AI AGENTS

Fable 5 posts an 80.3% SWE-Bench Pro score and costs 2x Opus 4.8 - here is the task-profile scoring guide that tells you when the premium pays off.

The 2026 agent decision is not CrewAI vs LangGraph. It is whether your loop lives in vendor infrastructure, a self-hosted graph runtime, or a plain while-loop you wrote yourself. Here is how to choose.

A hands-on look at Mastra, the open source TypeScript framework for building production-ready AI agents and workflows -- with verified setup commands, honest tradeoffs, and current pricing.

Both Mastra and LangGraph.js are serious TypeScript agent frameworks - but they start from opposite philosophies. Here is what that means for your next project.

A practical comparison of OpenAI's Agents SDK and Anthropic's Claude Agent SDK - orchestration models, tool ecosystems, sandboxing, and how to choose the right platform for your team.

Four mature, production-ready TypeScript frameworks have made building agents genuinely enjoyable. Here is how to pick the right one - and how they fit together.

AI SDK 6 ships ToolLoopAgent and full MCP support. LangGraph hits 1.0 GA with durable state and built-in interrupt/resume. Here is how to choose between them for your TypeScript team.

Goose is a Rust-built AI agent with a CLI, desktop app, and API that runs against 15+ LLM providers and extends through 70+ MCP extensions - here is why developers are installing it.

OpenAI's harness engineering post and new token-use research point to the same lesson: agentic coding teams need token budgets, receipts, and eval loops, not vibes.

Headroom is a context compression layer that intercepts your AI agent's tool outputs and strips 60-95% of the tokens before they hit the model - with benchmarked accuracy preserved.

Anthropic's open-source vulnerability harness shows where AI security work is going: reproducible exploit loops, separate verification agents, and patch receipts.

Anthropic's Claude containment writeup points to the next security layer for coding agents: deterministic capability ledgers, not another approval prompt.

GitHub Trending is full of agent memory and context tools. The useful version is not magic recall. It is a context ledger: source-linked, scoped, expiring memory that agents can inspect and users can audit.

The ChatGPT for Google Sheets exfiltration report is not just a spreadsheet bug. It is a warning about agentic office tools: permissions need to be action-scoped, logged, revocable, and visible.

A huge Hacker News thread says domain expertise is the real moat in agentic coding. The sharper version: tacit judgment only compounds when you turn it into examples, tests, DSLs, and review gates.

Before an AI agent gets tools, files, APIs, MCP servers, or deployment access, decide what it can read, write, call, log, and roll back.

Mastra is the strongest fit when a TypeScript product needs agents, workflows, memory, tools, MCP, evals, and traces in one backend layer. It is not the right answer for every chat feature.

A practical field note on where Mastra, CopilotKit, and LangGraph fit when you are building the same agent-native product interface.

The AI coding market is noisy. The changes that matter are easier to spot when you separate model capability, editor loops, terminal agents, background agents, agent frameworks, UI layers, context, security, and cost.

If I were rebuilding my AI coding workflow on May 30, 2026, I would not pick one magic tool. I would pick a layered stack: terminal agent, editor, background agent, Mastra, CopilotKit, MCP, context, security, and cost controls.