Agrawal is constructing infrastructure and instruments optimized for AI brokers to entry, confirm, and organise internet knowledge. Merely put, he needs to vary how AI surfs the web by making a platform that’s constructed for AI, works in actual time, may be trusted, and scales simply. However is the concept new? What does it entail? And can it actually rework the net or stay a personal ecosystem? Mint explains

What’s Parag Agrawal’s story thus far?

India-born Parag Agrawal started his profession in 2006 as a researcher at Microsoft earlier than shifting briefly to Yahoo and later returning to Microsoft. In 2009, he joined AT&T’s analysis division, nevertheless it was at Twitter, which he entered in 2011 as a distinguished software program engineer, that his profession took off.

After six years, he rose to chief know-how officer and, in 2021, was named CEO. His tenure was reduce brief when Elon Musk acquired Twitter in October 2022, rebranded it as X, and ousted hundreds of staff, together with Agrawal and three different prime executives. The staff collectively sued Musk for $500 million in severance pay, which the latter has partly tentatively settled for now. Nonetheless, Agrawal and the opposite three senior executives proceed to pursue their very own claims in court docket.

Why is he within the information once more?

The IIT Bombay graduate just lately introduced the launch of his new firm’s “Deep Analysis API (software programming interface)”, which he claimed is “…the primary to outperform each people and all main fashions together with GPT-5 on two of the toughest benchmarks (the DeepResearch Bench and Browse Comp benchmarks present how effectively AI can dig up hard-to-find info and produce detailed studies)”.

In operations since final October, Parallel Internet Methods already powers “tens of millions of analysis duties day-after-day”. In response to Agrawal’s LinkedIn put up final week, “…among the quickest rising AI corporations use Parallel to convey internet intelligence immediately into their platform and brokers. A public firm automates historically human workflows, exceeding human-level accuracy with Parallel. Coding brokers depend on our search to seek out docs and debug points…”

What does it imply for customers and enterprises?

Not like Google or Perplexity, which serve folks with solutions or hyperlinks, Parallel is designed for machines. Its Deep Analysis API permits AI brokers to maneuver past surface-level searches, utilizing a number of analysis engines to ship something from fast responses to complicated, time-intensive insights.

Every consequence comes with attribution, confidence scores, and structured outputs, making the info each verifiable and machine-ready. For enterprises, this implies plugging the API into their very own AI techniques to energy duties like market evaluation, due diligence, buyer analysis, or aggressive intelligence. By prioritising traceability and reliability, Parallel is trying to sort out the issue of AI hallucinations. That makes it particularly helpful for sectors equivalent to finance, legislation, and healthcare, the place accuracy and belief matter most.

However how distinctive is Agrawal’s thought?

Agrawal is correct that at present’s internet nonetheless serves people: we click on hyperlinks, juggle tabs, evaluate costs, and choose credibility. AI techniques try the identical with unstructured knowledge, paywalls, and noise, limiting them to easy queries. His imaginative and prescient, although, isn’t fully new.

The programmatic internet has lengthy imagined reshaping the web so machines can work together with it immediately. The agentic internet goes additional, envisioning AI brokers that don’t simply fetch info however act on them— reserving flights, restocking groceries, or working evaluation. However not like Web3, which targeted on decentralised possession however by no means scaled, Parallel is an internet constructed for AI as its major person. With APIs that promise clear, verifiable, real-time knowledge, Agrawal is creating the primary critical infrastructure for this shift within the hope that enterprises can pay for it.

What about requirements and protocols?

Each main shift within the internet’s historical past has relied on requirements and protocols established by our bodies just like the World Huge Internet Consortium (W3C) and Web Engineering Job Pressure (IETF)—from hypertext mark up language (HTML) to the frameworks that make at present’s internet interoperable, permitting applications to speak with one another.

Within the early days of search engines like google and yahoo, as an example, corporations every used their very own indexing strategies till requirements round metadata and web site maps helped unify the ecosystem. Likewise, the rise of cellular apps pressured builders and machine makers to agree on protocols that allowed apps to work throughout platforms.

The programmatic internet, although, is a posh market dominated by opaque techniques and proprietary tech. It nonetheless runs on the inspiration set by our bodies just like the W3C and IETF, however stays a patchwork of open beliefs and closed industrial pursuits whilst requirements teams are attempting to rein it again.

Agrawal’s imaginative and prescient of a programmatic, machine-first internet, too, would wish frequent codecs for attribution, verification, and structured outputs to allow AI brokers to reliably share and interpret info throughout platforms, failing which his undertaking dangers changing into one other siloed ecosystem versus the transformative internet infrastructure he envisions.

What in regards to the AI bot downside?

Automated bots already make up near half of all web site visitors, performing duties equivalent to value comparisons and content material scraping, but additionally spamming or gaming techniques for advertisements and clicks. Additional, AI-driven bots can mimic human behaviour, be taught from their atmosphere, and evade detection.

A machine-first internet dangers amplifying these issues except it could actually distinguish between “good” AI brokers and malicious bots. Verification and attribution, which Parallel is constructing into its system, could assist by giving enterprises a approach to belief sure sources whereas filtering out noise. However how do you cease an AI-first web from changing into overrun by low-quality or adversarial site visitors? Search engines like google like Google already struggle fixed battles with website positioning spam; a programmatic internet like Agrawal’s Parallel may amplify that problem many occasions over.

So, what can we conclude?

For now, the excellent news is that buyers are supporting the concept. To this point, Agrawal has secured $30 million in funding from buyers together with Khosla Ventures, First Spherical Capital, and Index Ventures. Nonetheless, given the unhealthy bot downside and interoperability challenges, Agrawal’s AI startup won’t solely must construct infrastructure for AI analysis, but additionally guarantee governance and guardrails that stop the platform from being gamed.