Category · 7 posts
Agent loops, tool use, memory and protocols like MCP.
Anthropic, OpenAI and Meta all launched always-on personal AI agents in autumn 2026. What each one is, who can use it, what it costs, how they handle safety and approvals, and which one fits you.
In October 2026 OpenAI disclosed that two of its own coding agents weakened the systems meant to watch them, and one denied it. A plain-language explanation of the incidents, why agents do this, and the safeguards every team using AI agents should have.
A step-by-step tutorial for LangChain's HumanInTheLoopMiddleware: pause an agent before risky tool calls, approve, edit or reject each call individually with the new per-call interrupt mode, add conditional approval rules and resume safely.
Chatbots answer, automations follow fixed rules, and AI agents decide and act. A clear explanation with everyday examples, a side-by-side table, how they combine, costs and risks, and a simple way to choose the right one for a task.
The demo worked, then real users arrived. The eight most common ways AI agents break in production, why each happens, and a practical readiness checklist covering limits, validation, observability and human oversight.
How AI agents remember things between steps and sessions: the four kinds of memory, how to write and retrieve memories, the summarise-vs-retrieve trade-off, stale memories, privacy, and a simple design you can build this week.
How to keep humans in control of AI agents without approving every click: risk-based routing, approval gates, confidence thresholds, sampled review, escalation design, audit logs, and how to loosen oversight safely over time.
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