M3, DeepSeek V4.1, Qwen 3.7. What it means for cost, moats, and strategy.
GLM-5.2 Benchmark: The New Open-Weight Leader (2026)
GLM-5.2 benchmark analysis: Z.ai's 753B MoE under MIT license, coding and agentic results vs GPT-5.5 and MiniMax M3, cost-per-token, and where it fits.
Corrective RAG and Self-RAG: Architecture Patterns (2026)
Corrective RAG (CRAG) and Self-RAG explained for 2026: retrieval grading, query rewriting, self-reflection loops, a reference design, and when each pays off.
LLM Semantic Router: An Inference Routing Pattern
The LLM semantic router pattern in 2026: route requests by intent and cost to the right model, with vLLM Semantic Router, embeddings, and a reference design.