DataFramed

AI agents are getting easier to build and connect. Open standards now let them discover each other, pass messages and use shared tools. But connecting agents is not the same as getting them to work toward the same goal, and research on multi-agent systems shows that misalignment grows with every agent you add. For professionals deploying agents, this shows up as handoffs that fail and results that don’t add up, even when each agent is right on its own. How do agents agree on what a task means? What does a team need in place before adding more agents? And how do you know the combined result is better?Vijoy Pandey is SVP and GM of Outshift by Cisco, the company’s incubation engine for emerging technologies such as agentic AI and quantum networking. He has spent about three decades building distributed infrastructure at Google, IBM and startups, holds a PhD in Computer Science from UC Davis, and has filed more than 80 patents.In the episode, Richie and Vijoy explore the cognitive revolution and what it means for agents, the Internet of Cognition, coordination failures in multi-agent systems, agent protocols and guardrails, task-based access control, tiny teams with tokens (T3), organizational memory and context leaders, measuring whether agents are actually better, emergent collective intelligence, and much more.Links Mentioned in the Show:AGNTCY: open-source agent connectivity projectModel Context Protocol (MCP) and Agent2Agent protocol (A2A)Outshift: Internet of CognitionCoherent Cognition, Vijoy’s SubstackMichael Tomasello: A Natural History of Human ThinkingMichael Levin: talks and interviewsConnect with VijoyAI-Native Course: Intro to AI for WorkRelated Episode: AI Agents Are the New Shadow IT (And Your Governance Isn’t Ready) with Stijn Christiaens, CEO at CollibraNew to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

Most hiring decisions still come down to gut feel, and gut feel is full of blind spots — a favorite school, a firm handshake, the type of coffee someone carries into the room. As AI reshapes recruiting, the industry is being forced to ask whether decisions can be made on evidence instead of instinct. That means defining what actually predicts success in a role, testing every candidate against the same criteria, and being able to show the reasoning behind a hiring call rather than just a hunch. For anyone building a team, the question isn't whether bias exists — it's whether your process is built to catch it before it shapes who gets hired.Daniel Chait is the co-founder and CEO of Greenhouse, the hiring software used by tens of thousands of companies to run structured, data-driven hiring processes. Ophir Samson is Greenhouse's Head of Voice AI, leading the team following Greenhouse's acquisition of Ezra AI Labs, the voice AI interviewing company he founded.In the episode, Richie, Daniel, and Ophir explore the flood of AI-generated resumes drowning both job seekers and recruiters, how Voice AI interviews let candidates showcase themselves beyond a resume, structured hiring and scorecards for fairer decisions, bias audits and anti-cheat safeguards, EU AI Act compliance, and measuring success through pass-through rates and time savings, and much more.Links Mentioned in the Show:• Zapier <> Greenhouse: Conversational Application Voice AI• Paradigm Strategy• Anthropic• Pipecat on GitHub• Daily• Are Emily and Greg More Employable Than Lakisha and Jamal? (AEA)• Connect with Daniel: LinkedIn• Connect with Ophir: LinkedIn• AI-Native Course: Intro to AI for Work• Related Episode: How to Have a Machine Learning Career in 2026New to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

AI agents are moving from demos into production, but many teams run into the same wall: responses that take too long and cost too much. As models grow bigger and reason through more steps before answering, every chained agent adds delay, and that adds up fast at scale. For data and AI professionals, this raises a practical question: how do you pick a model and an inference platform that keeps a multi-agent workflow fast without blowing the budget? And underneath that choice sits a bigger one — how much of the AI stack, from chips to software, should a company actually control?Sumti Jairath is Chief Architect at SambaNova Systems, where he's focused on machine learning, big data analytics, and software-defined hardware since 2017. Before that, he spent close to eight years as a Senior Hardware Architect at Oracle working on hardware acceleration for machine learning and data analytics, and earlier held processor and server design roles at Hewlett Packard and Sun Microsystems. He holds a B.Tech in Electronics and Communication Engineering from the National Institute of Technology, Kurukshetra.In the episode, Richie and Sumti explore why AI agents are slow and expensive, the architecture behind faster inference, choosing the right model and platform for a task, open-source models and sovereign AI, the full AI infrastructure stack, power efficiency, the AI data center debate, career paths in AI infrastructure, and much more.Links Mentioned in the Show:• NVIDIA's Groq acquisition, announced around GTC• Chris Olah, Anthropic• Anthropic and OpenAI's coding-agent-driven revenue growth• Ollama• Apple Mac mini• SambaNova careers• Connect with Sumti• AI-Native Course: Intro to AI for Work• Related Episode: From City Sewers to Sovereign AI with Russ Wilcox, CEO at ArtifexAINew to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

Hypergrowth companies rarely scale on strategy alone — they scale on how fast decisions get made and how much risk employees feel safe taking. A recurring idea in high-growth environments is treating decisions differently depending on whether they're reversible, and rewarding people for making an impact rather than simply avoiding mistakes. For managers and individual contributors alike, that shift changes what "good work" looks like day to day. Which of your team's decisions actually need a leader's sign-off, and which ones would move faster if people just tried something and adjusted?Jon McNeill is CEO and co-founder of DVx Ventures, a venture studio that has launched 12 companies. He previously served as President at Tesla, where revenue grew from $2B to $20B in 30 months, and as COO at Lyft through its IPO. A serial entrepreneur, he's founded and sold six companies, sits on the boards of Lululemon and Asurion, and wrote The Algorithm: The Hypergrowth Formula that Transformed Tesla, Lululemon, General Motors and SpaceX.In the episode, Richie and Jon explore the algorithm behind Tesla's 10X hypergrowth, why automation should always come last, how to find and delete unnecessary process steps, building a culture of curiosity and urgency, one-way vs. two-way door decisions, small-team organizational design, changing the currency of promotion, and running effective meetings, and much more.Links Mentioned in the Show:• The Algorithm: The Hypergrowth Formula that Transformed Tesla, Lululemon, General Motors and SpaceX by Jon McNeill• Incorruptible by Eric Ries• Unreasonable Hospitality by Will Guidara• Eleven Madison Park• Jensen Huang on LinkedIn• Karim Bousta on LinkedIn• Connect with Jon• AI-Native Course: Intro to AI for Work• Related Episode: How to Thrive in a World of Continuous TransformationNew to DataCamp?Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business