AI AGENTS

9 min read

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

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

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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.

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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.

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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.

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

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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.

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

10 min read

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.

11 min read

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.

9 min read

AI coding agents become safer when permissions, logs, and rollback are designed as one system. Here is the operating loop I would put around any agent that can edit code, run tools, or open pull requests.

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Prompt injection stops being an abstract LLM risk once an agent can call tools. The practical defense is data boundaries, structured handoffs, tool guardrails, and approval gates around side effects.