Enterprise agentic AI is seeing massive growth. The enterprise agentic AI market is now expected to grow from $2.6 billion to $24.5 billion by 2030, increasing more than 9x. These systems can effectively reason and execute with minimal human oversight. By 2028, 30% of enterprise applications are expected to embed autonomous agents, per Gartner forecasts. Adoption is expected to expand across customer service, operations, finance, HR, and cybersecurity. The biggest shift is that AI is moving from simply assisting humans to actually executing and orchestrating work. This could materially reduce operating costs and improve operational efficiency. Agentic AI is a major catalyst for corporate efficiency.

Early stage enterprise sales is all about moving from Promised based Aura to Performance Based Aura @rox_ai grew from 0 to 8 figures in just 7.5 months and raised $80M+ from Sequoia, GV, and General Catalyst. For episode 2 of @Distributionpod, @subahwadhwani and I sat down with @ishanmkh to unpack how they’ve sold into some of the largest enterprises. Today, they announced their self-serve motion, Rox Teams. Before Rox, Ishan scaled New Relic's self-serve business to $200M in ARR. Now he's building the next-gen CRM to take on Salesforce, but with zero self-serve, pure top-down enterprise sales. Here's what I'm taking away on landing enterprise deals: 00:00 Intro 06:05 Don't lock your product behind a "contact sales" form. 20:53 Your first customers shouldn't be picked for revenue. 28:55 Only 5-10 people actually run massive companies. Find them. 39:05 Pick 1-2 marketing channels and dominate them. 57:22 Run your book of business like you're the CEO. This episode is a cheat code for anyone selling into enterprise

Rox has been powering Global 2000 enterprises with $5T+ in combined market cap. Today, we’re putting it in everyone’s hands. Over the last 2 years, companies like @MongoDB, @togethercompute, and @Xbow shifted investment from legacy CRM and SaaS to Revenue Agents. But most teams have been locked out. They lacked in-house technical talent or FDEs required to set up the revenue-specific context, harnesses, and agent systems needed to run Revenue Agents in production. Today, we launched Rox Teams to remove that barrier. With Rox Teams, businesses of every shape, size, and vertical can activate Revenue Agents on their own. Set up in minutes. No FDEs required. & Nolaned this hilarious explainer video 👇

Rox has been powering Global 2000 enterprises with $5T+ in combined market cap. Today, we’re putting it in everyone’s hands. Over the last 2 years, companies like @MongoDB, @togethercompute, and @Xbow shifted investment from legacy CRM and SaaS to Revenue Agents. But most teams have been locked out. They lacked in-house technical talent or FDEs required to set up the revenue-specific context, harnesses, and agent systems needed to run Revenue Agents in production. Today, we launched Rox Teams to remove that barrier. With Rox Teams, businesses of every shape, size, and vertical can activate Revenue Agents on their own. Set up in minutes. No FDEs required. & Nolaned this hilarious explainer video 👇

We spent 11x more tokens on reasoning and got 0% better answers on revenue data ... until we added a knowledge graph. TLDR: our latest research shows that improving data representation increases agent retrieval accuracy more than upgrading the model. We ran 3,100 runs across 8 models answering revenue questions, like deal amounts, contacts, and identifying customer champions. We compared two ways of storing the data: 1. a normal database (SQL) VS. 2. a knowledge graph (relationships pre-mapped) Frontier models hit 8.9% accuracy on the questions using SQL over a relational schema. Cranking Claude Opus 4.8’s reasoning effort from minimum → maximum accuracy did not help. However, swap raw Salesforce data for a knowledge graph built on lakehouses like @databricks, @Snowflake, @googlecloud's Big Query, or @Azure Data Fabric ... and accuracy jumps from 8.9% to 99.9% - even using a 27B open-weight model at 1/20th the cost. This research shows throwing more compute at your agent cannot fix bad data structure. And is proof a revenue-specific knowledge graph is key to making revenue agents work at scale.

@AnthropicAI and @OpenAI want your product living inside Claude or ChatGPT. @Google wants the model to redraw your UI from scratch. Both are wrong about who should own the pixels, since it diminishes beautiful product experiences to basic text. We measured a 3rd option: up to 100x cheaper, and your product stays whole. → Introducing Rox Tether: An alternative to MCP Apps, A2UI, and ChatGPT Apps. TLDR: the product manages its own pixels, and the agent operates it via reference. The results: - Up to 100x token reduction - No new protocol - No browser API needed - No cooperation from the chat host Breakdown below:

Introducing ask-web: Rox’s in-house web search agent. ask-web sits on the cost-per-accuracy pareto frontier of the hyper-parameter grid when compared to frontier labs and commercial search agent providers. The agent delivers 91.3% accuracy at 1.03 cents per query on real production prompts. It has been running in production for more than 6 months with continuous evals. Inference partners: @togethercompute, @baseten, @modal Commercial Search vendors benchmarked: @perplexity_ai, @ExaAILabs, @p0. Frontier Search vendors benchmarked: @OpenAI, @AnthropicAI Exa, OpenAI and Anthropic excel on accuracy. Parallel and Perplexity are cost-efficient. Here’s the breakdown: