You.com

🏗️ AI governance is being treated as a policy problem when it's actually an infrastructure problem. Teams write usage guidelines, add human review, restrict access, and still can't answer the most basic audit question when something goes wrong: where did this response come from? That gap is caused by retrieval logic that was never designed to be governed. Different teams, different sources, different ranking strategies, no shared layer, and no way to enforce rules consistently across any of it. The retrieval layer is where AI governance either works or doesn't. Web search APIs are the point in the stack where you can actually define what's accessible, standardize how it's ranked, and trace outputs back to their source. The pattern that compounds quietly. teams that defer this decision end up retrofitting governance onto infrastructure that was never built to support it, creating technical debt. The organizational debt—inconsistent behavior, unclear accountability—is even harder to unwind. Worth reading if you're thinking about what production AI systems need to be controllable, not just capable. 🔗 https://hubs.ly/Q04cTv_k0

Most teams building AI agents are doing it wrong—and benchmarks are starting to prove it. The industry default has been: "more agents = better results." Spin up a swarm, divide the work, parallelize everything. It feels like good engineering. 🔬 The data, however, disagrees. For sequential, high-reasoning tasks, multi-agent setups consistently underperform single-agent configurations at the same compute budget. Why? Coordination tax. Errors don't cancel out across agents—they cascade. At You.com, we leaned into this finding while building our Research API. Instead of orchestrating swarms, we built a harness for extreme single-agent inference scaling—up to 10 million tokens and 1,000 turns in a single session. The result: SOTA on DeepSearchQA. 83.67% accuracy. 93.16% F1. One design choice that made the biggest difference: treating budget as a first-class citizen of the reasoning loop—not a hidden kill-switch. When an agent knows its remaining compute from turn zero, it plans differently. It doesn't stop too early. It doesn't spiral. It manages its own research depth. The architecture question isn't "how many agents?" It's "how well can a single agent use what it's given?" Are you building with multi-agent setups because the evidence supports it—or because it felt like the right call two years ago? See what You.com Senior Research Engineer, Abel Lim, has to say. 👇 https://hubs.ly/Q04cKSL50

Building an AI company is one thing. Scaling it into the enterprise is another. Our CRO Peter Grant joined Revenue Brew—Morning Brew's new newsletter for revenue leaders—to discuss how You.com is evolving from AI-powered search to enterprise AI infrastructure. He also appeared on Make it Happen Mondays with John Barrows, diving deeper into revenue strategy, sales execution, and what it takes to win in B2B AI. If you're in sales, RevOps, or GTM, these are worth your time. ☕ Revenue Brew: https://hubs.ly/Q04b-8Kc0 🎙️ Make it Happen Mondays: https://hubs.ly/Q04b-N0l0

⏰ Most API evaluations end at latency. That's the wrong place to stop. A vendor's p50 benchmark may tell you how fast their system performs on a clean query, with warm cache, minimal load—but those conditions that don't exist in your production environment. What it doesn't tell you: how that number degrades when multiple requests run concurrently. What happens to your p99. Whether a 300ms response that hallucinated an answer actually saved you time—or just pushed the correction cost downstream. The frame most teams are missing is time-to-useful-result: how long does it take, end to end, from user intent to an answer someone can actually act on? That composite includes latency. It also includes recall accuracy, grounding rate, re-query rate, and integration overhead. None of those show up in a benchmark table. All of them show up in your production logs. The teams that make good API decisions test at their actual concurrency levels—not the vendor's demo. They build basic eval sets. They look at p99, not just p50. ⚡A faster result isn't always a better answer. Download the guide to learn more: https://hubs.ly/Q04cpTdK0

Pulse 2.0 sat down with our CTO, Saahil Jain, for an in-depth interview. Saahil joined You.com in December 2020 as a founding engineer and has been instrumental in building the search and AI infrastructure that now serves over 1 billion queries monthly. In the interview, he talks about the story behind You.com, our vision for AI search infrastructure, and where we're headed next. A great read for anyone building with AI agents or following the future of search infrastructure. 🔗 Link in the comments.

What does product management look like when AI can generate research, prototypes, and PRDs faster than most teams can write specs? Our CPO Saurabh Sharma sat down with The Way of Product to unpack exactly that. From his time at Google and OpenSea to leading product at You.com Saurabh shares: → Why the skills that made someone a great PM 5 years ago might not cut it anymore → How to find compounding advantage when features get commoditized → What "good" looks like for PMs building at the AI frontier Whether you're a PM, product leader, or building in AI, this one's essential listening. 🎧 https://hubs.ly/Q04b_5RQ0

Most AI assistants are confidently wrong about anything that happened last week. That's not a model problem—it's an architecture problem. The model is only as good as the data it can reach. And if that data stops at a training cutoff, your "AI-powered" workflow is just an expensive autocomplete running on stale context. The fix isn't complicated. An agent with real-time search doesn't just answer questions—it researches competitors, monitors regulatory news, verifies live pricing, then acts on what it finds. Creates the Jira ticket. Sends the Slack alert. Updates the CRM. No human in the loop for the retrieval step. A91I just published a walkthrough on wiring the You.com Search API into an autonomous agent via MCP—the same protocol standard that means this integration won't break when either platform ships updates. 🔌 The setup is genuinely 5 minutes. You.com API key (free tier, $100 credit, no card), one connection in A91I, and your agent has 93% SimpleQA accuracy on live web data—ranked #1 on DeepSearchQA for deep research. The use cases that actually ship: competitive intelligence digests to Slack, lead enrichment triggered by HubSpot events, news monitoring to WhatsApp. Not demos. Workflows. 🔗 https://hubs.ly/Q04bFyf10

Most teams pick their search API based on a feature checklist. The one that bites them in production is architecture. There are two fundamentally different API categories: 1️⃣ SERP APIs that return a URL and two sentences, leaving you to fetch, parse, render JS, and extract text before your LLM sees anything 2️⃣ AI-native APIs that return LLM-ready content in a single call. For agentic workloads, that difference compounds fast. A 500ms latency gap becomes five seconds across 10 tool calls. And if you're building RAG pipelines, "snippets by default" means a hidden extraction step most benchmarks don't include. The things that actually matter in production: content extraction architecture, full-page access per call, index independence, and framework integration depth. Staff Product Manager, Brian Sparker, broke down You.com, Tavily, Exa, Parallel, Perplexity, SerpAPI, and Serper—with pricing, latency trade-offs, and what each actually costs. 🔗 https://hubs.ly/Q04byGdY0

"The majority of searches in the future will not be by humans—it'll be by AI agents." What a week in San Francisco. Our CTO, Saahil Jain, represented You.com at two HumanX sessions—a Q&A and a panel on AI governance and the role AI search infrastructure plays. As we continue building the most accurate real-time web search APIs powering the agentic era, moments like these are a reminder of how fast the space is moving and why getting it right matters. See you next year, HumanX. 🤝

Getting a working agent is the easy part. Getting a good one requires a completely different system. You.com AI Engineer Patrick Donohoe built a recursive improvement pipeline for an equity research agent—one where the agent iterates on its own prompts, scores its own outputs against a six-dimension judge, and only ships changes that beat a high-watermark across 20 stocks. 15+ prompt versions later, here's what he learned: ⚖️ The judge is as important as the agent. If you calibrate it too generously, the agent has nowhere to go. If the rubric has shortcuts, the agent will find them—confident claims without citations, consensus framed as insight, generic analysis dressed up as specificity. You close those off one by one as they appear. ⛰️ Prompt iteration has a ceiling. Every plateau is telling you something: not that the prompts need more work, but that the agent needs new capabilities. Adding a DCF model, earnings call transcripts, and computation tools each unlocked step-change quality that no amount of prompt tuning could have reached. 🛠️ The infrastructure matters more than most teams realize. Versioned prompts, rollback on aggregate score decline, stored run history. Without all three, you can't tell whether you're improving or just changing things. Worth a read if you're building agents that need to do more than demo well. 🔗 https://hubs.ly/Q04b9jjy0