2688 related articles

An in-depth look at the three-layer funnel architecture for agent intent recognition: rules for fast interception, context for routine intents, and LLM as fallback. Exploring the engineering trade-offs of accuracy, latency, and cost.

How Pinterest engineers built Medic for Apache Spark — a multi-agent auto-diagnosis tool — covering the evolution from a single ReAct agent, observability, log denoising, and end-to-end testing.

A deep dive into enterprise Agent engineering: long-running execution, HITL safety approvals, and event sourcing — with two real-world commercial projects for content ops and SRE.

Context engineering is the core methodology for building efficient AI Agents, covering query enhancement, RAG retrieval, prompt design, memory management, and tool invocation. Master Write, Select, Compress, and Isolate to solve LLM hallucination at its root.

A deep dive into engineering AI applications: from a simple chat page to a multi-layer Agent platform, covering RAG knowledge bases, Workflow scheduling, multi-model management, and run tracing.
Multi-Agent Collaboration: A GPT Team …
Explore multi-agent collaboration architecture: role division, communication protocols, coordination mechanisms, and how Workbench templates help developers build efficient AI agent teams.

How to handle Agent infinite loops? This guide covers three-layer loop detection, four strategy-switching techniques, root cause analysis, and multi-layer fallbacks for building stable, production-grade Agent systems.

Loop Engineering is an emerging AI dev paradigm where Agents iterate in controlled loops instead of one-shot outputs. Learn the 4-year evolution and what it means for developers.
Claude Is Mr. Meeseeks: The Disposable…
Using Rick and Morty's Mr. Meeseeks to explain Claude and AI agents: stateless execution, task atomicity, and multi-agent recursive failure risks. A deep dive for developers building better AI workflows.

LangChain launches Harness, Sandboxes, and Eval integrated into LangSmith, creating the first complete Agent engineering toolchain from development to acceptance testing.

A deep dive into Loop Engineering: how multi-agent collaborative dev systems achieve automated coding loops through workflow scheduling, step isolation, and validation.
GitHub Daily · July 18: 3D Reconstruct…
July 18 GitHub Daily: 3D reconstruction foundation model lingbot-map tops the charts, with AI engineering tooling, CLI Agents, and the MCP ecosystem exploding across the board.

AI agents are revolutionizing JS reverse engineering. This deep dive covers built-in tool chains, automation modes, prompt engineering for e-commerce, and full pipeline automation from parameter extraction to database storage.

A deep dive into Looping Engineering — covering the five core loop elements (Trigger, Goal, Judgment, Feedback, Memory), when to use loops, and a step-by-step guide to building a topic-selection loop with Claude Code.

A deep dive into the three-layer AI Agent evaluation framework — outcome, process, and system layers — covering trajectory evaluation, tool call accuracy, automated testing, and key engineering challenges.

Deep dive into Claude Code cloud sessions (Claude Cowork): build context repositories, assign minimum-privilege credentials, and orchestrate parallel AI agents as an engineering team.
Production-Grade LangGraph Template: A…
A deep dive into production-grade LangGraph templates covering state management, observability, error handling, and containerized deployment to bridge the gap from demo to production.

A deep dive into embedding a coding agent in Slack: core architecture, permission design, async task handling, and RAG context management for AI-powered dev workflows.
Assess Your Engineering Team's AI Agen…
How to quickly gauge your engineering team's AI Agent adoption level? This article breaks down a four-tier AI maturity framework covering tool adoption, workflow integration, governance, and measurement.
Deep Dive into AI Agent Skill Design: …
A deep dive into Skill design philosophy from Anthropic's Claude Code team and Perplexity's Agent team, covering the Tax Test, Gotchas Flywheel, progressive disclosure, and Eval-First practices for building high-quality AI Agent skill systems.