India’s first GenAI unicorn shifts to cloud services as AI model ambitions face reality
Krutrim's pivot to cloud after layoffs and limited product updates reflects the economic challenges of building AI models in India.
InfoWorld AI·
Modern frontend applications rely on cloud services for far more than basic data fetching. Authentication, search, file uploads, feature flags, notifications and analytics often depend on APIs and managed services running behind the scenes. Because of that, frontend reliability is closely tied to cloud reliability, even when the frontend team does not directly own the infrastructure. This is often one of the biggest mindsets shifts for frontend engineers. We often think about failure as a total outage where the whole site is down. In practice, that is not what most users experience. More often, the interface is partially degraded: A dashboard loads but one panel is empty, a form saves but the confirmation never arrives, or a file upload stalls while the rest of the page still appears normal. That is why I think frontend resilience deserves more attention in day-to-day engineering conversations. The goal is not to prevent every cloud issue. That is rarely realistic. The more practical g
Read full articleKrutrim's pivot to cloud after layoffs and limited product updates reflects the economic challenges of building AI models in India.
APIs and MCPs are often mentioned in the same breath as ways that systems can exchange information, but they are designed differently and have different purposes. This article hopes to explain the differences and how software developers and users should approach interaction with each. An API is mainly found in software applications, while an MCP […] The post A guide to APIs, MCPs, and MCP Gateways appeared first on AI News.
April 22, 2026 — NVIDIA and Google Cloud have collaborated for more than a decade, co‑engineering a full‑stack AI platform that spans every technology layer — from performance‑optimized libraries and frameworks […] The post NVIDIA and Google Cloud Collaborate to Advance Agentic and Physical AI appeared first on AIwire.
NVIDIA and Google Cloud have collaborated for more than a decade, co‑engineering a full‑stack AI platform that spans every technology layer — from performance‑optimized libraries and frameworks to enterprise‑grade cloud services. This foundation enables developers, startups and enterprises to push agentic and physical AI out of the lab and into production — from agents that […]
As companies move from experimenting with AI agents to deploying them in production, one pattern becomes clear: capability without control is a liability. Agents operate in long-running, stateful environments. They browse the web, read repositories, execute shell commands, call APIs and interact with internal systems. That power is transformative — and it meaningfully expands the attack surface. In a recent interview, Jonathan Wall, CEO of Runloop, summarized the shift: “By default, agents should have access to very little. They need to do real work, but capabilities have to be layered on in a controlled way.” That framing reflects a broader industry reality: agent infrastructure must be designed around least privilege, explicit isolation and observable execution. What follows is a practical control architecture for production agents. The layered control model A resilient agent deployment combines six explicit layers: Strong runtime isolation with a microVM Restrictive network policy w
Oracle says its new Trusted Answer Search can deliver reliable results at scale in the enterprise by scouring a governed set of approved documents using vector search instead of large language models (LLMs) and retrieval-augmented generation (RAG). Available for download or accessible through APIs, it works by having enterprises define a curated “search space” of approved reports, documents, or application endpoints paired with metadata, and then using vector-based similarity to match a user’s natural language query to the most relevant of pre-approved target, said Tirthankar Lahiri, SVP of mission-critical data and AI engines at Oracle. Instead of retrieving raw text and generating a response, as is typical in RAG systems that rely on LLMs, Trusted Answer Search’s underlying system deterministically maps the query to a specific “match document,” extracts any required parameters, and returns a structured, verifiable outcome such as a report, URL, or action, Lahiri said. A feedback loop
Salesforce is packaging its developer and AI tooling, including its vibe coding environment Agentforce Vibes, into a new platform named Headless 360, designed to help enterprise teams build agent-first workflows. The CRM software provider defines agent-first workflows as enterprise processes in which software agents, rather than human users, carry out tasks by directly invoking APIs, tools, and predefined business logic. To support this approach, Headless 360 exposes Salesforce’s underlying data, workflows, and governance controls as APIs, MCP tools, and CLI commands, via its existing offerings, such as Data 360, Customer 360, and Agentforce, Joe Inzerillo, president of AI technology at Salesforce, said during a press briefing. This allows agents to operate directly on the platform’s existing business logic and datasets, rather than relying on separate integrations or user interfaces, Inzerillo added. Push to become a control layer for enterprise AI agents Analysts, however, see Headle
Explore how OpenAI products like ChatGPT, Codex, and APIs bring AI into real-world use for work, development, and everyday tasks.