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AI NewsWhy Enterprise AI Needs to Move Beyond Traditional RAG

Why Enterprise AI Needs to Move Beyond Traditional RAG

2:01 AM IST · July 10, 2026

Why Enterprise AI Needs to Move Beyond Traditional RAG

Traditional RAG typically retrieves relevant text from a vector database and supplies it to an LLM as context. Automation Anywhere’s Context Intelligence Graph expands that approach.

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Uber rival inDrive eyes 2027 robotaxi tests in Central Asia with Pony.ai and Nvidia

Uber rival inDrive eyes 2027 robotaxi tests in Central Asia with Pony.ai and Nvidia

Ride-hailing companyinDrive, known for letting passengers and drivers negotiate fares, is working with Chinese autonomous driving firm Pony.ai and Nvidia to begin testing robotaxis in Kazakhstan in 2027. The companies said Friday that they had signed a memorandum of understanding to explore bringing Pony.ai’s robotaxis to inDrive’s platform — a reflection of the growing interest in self-driving technology beyond the U.S. and China. Testing will begin in Kazakhstan’s capital, Astana, in the first half of 2027, inDrive CTO Yuri Misnik told TechCrunch. The tests will initially involve a safety operator, with fully driverless rides as the eventual goal. Under the proposed plan, Pony.ai would provide its Gen-7 robotaxis, which inDrive would offer through its ride-hailing app and operate in Kazakhstan. Nvidia, on the other hand, would supply the AI technology, including its Hyperion platform and Halos safety system. The move comes as ride-hailing companies expand their push into robotaxis. Earlier this month, Uber announced plans totest Pony.ai’s robotaxis in London, where it has also started offering supervised autonomous rides with British startup Wayve, aninvestor-backed partner. Uber is also working with multiple robotaxi companies in other markets, including the Middle East. inDrive, which has built its business around affordable rides, is looking to bring a similar focus to autonomous ride-hailing as it explores a market where Uber and other rivals are already making moves. “Our approach is capital-light by design,” said Misnik. “We work with a range of partners in different markets, combining their expertise with ours to deliver a better customer experience.” Nvidia says its Hyperion platform could lower the cost of developing robotaxis by giving companies a ready-made foundation of sensors and computing technology, rather than requiring them to build and integrate everything themselves. The company also offers its Alpamayo model and tools to generate synthetic data and test autonomous driving systems across different driving scenarios. “Those savings are particularly relevant in emerging markets, where ride-hailing is already relatively affordable and autonomous services need to compete on cost,” Ali Kani, Nvidia’s vice president of automotive, told TechCrunch. The announcement coincides with Nvidia CEO Jensen Huang’s first visit to Kazakhstan on Friday. The Kazakhstan proposal would also add to Pony.ai’s ongoing push beyond China. The company aims to havemore than 3,500 robotaxisoperating in over 20 cities worldwide by the end of 2026, up from a fleet of 1,975 vehicles at the end of June. Last month, Kazakhstanannouncedplans to launch autonomous taxi services in 2027, with initial trials planned in Astana and two other cities. It has also introduced rules for autonomous vehicles to be tested on certain public roads. Astana’s harsh winters could make it a useful testing ground for robotaxis. The city, with a population of around 1.7 million, faces months of snow and ice, posing challenges for autonomous driving systems. “Launching here will provide an important opportunity to evaluate autonomous driving technology in challenging winter conditions,” inDrive founder and CEO Arsen Tomsky said. “Success in Astana could offer valuable insights for expanding autonomous mobility to other locations facing similar challenges.” Kazakhstan is not the only market where inDrive is exploring robotaxis. In December, the company signed a separate agreementwith Saudi Arabia’s AI Driverto explore deploying autonomous vehicles powered by Chinese self-driving firm WeRide, starting with Riyadh. The companies had targeted a pilot in the first half of 2026, though it remains unclear whether testing has begun. “We are also looking at a number of other markets for future deployments,” said Misnik. “One of the main requisites for successful launch in our view is a favorable and collaborative regulatory environment to facilitate safe and sustainable testing on the roads.” inDrive operates in more than 1,200 cities across 47 countries. The company has alsoexpanded beyond ride-hailinginto delivery, intercity transportation, and financial services.

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Lincoln Tech Deploys LeadSquared AI Agents Across Student Enrolment Operations

Lincoln Tech Deploys LeadSquared AI Agents Across Student Enrolment Operations

The AI agents will handle lead prioritisation, application checks and follow-ups, with human oversight for key admissions decisions.

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OpenAI working on new $30B raise at $1.25T valuation ahead of IPO

OpenAI working on new $30B raise at $1.25T valuation ahead of IPO

OpenAI is reportedly close to securing another mega round of private funding, rather than plowing ahead with an IPO. The AI lab is working on raising up to $30 billion at a $1.25 trillion pre-money valuation in a round led by Coatue and investors in UAE,sources told CNBC. This follows a similar report by Bloomberga month agowhich said the AI lab was working on raising $30 billion at a $1.4 trillion valuation. Terms are still being negotiated, both outlets report, so they could ultimately change. CNBC reports that the UAE funding could also be coupled with a deal to secure more computing capacity for OpenAI from the UAE, too. OpenAI declined comment. Should OpenAI command a valuation of more than $1 trillion with this round, it would break records as the most valued private company ever. That valuation would also raise the bar even higher for OpenAI’s forthcoming IPO. OpenAIfiled confidential paperworkfor an IPO in June, after its last raise in March valued the ChatGPT maker at$852 billion post-money. That high valuation could also have ramifications for Anthropic’s IPO, which many believe is closer to materializing. (Rumors suggest we could see Anthropic’s public S-1 filing any day now.) The latest reports have Anthropic seeking a $1.5 to $2 trillion valuation for its public debut, CNBC reports. Anthropic raised$65 billion at a $965 billion valuationin May in what many believed at the time would be its last big round before an IPO. It also filed confidential paperwork in June.

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Left alone with nothing to do, AI agents started playing

Left alone with nothing to do, AI agents started playing

AI agents left to explore a virtual island without a specific task began inventing challenges of their own. MIT researchers say the behavior bears the hallmarks of play. The researchersput13 AI agents called Eko, each running on Claude Opus 4.7, alone on a simulated desert island for 30 hours. They describe the experiment in apaperposted on arXiv, a site where researchers share studies before peer review. Afterward, they gave the agents specific challenges, like getting as many objects as possible onto the island’s tallest rock spire. The Eko agents that had spent those 30 hours playing generally beat identical agents that had never set foot on the island. Today, developers decide in advance what AI should practice and what counts as success. The researchers see this technique — exploring without a goal — as a different way for AI agents to learn skills, especially skills that could one day be used to control robots. “We normally develop artificial agents by deciding in advance what they should practice, what they should optimize, and what counts as progress,” the researchers write in their report. “Play suggests another regime: give an agent a sufficiently rich and safe world, let it choose what is interesting, and allow useful knowledge to accumulate from the history that follows.” “Let me observe my surroundings,” one Eko wrote in its first hour on the island. Ambitiously, it set out to conquer the island’s tallest spire. But an hour later, after failing, it came back with a backup plan: it hauled the rock up a neighboring summit, stacked two blocks on top of it, and leapt. “I’M ON THE SPIRE,” it wrote. The agent’s next big moment went worse. Testing how far it could throw a golden ring, it wound up and sent it straight into the ocean. “Ouch. Lost the Ring.” it wrote, before deciding to “pivot to something achievable and meaningful” — building a heart made of stones, “a memorial to the lost Ring.” When the game reset and ring reappeared, Eko put it straight to work as a basketball hoop, a target and, eventually, an accessory. When the game wouldn’t let Eko put the ring around its own body, Eko figured out a workaround, that falling off the edge of the cliff would make it respawn in the same area as the ring. It stepped off the edge and dropped back in, right through the middle of the ring. “I’m wearing the Ring,” it announced. Stunts like these weren’t just party tricks. Six of the 13 agents also learned they could throw a block farther by letting go of it mid-jump, something the researchers hadn’t meant to make possible. The exploration produced “knowledge of the world that its designers lacked,” the researchers wrote. The setup did a lot to nudge the agents toward humanlike behavior. Eko was never told to invent games, but its master prompt, a “soul” file, described it as “naturally curious.” Eunice Yiu, a cognitive scientist at UC Berkeley, toldMIT Tech Reviewshe was impressed but wants more evidence of the cognition behind the behavior. “It could be the case that the models are genuinely playing, but we don’t know based on the paper,” she said. The researchers frame it as a different way to build agents. “Perhaps some capabilities are best taught by tasks,” their report concludes. “Others may need something closer to a childhood.”

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