All the articles with the tag "agentic-ai".
Service accounts were built for predictable software. Agents are different: they act for people, cross tools, and change context. Identity needs to follow the agent, the user, and the task.
The raw MCP server already worked. The useful step was building a curated integration facade: chores, Tesla, cameras, leaks, lights, and power, with writes behind gates.
OpenAI's new misalignment reporting framework is a useful signal for enterprise teams: if agents can act, they need traces, eval gates, ownership, and a boring incident loop.
LangChain moving MCP into langchain.mcp is useful because it removes adapter plumbing. It is also a reminder that easier tool access does not remove the need for identity, policy, and ownership.
AI can generate more code. That is not the bottleneck. The real work is removing integration, ownership, approval, and governance bottlenecks with a domain-driven MCP platform, APIM, and MLflow from mlflow.org.
The useful version of agent memory is not a bigger bucket. It is a layered system: compact truth, raw notes, topic summaries, and a local index that works even when embeddings do not.
Daniel Kokotajlo's AI forecasts look extreme until you separate the mechanism from the calendar. The direction is plausible. The 2027-style timeline is the part I struggle to buy.
Model Context Protocol can make agents useful inside large companies, but only if it is wrapped in identity, policy, audit, ownership, and a serious gateway architecture.
A useful personal agent needs more than prompts. It needs a workbench, QA, memory discipline, durable task running, evaluation, and docs that stay honest in CI.
Agents are the easy story. Changing the operating model is the hard one.
