Every team that ships with large language models eventually hits the same wall: performance flatlines even as prompts balloon, costs spike despite clever caching, and users complain that the model “forgot” the most important detail while clinging to a trivial aside; the fix, as it turns out, is not
Enterprises building AI agents have long stumbled at the final mile, where promising demos buckle under operational debt, inconsistent environments, and manual governance checks that slow deployment from months to quarters, and Google Cloud’s latest Vertex AI Agent Builder and ADK upgrades attempt
A Search Box That Starts The Work A routine query now triggers summaries, proposes next steps, and spins up multi‑step workflows that reach across systems many teams rely on every day, collapsing the distance between a question and a result that actually moves work forward. That shift arrived when
From Postgres Workhorse to AI Convergence: Why HorizonDB Matters Now Sudden spikes from chat-driven features and agent workflows reshaped what “production database” means, and practitioners across data platforms agreed that the center of gravity moved to places where vector search, governance, and
Daily code now ships with machine help woven into every step from planning to review and beyond, yet teams still pause before trusting it with the parts that matter most. The industry has moved past the proof-of-concept era into a pragmatic phase where assistants are expected to work inside real
As AI coding leaps from clever autocomplete to end-to-end planning, a fork of VS Code named Kiro asks whether specs should steer the work before a single line is written, promising faster delivery with fewer rewrites for teams drowning in drift. The question is not whether coding assistants are
