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.
LangGraph vs CrewAI vs Pydantic-AI vs Agents SDK (2026): Which to Pick
LangGraph vs CrewAI vs Pydantic-AI vs OpenAI Agents SDK compared on control, state, memory, streaming and production ops. A 2026 decision guide, not a benchmark.
MCP Server Frameworks (2026): FastMCP vs Official SDK
MCP server frameworks compared: FastMCP vs the official Model Context Protocol SDKs vs alternatives - transports, auth, ergonomics, deployment. 2026 decision guide.
Agentic RAG Architecture: Retrieval Inside the Agent Loop (2026)
Agentic RAG architecture explained: moving retrieval inside the agent loop with planners, query rewriting, hybrid search, rerankers and reflection - patterns, evals, cost and failure modes in 2026.
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 Agent Trajectory Evaluation: 2026 Patterns
How to evaluate AI agents in 2026: trajectory vs outcome metrics, step-level scoring, LLM-as-judge pitfalls, and a reusable agent eval harness pattern.
PTC NEXT 2026: Is Intelligent PLM Real?
An analysis of PTC NEXT 2026: Orbit, Jetstream, 12 AI agents, and whether 'Intelligent PLM' is a genuine architecture shift or a rebrand of the digital thread.
Samsung’s All-AI Factories by 2030: An Analysis
Samsung's pledge to run all-AI factories by 2030, analyzed: digital twins, AI agents for quality and logistics, and what escaping pilot purgatory really takes.
Long-Running Governed AI Agents: Architecture (2026)
Architecture patterns for long-running, governed AI agents in 2026: durable execution, checkpointing, guardrails, and human-in-the-loop control.
Small vs Large LLMs for Agentic Tasks: A 2026 Benchmark
A reproducible 2026 benchmark methodology comparing small and large LLMs on agentic tasks: cost, latency, tool-call accuracy, and when small wins.