Coder Explained: Secure Environments for Devs and Agents
What Coder is, how it provides governed cloud workspaces for developers and AI agents, and why enterprise agents need this layer.
Agent Daily News
DeerFlow Explained: ByteDance's SuperAgent Harness
What DeerFlow is, how ByteDance built an open-source SuperAgent harness for multi-hour tasks, and what 'harness' means for agent infrastructure in 2026.
Dify Explained: The Visual Agent Workflow Platform
What Dify is, how its visual workflow builder works for agent development, and where it fits in the agentic AI stack in 2026.
LangChain and LangGraph Explained: The Agent Framework Stack
What LangChain and LangGraph are in 2026, how LangGraph's graph-based agent orchestration works, and when to use them vs newer alternatives.
LiteLLM Explained: The Open-Source Model Gateway for Agents
How LiteLLM works as an open-source proxy for 100+ LLM providers, with routing, cost tracking, and failover for agent stacks in 2026.
Mastra Explained: The TypeScript-First AI Agent Framework
What Mastra is, how the Gatsby team built a TypeScript-native agent framework, and why it matters for JS/TS developers building agents in 2026.
n8n Explained: AI Workflow Automation for Agent Builders
What n8n is, how its 70+ AI nodes enable agent workflows, and when to choose it over Dify or code-first approaches for building AI automation in 2026.
SGLang Explained: The Low-Latency Inference Engine for Agents
How SGLang works, why RadixAttention gives agents faster prefix reuse, and when to choose it over vLLM for production inference in 2026.
vLLM Explained: The Inference Engine Behind Agent Stacks
How vLLM works under the hood, why PagedAttention matters for agent workloads, and where it fits in a production agent infrastructure stack in 2026.
Warp Explained: The Agentic Development Environment
What Warp 2.0 is, how it evolved from AI terminal to agentic development environment, and why it matters for developers working with coding agents in 2026.
Why Autonomous AI Agents Need Secure Sandboxes
Autonomous AI agents that run code and shell commands need isolation. Why sandboxes are non-negotiable in production, the isolation levels, and how to choose.
Agent Observability: Logging, Tracing & Debugging
How to debug AI agents in production with structured logging, distributed tracing, and span-level cost tracking. What to capture and what to ignore.
5 Agent Design Patterns for Robust, Cheap AI Systems
Five agent design patterns for reliable, low-cost AI systems: ReAct, Plan-and-Execute, Reflection, Router, and Tool-First, with trade-offs for each.
Build a Custom MCP Server for Data Analysis in 30 Minutes
Build a custom MCP server that lets any AI agent run data analysis on your CSVs and databases. A complete, runnable TypeScript walkthrough.
Inside OpenHands: How an AI Coding Agent Executes Code
A teardown of how OpenHands, the open-source AI coding agent, plans, edits files, and runs code in a sandbox: the event-stream and action-observation loop.
MCP vs Function Calling: Which Tool Integration to Use
MCP vs function calling for AI agents: they solve different layers of the same problem. When to use each, how they compose, and the token-cost trade-off.