In the AI era, defining problems is often more important than solving them; here we explore AI's contradictions, boundaries, and possibilities.
The article explains why format constraints alone cannot guarantee correct content in LLM agents, compares JSON Mode, Structured Outputs, and Tool Calling, and provides a step‑by‑step engineering guide—including model‑specific quirks, schema validation, retry loops, and layered fallback strategies—to achieve robust structured results.
A comprehensive benchmark compares four open‑source PDF parsers—MinerU 2.5, Granite‑Docling, PaddleOCR‑VL, and XBerg 1.0—across six diverse document types, evaluating twelve accuracy dimensions and processing speed on GPU and CPU, and offers practical selection guidance based on the results.
This article compares LangGraph, Google ADK 2.0, and LlamaIndex on how they model, scope, update, persist, interrupt, and concurrently control state in agent workflows, providing concrete code snippets, tables, and practical guidance for selecting the right framework.
The article defines tool hallucination in LLM agents, analyses training bias, context pollution, loop feedback and dialogue inertia as root causes, and proposes multi‑layer defenses—including visibility control, intent verification, runtime gating, architectural isolation, and feedback loops—framed as a system‑engineering challenge rather than mere prompt tweaking.
ADK 2.0 introduces a graph‑based Workflow Runtime that separates routing from LLM inference, replacing Sequential/Parallel/Loop agents with deterministic nodes, cutting token usage by ~50% and latency by ~20%, while adding HITL, automatic retries, and a clear migration path for Python and Go users.
This report breaks down production‑grade Retrieval‑Augmented Generation (RAG) system costs into five modules—LLM inference, vector database, embedding, bandwidth, and infrastructure—revealing that model choice drives over 40% of expenses, quantisation can halve vector costs, and multimodal storage may outpace vector database spending.
The article compares traditional imperative sequencing of function calls with declarative workflow orchestration using state machines, highlighting how the latter separates business logic into data-driven transition tables to improve flexibility, observability, and error recovery in complex, mutable backend systems.
This guide walks through practical Git workflows—creating parallel worktrees, handling merge conflicts, stashing changes, inspecting diffs, navigating history with log and reflog, amending commits, using reset versus revert, cherry‑picking specific changes, bisecting bugs, and preserving empty directories with .gitkeep—tailored for AI coding tools such as Claude Code, Cursor, and Codex.
This article explains the distinction between control state and data state in AI agent frameworks, tracing the concept from Turing machines through operating systems, databases, and compilers, and shows how LangGraph separates these states via a three‑layer architecture, code examples, and design guidelines.
This note outlines a vendor‑agnostic Agent Development Lifecycle (ADLC) that extends traditional SDLC with five stages—Build, Test, Deploy, Monitor, and Govern—detailing layer‑wise tooling choices, evaluation strategies, deployment infrastructure, observability practices, and governance concerns for modern AI agents.
