All the articles with the tag "agentic-ai".
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.
First impressions from DTW Ignite 2026 in Copenhagen: agentic AI is all over the agenda, but telecom still has the awkward problem of making the economics work.
A useful personal AI agent is not one clever prompt. It is the boring operational loop around memory, recovery, context handoff, scheduled work, and cleanup.
Most enterprise AI strategies are still too model-first. The real leverage comes from designing an agent loop that can survive contact with reality.
Most agent products do not break on model quality first. They break on messy context, brittle tools, fake evals, weak recovery paths, and sloppy release discipline.
