AI Agents
Autonomous agents, LLM applications, and intelligent systems
GPT-6 Astra Guide: Architecture, Benchmarks, and Production Agent Patterns
An in-depth engineering guide to GPT-6 Astra. We analyze ARC-AGI-3 benchmark reality, computer-use protocols, token caching economics, and production agent loops.
What is Codex starts encrypting sub-agent prompts? A Practical Overview
OpenAI Codex now encrypts sub-agent prompts by default. This change affects how agentic systems share context between sub-agents. Here's what it means for your AI pipelines.
Building reliable agentic AI systems: A Practical Overview
A practical guide to building reliable agentic AI systems covering structured outputs, observability, fallbacks, and cost controls with real code examples.
How Harness Engineering Keeps Codex Agents from Breaking Production
Discover how harness engineering transforms autonomous Codex models into safe, production-ready developer agents using sandboxing, git worktrees, and cost tripwires.
What is My Agent Skill for Test-Driven Development? A Practical Overview
An AI agent skill for test-driven development automates the red-green-refactor cycle. Here's how it works, what it costs, and when to use it.
Skills vs MCP: When to Use Which in 2026
Skills teach the agent your workflow; MCP connects live systems. Here is when to use each — and the hybrid pattern HN devs settled on in 2026.
How to Build Reliable AI Agents in Production with Durable State
Learn how to build reliable AI agents in production with battle-tested patterns for durable state management, resumable workflows, and fail-safe execution.
Designing Multi-Agent System Architectures for Enterprise Workflows
How to orchestrate multiple specialized AI agents to solve complex, multi-step business problems. Explore communication protocols and handovers.
How to Test AI Agents with Robust Mocks and Integration Suites
Learn how to test non-deterministic AI agents in CI/CD with contract-based mocks, trajectory evaluation, and statistical Pass^k metrics.
How to Build Human in the Loop AI Agents with Flawless Handovers
Learn how to architect human in the loop AI agents with seamless handovers, suspended execution states, and operator-friendly approval interfaces.
How to Build Scalable Memory Architectures for Conversational AI Agents
Learn how to architect scalable memory architectures for conversational AI agents, from vector episodic recall to structured entity consolidation.
Devin vs Claude Code vs Copilot Workspace: AI Software Engineers Compared
Three tools claim to be AI software engineers. Here is an honest comparison of what each actually does well, what the benchmark numbers mean, and when to reach for each one.
Browser Agents: Automating Web Tasks With AI
Browser agents let LLMs control a real web browser to navigate, click, fill forms, and extract data. Here is how they work, when they are worth the cost, and when they are not.
Memory in AI Agents: Short-Term, Long-Term, and Episodic
Agents without memory repeat themselves, forget context, and fail on multi-session tasks. Here is how short-term, long-term, and episodic memory work and how to implement each.
Advanced RAG: Beyond Basic Chunk Retrieval
Basic RAG retrieves the wrong chunks and loses context across chunk boundaries. Advanced techniques including hybrid search, HyDE, re-ranking, and agentic retrieval fix these problems systematically.
AutoGen Framework Guide for Conversational Multi-Agent AI
Master Microsoft AutoGen with ConversableAgent patterns, GroupChat orchestration, sandboxed code execution, and production cost control strategies.
LlamaIndex RAG Framework Guide for Production Python Applications
Master LlamaIndex for production RAG pipelines with custom document readers, semantic chunking, vector indexing, sub-question query engines, and evaluation.
How to Evaluate AI Agents: Beyond Task Completion Rate
Task completion rate alone misses most of what matters in agent evaluation. Here is how to measure trajectory quality, cost efficiency, error recovery, and build your own eval suite.
Running AI Agents in Production: What Actually Breaks
Deploying agents to production reveals failure modes that benchmarks never show. Here is what actually breaks and the patterns that keep agents stable under real conditions.
Computer Use AI Agents: What They Can Do in 2026
Computer use agents can click, type, and navigate a real desktop. Here is what the technology can actually do, where it still fails, and when it beats a proper API integration.
AI Workflow Automation: Practical Patterns for Teams
AI workflow automation works reliably for some tasks and fails badly for others. Here are the patterns that work in 2026 and the rules for deciding when agents should propose versus act.
LangChain Complete Guide 2026: When to Use It and When Not To
LangChain is a powerful LLM framework, but its complexity is frequently criticized. Here is when it genuinely helps and when you should skip it entirely.
Tool Use in LLMs: Design Patterns for Reliable Agent Actions
Tool use is how LLMs take actions in the world. These design patterns make the difference between an agent that works reliably and one that hallucinates parameters and loops forever.
CrewAI Multi-Agent Systems and Role-Based Automation in Python
Master CrewAI in Python for role-based multi-agent teams. Learn agent roles, sequential and hierarchical processes, tool calling, memory, and cost control.
