Beyond The Pilot: Enterprise AI in Action

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Oct 21, 2026 42 min

AI Engineering: Measure the PRs not the Code

Season 1, Episode 18

Show notes

A $600 day of AI coding spend sounds alarming — until you divide it by the PRs shipped. At ~$30/PR, it reframes the entire ROI conversation: the spend isn't the problem, spend with no return is.

This episode comes from a live panel Sam moderated at VentureBeat Transform, featuring Emilie Schario (Kilo Code, acquired by Anaconda the morning of the panel), Amol Jain (Replit), and Jared Go (Symbiotic). The conversation cuts straight to what agentic coding actually looks like in production — not in demos.

On architecture: Kilo routes across 500+ models and processes 10 trillion tokens/month, using expensive frontier models for planning and cheaper open-weight models for execution. Replit built an internal fleet of agents running in isolated cloud VMs — connected to GCP, Datadog, and Slack — that autonomously handled a gnarly multi-day bug and produced a merged PR with zero human intervention. Symbiotic runs tiered monthly token budgets ($150 / $450 / exception) managed directly by engineering managers, not finance.

On org design: code review — not code generation — is now the bottleneck. Replit risk-scores every PR; low-risk ones auto-merge. The panel is aligned that "what does code review mean in 2025" is the defining question for the next 6–12 months.

On the 6-month outlook: open-weight models take majority share of coding tasks, frontier models reserved for architecture decisions; 24–48 hour unattended agent work loops become standard; and model sovereignty (geographic data routing, on-prem isolated environments) becomes a hard enterprise requirement.

---

🎙️ HOST: Sam Witteveen | VentureBeat

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00:00 Intro & Cost Per PR framing

00:40 Panel intro: Kilo Code, Symbiotic, Replit

02:20 Do engineers still read code? (Instacart CTO's 97% stat)

03:00 How Replit uses risk-scored PRs to auto-merge

04:20 Physical AI and safety gates for code review (Symbiotic)

05:00 Kilo Code: 500+ models, 10T tokens/month, Anaconda acquisition

07:20 Multi-model routing: frontier for planning, open-weight for execution

09:40 Symbiotic's stack: Cursor + homegrown SymCloud agentic harness

11:20 Replit's internal self-driving software engineering tool

12:20 MCP servers, skills repositories, and the 6-hour autonomous bug fix

13:20 Build vs. buy: where the real cost is maintenance, not construction

15:40 Replit replaces a seven-figure SaaS tool built entirely in-house

17:40 Token maxing: how teams burned their annual AI budget by July

18:20 Cost per PR as a ROI metric for AI coding spend

21:00 Symbiotic's tiered monthly token budget system for 900 engineers

23:20 Replit's 3x engineering productivity — and the GPT-5.5 Pro Max bill that forced a reckoning

24:20 Audience Q&A: model routing as a product layer for ISVs

26:20 Audience Q&A: PR review bottleneck, code deletion, and what review looks like next

29:40 6-month predictions: open-weight dominance, 24–48hr agent work loops, model sovereignty

---

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Oct 14, 2026 33 min

Routing Wars: Which Model Gets the Workload?

Season 1, Episode 17

Show notes

66% of enterprise AI teams are actively engineering toward zero human oversight — but only 4.5% fully trust their automated evals. Half have already shipped an agent that passed evals and still failed a real customer.

VentureBeat's latest research exposes a dangerous gap between deployment ambition and evaluation maturity. The most-used eval platform is OpenAI's native tooling. The second most common answer: no dedicated eval platform at all. Meanwhile, 51% of enterprises are relying on a hyperscaler's built-in guardrails as their primary AI security layer — not a deliberate security architecture.

Fable's 19-day market absence changed the conversation. Enterprises that had built production workflows on frontier models got a wake-up call about single-vendor dependency. The result: GLM 5.2 is gaining serious developer traction as an open alternative competitive with Opus 4.8, and Tencent's new Hai 3 — less than half the size — is drawing attention for agentic workloads that don't require cutting-edge coding performance. Sam and Matt break down why companies like Shopify, LinkedIn, and Pinterest were already on open models, and why others are now catching up fast.

On infrastructure: neo-clouds are currently used by just 2% of respondents, but 45% name them as their top planned investment area over the next 12 months. The market hasn't calcified. Matt and Sam preview what they expect to hear at VentureBeat Transform from engineering leaders at Intuit, Brex, Instacart, and Mastercard — including how Intuit bootstrapped and pivoted its agent architecture mid-build, and why agent conversations are generating higher-quality behavioral data than traditional user research ever did.

🎙️ HOSTS: Matt Marshall · Sam Witteveen | VentureBeat

__

If you enjoy these conversations, you need to be in Menlo Park this July.

VB Transform 2026 is VentureBeat's flagship enterprise AI event, built entirely around one question: How do you orchestrate AI autonomy at scale? July 14–15, Hotel Nia. Real projects, proprietary research, no fluff.

50% off for listeners with code BEYONDTHEPILOT: https://bit.ly/4fK4F6z

—

Chapters:

00:00 Research data drop: eval gaps, zero-HITL targets, and hyperscaler security dependency

01:00 Chinese models breaking into enterprise — developer fad or structural shift?

02:00 GLM 5.2 and Tencent Hai 3: what they do well and where they fall short

03:20 How Shopify, LinkedIn, and Pinterest are already running open models in production

05:40 Fable's 19-day outage: why 19 days changed enterprise AI vendor strategy

07:00 Intuit's agent build: bootstrapping, mid-build pivots, and real-time behavioral data

08:40 Neo-cloud infrastructure: 2% current usage, 45% planned investment next 12 months

09:20 Is the enterprise AI market still wide open? Anthropic vs. OpenAI for enterprise lead

11:20 The routing question: when do you send workloads to smarter vs. cheaper models?

12:20 Transform preview: what to expect from Amazon, Databricks, Brex, Instacart, Mastercard

---

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Jul 8, 2026 15 min

Small Models, Massive Wins: The New Shopify AI Formula

Season 1, Episode 16

Show notes

Shopify's distillation pipeline cuts production AI costs by up to 30x — and in some cases, the smaller model outperforms the frontier model on the narrow task. That's not a trade-off. That's a win on accuracy, latency, and cost simultaneously.

Farhan Thawar, VP & Head of Engineering at Shopify, runs AI across one of the largest commerce platforms on earth. In this episode, he breaks down the exact infrastructure decisions Shopify made to avoid being locked into any single model provider — and why 29% of enterprise AI projects die from token costs, not model failure.

Shopify built an internal LLM proxy that routes tokens across every major provider, enabling automatic failover when any one goes down. On top of that, their Universal Distillation Platform (UDP) lets any R&D team distill a frontier model (Opus 4, GPT-5+) down to a fine-tuned open source model (Qwen and others) for a specific subtask — in roughly a day, with evals baked in. Results range from 2x to 30x cheaper, faster, and more accurate than calling the frontier API for everything. Shopify currently runs roughly half a dozen of these distilled models in production, with more being added.

Farhan also details River, Shopify's internal agentic substrate — a public-only Slack agent that queries their data warehouse, reads their PM system, and improves its own answers when engineers jump in to correct it. Plus: how Shopify governs AI-generated code at scale, why they killed their token leaderboard, how UCP is positioning Shopify's catalog for agentic commerce, and what a two-to-three year horizon looks like when agents start holding spending budgets and buying autonomously.

🎙️ GUEST: Farhan Thawar | VP & Head of Engineering, Shopify

🎙️ HOST: Sam Witteveen | VentureBeat

__

If you enjoy these conversations, you need to be in Menlo Park this July.

VB Transform 2026 is VentureBeat's flagship enterprise AI event, built entirely around one question: How do you orchestrate AI autonomy at scale? July 14–15, Hotel Nia. Real projects, proprietary research, no fluff.

50% off for listeners with code BEYONDTHEPILOT: https://bit.ly/4fK4F6z

—

**CHAPTERS**

00:00 Intro — Infrastructure-first philosophy at Shopify

02:00 Episode overview: token economy, distillation, and the cost crisis killing AI projects

03:00 Toby's "AI reflexivity" mandate and what it actually means for engineers

05:20 Ecosystem strategy: when to build vs. leave room for third-party developers

06:20 Developer tooling stack: GitHub Copilot (2021), Claude Code, Cursor, Codex, and Shopify's own River

08:00 LLM proxy architecture: bulk token purchasing, multi-provider failover, and usage reporting

09:00 Model agnosticism: why Shopify lets engineers choose their own harness

10:00 AI adoption beyond R&D — finance, HR, sales, and the Qwik internal deployment platform

11:20 Code governance: who owns AI-generated code going to production

13:00 Token maxing, leaderboards, and the shift from AI reflexivity to AI leverage

14:20 Circuit breakers: how Shopify catches runaway token spend without hard limits

15:20 LLM proxy deep dive: uptime, insights, and cross-team learning from usage data

16:40 River: Shopify's agentic substrate, public-channel-only design, and emergent HITL behavior

18:40 Model distillation explained: teacher/student models, narrow tasks, and the trade-offs

21:00 Universal Distillation Platform (UDP): how any team submits a distillation job in ~one day

22:20 Tangle: open source pipeline visualization for distillation workflows

22:40 Who uses UDP today, and Farhan's vision for auto-selecting the distillation target model

24:00 Evals in practice: golden datasets, Toloka for data generation, threshold-based deployment

26:20 Sim Gym: simulating A/B tests for small merchants without enough traffic

27:40 Pulse: async AI insights on store performance and conversion

28:20 GPU infrastructure trade-offs: when running your own inference makes sense at scale

29:20 Frontier vs. distilled model split in production — and why dev tokens stay frontier

30:40 Should Shopify train its own coding model? Why Farhan says not yet

31:40 UCP protocol, agentic commerce, and how Shopify's catalog surfaces in every LLM

35:00 Early signals: agentic commerce growth rate and the shift away from SEO

36:00 What developers and entrepreneurs should build for now given multi-channel uncertainty

37:20 River as a hive-mind agent — and what "truly agentic" actually means

38:40 Two-to-three year forecast: agents with spending budgets, autonomous purchasing, proactive outreach

40:00 Project Glasswing, model access loss (Fable), and the case for multi-provider architecture

42:00 Why every company should have a backup plan — and how Shopify built theirs years ago

43:20 Wrap-up

---

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Jun 24, 2026 44 min

Weaponize Tokenmaxing: MassMutual’s ROI Engine

Season 1, Episode 15

Show notes

If you enjoy these conversations, you need to be in Menlo Park this July.

VB Transform 2026 is VentureBeat's flagship enterprise AI event, built entirely around one question: How do you orchestrate AI autonomy at scale? July 14–15, Hotel Nia. Real projects, proprietary research, no fluff.

50% off for listeners with code BEYONDTHEPILOT: https://bit.ly/4fK4F6z

_

He negotiated seat-based (unlimited) licenses before token costs exploded — and projects only a 20–30% spend increase when that changes. MassMutual's CIO rebuilt a COBOL mainframe app into a working web prototype in 7 days — work that used to take a 15-person SI team 90 days. That's not a pilot. That's a new build-vs-buy equation.

Sears Merritt, CIO at MassMutual, runs AI inside one of America's most regulated legacy environments. In this conversation, he breaks down the architecture decisions, cost structures, and security posture behind real, production deployments — not roadmaps.

On infrastructure: MassMutual routes all agentic tool calls through centralized API gateways with identity and access controls, using Amazon Bedrock as a proxy layer. That multi-harness design preserves model optionality while enforcing FinOps discipline. On model selection: a trust score rubric drives every model decision, balancing cost against user experience. In their IT contact center, that rubric led them to choose the more expensive model after users said the quality gap was worth two extra seconds of inference time.

Productivity results are concrete: 30% boost in developer output across the SDLC, call resolution time dropping from 10 minutes to under 1 minute for specific call types, cost per interaction from dollars to cents. On the security side, MassMutual is embedding AI into its SDLC for vulnerability scanning and compressing cyber response cycles from days to hours — building agentic tier-one and tier-two capabilities to match the accelerated threat landscape that frontier models like Mythos have exposed.

🎙️ GUEST: Sears Merritt | Head of Enterprise Technology & Experience, MassMutual

🎙️ HOSTS: Matt Marshall | VentureBeat, Sam Witteveen | VentureBeat

—

If you enjoy these conversations, you need to be in Menlo Park this July.

VB Transform 2026 is VentureBeat's flagship enterprise AI event, built entirely around one question: How do you orchestrate AI autonomy at scale? July 14–15, Hotel Nia. Real projects, proprietary research, no fluff.

50% off for listeners with code BEYONDTHEPILOT: https://bit.ly/4fK4F6z

—

00:00 Intro & COBOL Modernization Preview

00:01:15 Guest Introduction: Sears Merritt, MassMutual CIO

00:02:00 Multi-Vendor Strategy & Avoiding Lock-In

00:02:30 How MassMutual Evaluates AI Tools (Cost vs. Experience Rubric)

00:03:15 12-Month Contracts and Switching Optionality

00:03:30 AI Standards Cycle: MCP, A2A, and the Early Internet Analogy

00:05:15 Measuring Developer Productivity: 30% SDLC Boost

00:06:15 Contact Center Results: 10 Minutes to 1 Minute, Dollars to Cents

00:07:00 Managing Token Cost Explosion

00:07:30 Seat-Based vs. Consumption Licensing Decision

00:08:45 Token Maxing While the All-You-Can-Eat Window Is Open

00:09:45 Building FinOps Infrastructure for Model Routing and Optimization

00:11:15 Outcome-First Model Selection: When to Pay for Opus vs. a Cheaper LLM

00:13:15 Trust Score Framework: How MassMutual Picks the Right Model

00:15:00 Sponsor: OutShift by Cisco

00:15:30 Claude, OpenAI Codex, and Multi-Harness Agentic Architecture

00:16:30 API Gateway Design: Identity, Access, and FinOps Controls

00:17:30 What the Usage Analytics Revealed (And What Merritt Was Afraid to Find)

00:18:15 Projected Token Cost Increase: 20–30% Off Unlimited Plan

00:19:45 Power Law Usage: Top 10% Consuming 80% of Tokens

00:20:45 COBOL Mainframe Modernization: The 7-Day Prototype Workflow

00:22:30 The Full AI-Assisted COBOL Migration Playbook

00:24:00 Implications for IBM and Mainframe-as-a-Service Providers

00:25:15 Open Source Models, DeepSeek, and the Cost Efficiency Question

00:27:45 Chinese Models in a Regulated Environment: Evaluation Criteria

00:29:30 Agentic Security: Identity Management and the Evolving Threat Landscape

00:30:00 How Frontier Models Changed the Threat Velocity (Not the Threat Types)

00:31:15 Fighting AI With AI: Agentic Tier-1 and Tier-2 Cyber Capabilities

00:31:30 Project Glasswing and the CISO Community Response

00:32:30 Embedding AI Into the SDLC for Security Scanning

00:34:00 Closing: When Will Agentic Standards Consolidate? Advice for Builders

---

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Jun 10, 2026 36 min

Building a 30% Better AI: The Taste Graph Moat

Season 1, Episode 14

Show notes

Pinterest's open-source AI stack costs 90% less than frontier models — and their custom-trained recommender outperforms off-the-shelf alternatives by 30% in accuracy. Pinterest CTO Matt Madrigal breaks down exactly how they did it, and what enterprise AI teams can actually replicate.

Madrigal walks through the full architecture behind Navigator 1, Pinterest's conversational shopping assistant built on Qwen 3 VL — and the specific decision to rip out its native vision encoder and replace it with PinCLIP, Pinterest's proprietary multimodal embedding layer. That swap alone closes a 20x inference latency gap and makes the economics work at 620 million monthly active users. This is the clearest public explanation yet of how a scaled platform operationalizes the "core vs. context" principle for model selection: open-source and custom-built where it touches the user, frontier models where speed-to-prototype matters more than cost.

The conversation also covers the Taste Graph — Pinterest's knowledge graph across hundreds of billions of pins and 15 billion boards — and how post-training on that proprietary data lets a smaller, fit-for-purpose model beat a larger frontier model on production metrics. Madrigal details their eval framework: gold set benchmarks, product-level evals tied to engagement and merchant click outcomes, and a structured A/B test pipeline that runs from engineer PRs through to live user signal.

On the organizational side: how Pinterest manages a "default yes" multi-IDE policy (Cursor, Windsurf, Claude Code, Codex) without collapsing security posture, how they segment sandbox environments between ML engineers with Taste Graph access and general application developers, and why Madrigal measures AI coding ROI in token usage and experimentation velocity — not lines of code.

🎙️ GUEST: Matt Madrigal | CTO, Pinterest

🎙️ HOSTS: Matt Marshall | VentureBeat, Sam Witteveen | VentureBeat

00:00 Show Intro and Guest

01:17 Open Source Cost Breakdown

02:20 Pinterest Multimodal Roots

02:37 PinClip and Embeddings

05:46 Core vs Context Models

07:43 Navigator 1 Assistant Stack

11:52 Benchmarking and Evals

13:29 Accuracy from Proprietary Data

17:16 Taste Graph Explained

18:29 Taste Graph in Training

22:22 Fighting AI Slop

25:16 Developer Tools and Velocity

27:57 Tool Choice and Governance

28:56 Security Sandboxes and CICD

30:57 Wrap Up

If you enjoy these conversations, you need to be in Menlo Park this July.

VB Transform 2026 is VentureBeat's flagship enterprise AI event, built entirely around one question: How do you orchestrate AI autonomy at scale? July 14–15, Hotel Nia. Real projects, proprietary research, no fluff.

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May 27, 2026 34 min

GPU Hoarding is Over. The $401B Reality Check

Season 1, Episode 13

Show notes

Enterprise GPU hoarding is over. LinkedIn CTO Erran Berger and VentureBeat analyst Rob Strechay break down what comes next — and the infrastructure math most enterprises are only now being forced to confront.

VentureBeat's Q1 research shows GPU availability anxiety dropped from 20.8% to 15.4% among enterprise teams, while cost-per-inference and TCO concerns jumped from 34% to 41% — a number that's still climbing. The hoarding phase is giving way to an audit phase, and the companies that didn't build the instrumentation to understand their workloads are now paying for it.

Erran Berger explains how LinkedIn runs one of the few remaining at-scale applied ML shops outside the hyperscalers — owning the full stack from bare metal GPU clusters to member-facing products. That means LinkedIn engineers can optimize custom CUDA kernels, compress embeddings, prune models for throughput, and adapt networking and storage per workload — trade-offs that are simply unavailable on public cloud instance menus. The result: a rigorous ROI framework that evaluates not just current traffic costs, but the traffic shape agents will drive in 2–3 years.

On the market side, 72% of enterprises admit they lack sufficient control over their AI infrastructure. Open-source inference tools like vLLM and LLMD are seeing rapid adoption, while 17% of organizations have moved to full-stack ownership. Hyperscalers report 60–80% of workloads have already shifted from training to inference — and most enterprise teams are still figuring out how to staff and instrument for that reality.

🎙️ GUEST: Erran Berger | CTO, LinkedIn

🎙️ ANALYST: Rob Strechay | VentureBeat

🎙️ HOST: Matt Marshall | CEO, VentureBeat

---

00:00 Intro: The GPU Hoarding Hangover

00:10 Guest Introductions

02:00 VentureBeat Q1 Data: GPU Panic Fades, TCO Concerns Rise

03:00 LinkedIn's Early Shift to Inference ROI Discipline

04:00 Budget Moving Into Inference Optimization and Control

07:00 LinkedIn's Full-Stack Advantage: Kernels, Pruning, Embedding Compression

08:00 Private AI and Sovereign Stacks: What the Q1 Data Shows

09:00 Open Source Inference Tooling: vLLM, LLMD, RDMA

10:00 Data Sovereignty at LinkedIn Scale: Member Data and Board-Level ROI Framing

12:00 Why Instrumentation Beats GPU Hoarding

13:00 Planning for Ambient Agent Traffic — Not Just Today's Workloads

14:00 Closing Advice for the Enterprise CTO Staring at 5% GPU Utilization

---

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May 13, 2026 16 min

Agents Ate the UI: Data is Your Only Moat with LlamaIndex

Show notes

The CEO who built one of the most-starred RAG frameworks on GitHub (47,000 stars) just publicly declared that frameworks like his are becoming obsolete — and then pivoted his entire company around that conclusion.

Jerry Liu, CEO and co-founder of LlamaIndex, joins Matt Marshall and Sam Witteveen to explain exactly what broke in the AI stack, why 95% of his team's code is now AI-generated, and where the real defensibility in enterprise AI infrastructure actually lives in 2026.

The conversation covers the specific architectural shift that made RAG orchestration frameworks less central: agent reasoning has improved to the point where dumb tools plus smart agents outperform sophisticated retrieval pipelines, coding agents have collapsed the cost of custom integrations, and model providers like Anthropic are consolidating the harness layer around MCP, sandboxes, and session state. Jerry walks through Anthropic's managed agent diagram as a real architectural reference point and explains why engineering leaders should prioritize modular interfaces over implementation investment — because parts of your current stack will need to be thrown away in months, not years.

On SaaS survival, Jerry argues the companies that retain value are those becoming systems of record — and that the real opportunity is building AI agents that automate labor on top of their platforms, not defending UI/UX that agents are now bypassing. On LlamaIndex's own bet: document understanding — parsing PDFs, tables, charts, and forms at higher accuracy and lower cost than frontier models — is the context layer every agent stack needs regardless of which model wins the next benchmark cycle. LlamaParse and the newly released open-source ParseBench (April 13) are the commercial expression of that thesis.

If you're evaluating your AI stack architecture, deciding how much to build vs. buy, or trying to understand where horizontal tooling still has a moat, this episode is the conversation.

🎙️ GUEST: Jerry Liu | CEO & Co-Founder, LlamaIndex

🎙️ HOSTS: Matt Marshall | VentureBeat, Sam Witteveen | VentureBeat

---

**CHAPTERS**

00:00 Intro — LlamaIndex's origin and RAG framework origins

02:00 How LlamaIndex started: GPT-3, 4K context windows, and GPT Index

04:00 Why AI frameworks are becoming less useful in the agentic era

07:00 What changed in the stack: agent reasoning, coding agents, and RAG's evolution

09:00 How Anthropic's managed agent diagram reframes enterprise architecture

13:00 The lock-in question: managed agents, session state, and stack modularity

16:00 Should you build horizontal tooling? Why Jerry says probably not

18:00 Open vs. closed: the Apple/Android analogy applied to frontier labs

21:00 The abstraction level is rising — English is the new programming language

24:00 SaaS market cap destruction: who survives agents eating software

28:00 The "full stack builder" emergence and the future of SaaS seats

31:00 Buy vs. build for agents: the AI recruiter thought experiment

33:00 LlamaIndex's pivot: document understanding as defensible infrastructure

36:00 Why frontier models won't commoditize specialized document parsing

38:00 LlamaParse deep dive: zero-shot accuracy, tables, charts, handwriting

41:00 LightParse, ParseBench, and designing for agent consumers

44:00 Wrap-up and where to follow LlamaIndex

---

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Apr 29, 2026 45 min

The Protocol Stack AI Is Missing

Season 1, Episode 11

Show notes

Cisco's OutShift deployed a multi-agent network configuration system that raised error detection from 10–15% to 100% and cut full change validation from 2–3 weeks to 6–7 minutes. The reason it worked — and why most enterprise multi-agent deployments still fail — comes down to a single gap nobody is talking about: agents can connect, but they cannot think together.

Vijoy Pandey, SVP and General Manager of OutShift by Cisco, joins Matt and Sam to explain why A2A, MCP, and existing agent protocols solve connectivity but leave out an entire layer: shared cognition. OutShift's research identifies this as a missing "Layer 9" — a semantic and cognitive communication stack above today's syntactic protocols — and they're already building it.

The conversation covers the four pillars of enterprise-grade multi-agent infrastructure (discovery, identity/access, communication, observability), why standard IAM models break when agents enter the picture, and how OutShift extended OpenTelemetry with Microsoft to cover multi-agent evaluation. Vijoy introduces three new cognition-state protocols — SSTP (Semantic State Transfer), LSTP (Latent Space Transfer), and CSTP (Compressed State Transfer) — and explains the staged rollout path for each, including a published MIT collaboration called the Ripple Effect Protocol.

The healthcare scheduling case study is particularly instructive: three independent third-party agents — insurance, diagnostics, scheduling — each with competing optimization functions and siloed context, and zero shared intent. That's the real multi-vendor, multi-org enterprise problem. Vijoy explains what an orchestrator can't fix, and what a cognitive fabric layer would.

🎙️ GUEST: Vijoy Pandey | SVP & General Manager, OutShift by Cisco

🎙️ HOSTS: Matt Marshall | VentureBeat, Sam Witteveen | VentureBeat

---

**CHAPTERS**

00:00 Intro & Cold Open: Agents Connect But Can't Think Together

00:03 Welcome & Guest Introduction: Vijoy Pandey, OutShift by Cisco

00:04 Do Agents Work Outside Coding & Customer Support? Challenging Amjad Masad's Diagnosis

00:05 What's Wrong With A2A and MCP? The Four Pillars of AGNTCY

00:08 Identity & Access Management for Agents: Why IAM Breaks and What TBAC Fixes

00:12 The Network Digital Twin: How OutShift Achieved 100% Error Detection in Production

00:13 From 2–3 Weeks to 6–7 Minutes: Real Results From Deployed Multi-Agent Networking

00:15 Agents Can Connect But Can't Think Together: The Core Thesis

00:20 The Cognitive Revolution Analogy: Shared Intent, Shared Context, Collective Innovation

00:25 The Healthcare Scheduling Case Study: Three Competing Agents, Zero Shared Intent

00:31 Why Orchestrators Fail in Multi-Vendor, Multi-Org Environments

00:36 Introducing Layer 9: SSTP, LSTP, and CSTP — The Cognition-State Protocol Stack

00:41 What OutShift Is Building Now: Protocols, Fabric, and Cognition Engines

00:44 MIT Collaboration: The Ripple Effect Protocol and Phase One Rollout

00:46 Cisco's 40-Year Networking Playbook Applied to the Internet of Cognition

00:49 Closing: Where to Find the Research, AGNTCY, and OpenClaw Integration

---

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#EnterpriseAI #AIAgents #MultiAgentSystems #AIInfrastructure #LLM

—

“Scaling Out Superintelligence” Vijoy Pandey, January 2026. The technical whitepaper detailing the Internet of Cognition architecture, three-layer stack, and cognition state protocols.

Internet of Cognition Interactive Demo Clickable walkthrough showing per-agent activity, intent, context, and collective reasoning across a multi-agent SRE system.

“A Layered Protocol Architecture for the Internet of Agents” Fleming, Muscariello, Pandey, Kompella. The OSI Layer 8/9 extension.

AGNTCY Open source multi-agent infrastructure under Linux Foundation governance. Covers discovery, identity, communication, observability.

Formative members: Cisco, Dell Technologies, Google Cloud, Oracle, Red Hat.

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Apr 15, 2026 51 min

100M Agents: Scaling the New Execution stack with Intuit

Season 1, Episode 10

Show notes

A QuickBooks customer discovered significant fraud by asking their AI assistant follow-up questions about transaction amounts that didn't add up. This isn't a demo — it's one of 3 million customers now using Intuit's AI agents in production, with 80.5% returning to use them again.

Marianna Tessel, EVP and GM of QuickBooks (formerly CTO of Intuit), walks through the architecture decisions behind one of the first enterprise AI deployments at true scale. Intuit's "done-for-you" agents now automate book closing, reconciliation, transaction categorization, and payroll — but the breakthrough came when they realized chatbots alone weren't enough. Businesses wanted human experts integrated directly into AI workflows, creating what Intuit calls the "AI + HI" model (artificial intelligence + human intelligence). The results: invoices paid 5 days faster, 90% more paid in full, 30% reduction in manual work, and 62% of users reporting bookkeeping is easier.

Tessel reveals the technical evolution: moving from monolithic agents to a dynamic orchestration layer that routes queries across multiple LLMs (including Intuit's proprietary FinLM built on open-source), 24,000 bank connections, and 600,000 customer attributes. The system now handles proactive anomaly detection, benchmarking against similar businesses, and even nascent vibe coding — all without requiring users to understand they're essentially programming workflows through natural language. She also addresses the "SaaS apocalypse" narrative head-on, explaining why QuickBooks saw 18% growth last quarter while competitors faced market pressure: durable data advantages and customer trust in financial accuracy matter more than ever when AI enters the mix.

For enterprise builders navigating agent architecture, data grounding, and human-in-the-loop design, this is a rare look inside a working system serving millions.

🎙️ GUEST: Marianna Tessel | EVP & GM, QuickBooks (Intuit)

🎙️ HOSTS: Matt Marshall | VentureBeat, Sam Witteveen | VentureBeat

00:00 Intro — Customer discovers fraud using QuickBooks AI

03:26 Intuit Intelligence: Agents, BI, and human expertise integration

05:20 First-time AI users and going beyond chatbots

08:02 How Intuit decides which workflows to automate

10:16 Sponsor: Outshift by Cisco

10:38 Human-in-the-loop: When to insert experts vs. full automation

13:00 The AI + HI model: Why customers want human verification

15:24 Human expertise as confidence layer, not just AI check

16:14 Proprietary data advantage: 24K bank connections, 600K attributes

18:39 Benchmarking: "Businesses like me" — using aggregate data for competitive insights

19:52 First-party vs. third-party data strategy

21:38 Addressing the "SaaS apocalypse" narrative — why Intuit grew 18% last quarter

24:39 Proactive AI: Anomaly detection for marketing expense spikes

25:20 Builder perspective: Leaning on LLM orchestration, not use-case-by-use-case builds

27:32 Architecture evolution: From monolithic agents to dynamic tools and skills

29:10 Composite UX: Chat side-by-side with traditional workflows

30:35 Multi-model strategy: Genos platform, FinLM, and model routing

31:16 Vibe coding and actions: Letting users automate without realizing they're coding

32:47 Personalization wave: Memory, persistence, and user-defined workflows

35:08 Docker background and primitives that survive disruption

36:00 Open Claw and agent automation: Real revolution or risky experimentation?

#EnterpriseAI #AIAgents #QuickBooks #Intuit #LLMOrchestration #AgenticAI

Presented by Outshift by Cisco Outshift is Cisco’s emerging tech incubation engine and driver of Agentic AI, quantum, and next-gen infrastructure. Learn more athttps://outshift.cisco.com.

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Apr 1, 2026 38 min

The AI War For Your Personal Context

Season 1, Episode 9

Show notes

Major SaaS companies including Salesforce, Intuit, and ServiceNow saw stock drops of 45-50% as enterprises shift from bloated software suites to personalized AI agents that users can control directly. Microsoft just capitulated this week, opening Copilot to allow Claude Cowork-style functionality — a clear signal that the "build vs. buy" calculus for enterprise software has fundamentally changed.

Matt Marshall and Sam Witteveen break down why personalization is no longer optional for enterprise products. Companies like Zoom now offer personalized workflows that access your conversation history and profile context. Infrastructure decisions are moving fast: token budgets must account for per-user context, identity management has become the biggest technical challenge for agent deployments, and "skills" (not just MCP) are emerging as the key abstraction layer.

Zoom's Li Juan explains how their AI Companion moved beyond generic templates to user-controlled personalization: tracking opinion divergence in meetings, generating follow-up emails with specific context controls, and giving users explicit prompt examples instead of "good luck with your prompt." This is the new standard. If your product can't reason over which tools to use, which skills to apply, and which context to pull — all personalized to the individual user — you're competing with something that can be built in 10 days (Cowork's timeline).

The agents-are-taking-over reality is here: multi-user agent architectures require thinking about context contamination, security postures for computer-use capabilities, and whether you're building internal agents or buying SaaS that will adapt. Sam's take: "AGI is agentic, and we're well along that continuum now."

🎙️ HOSTS: Matt Marshall | CEO, VentureBeat & Sam Witteveen | VentureBeat

📺 CHAPTERS:

00:00 Intro — The SaaS Apocalypse

00:01:00 The Personalization Imperative

00:02:00 Microsoft Copilot Capitulates to Cowork

00:03:00 From Template Selection to Skill Generation

00:04:00 The Land Grab for User Context

00:05:00 Zoom's Li Juan on Personalized Meeting Intelligence

00:06:00 Why Context = Magic in Enterprise AI

00:07:00 Product-Market Fit in the Agent Era

00:08:00 Metrics That Matter: JP Morgan's 30,000 Agents

00:09:00 Build vs. Buy: The New Calculus

00:10:00 Why Slack Might Win on Agent Identity Management

00:11:00 Zoom's AI Companion: Control Over Randomness

00:13:00 Li Juan on Purposeful Prompts and Reference Control

00:15:00 Multi-Agent vs. Multi-User: The Critical Distinction

00:16:00 LinkedIn's GPU Optimization Strategy

00:17:00 AGI Is Agentic: Where

Presented by Outshift by Cisco Outshift is Cisco’s emerging tech incubation engine and driver of Agentic AI, quantum, and next-gen infrastructure. Learn more athttps://outshift.cisco.com.

About VentureBeat: VentureBeat equips enterprise technology leaders with the clearest, expert guidance on AI – and on the data and security foundations that turn it into working reality.

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Mar 18, 2026 21 min

LangChain: What OpenClaw Got Right (And Why Enterprises Can't Have It)

Season 1, Episode 8

Show notes

LangChain told employees they cannot install OpenClaw on company laptops due to "massive security risk" — yet this unhinged approach is exactly what makes it work. Harrison Chase unpacks why OpenClaw succeeds where AutoGPT failed, and why context engineering, not just smarter models, separates demo agents from production-ready systems.

The shift is architectural: Modern agent harnesses like Claude Code now dump 40,000-token API responses to file systems instead of cramming them into message history. LangChain's Deep Agents framework emerged from reverse-engineering Claude Code, Codex, and Deep Research — discovering they all use planning via to-do lists, subagents for focused work, file systems for context control, and 2000-line system prompts. Harrison explains why coding agents make surprisingly good general-purpose agents, how prompt caching creates accuracy trade-offs, and why "context engineering" — bringing the right information in the right format to the LLM at the right time — matters more than framework choice.

For enterprise teams: Harrison breaks down LangGraph (agent runtime with durable execution), LangChain (unopinionated agent framework), and Deep Agents (batteries-included harness). The conversation covers when to use graphs vs. loops, how skills differ from tools and subagents, and why nine months ago marked the inflection point where models could finally run reliably in autonomous loops.

🎙️ GUEST: Harrison Chase | Co-founder & CEO, LangChain

🎙️ HOSTS: Matt Marshall | CEO, VentureBeat | Sam Witteveen | VentureBeat

**CHAPTERS:**

00:00 Intro — OpenClaw security warning

01:00 LangChain's origin story: From open source library to company

03:00 Early LLM patterns: RAG and SQL agents before ChatGPT

05:00 Why OpenClaw works where AutoGPT failed

08:00 Step change in agent capability: The summer 2024 inflection

11:00 Deep Agents unpacked: Planning, subagents, file systems, prompting

14:00 Skills vs tools vs subagents

16:00 LangGraph, LangChain, and Deep Agents architecture

19:00 Context engineering: What the LLM sees vs what developers see

21:00 File systems for context management vs AutoGPT's approach

**LINKS:**

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#EnterpriseAI #AIAgents #LangChain #AgenticAI #LLMInfrastructure

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Mar 4, 2026 56 min

LexisNexis on Why Standard RAG Fails in Law

Season 1, Episode 7

Show notes

On February 2nd, a single plugin wiped nearly $800 billion off the enterprise software market. Wall Street is terrified that AI agents are about to eat the legal industry's lunch. But LexisNexis isn't scared—they're building the moat.

In this episode of Beyond the Pilot, Min Chen (Chief AI Officer, LexisNexis) reveals the sophisticated architecture they built to counter the "LLM wrapper" revolution. Moving beyond standard RAG, Min breaks down their move to "GraphRAG", their deployment of Agentic workflows (using Planner and Reflection agents), and why they created a proprietary "Usefulness Score" because standard accuracy metrics weren't good enough for lawyers.

AI Gets Real Here. No theory, just the execution roadmap for deploying AI in a zero-error environment.

In this episode, we cover:

•

The "Dangerous RAG" Problem: Why semantic search fails in professional domains (retrieving "relevant" but overruled cases) and how "Point of Law" knowledge graphs fix it.

•

The "Usefulness" Metric: The 8 sub-metrics LexisNexis uses (including Authority, Comprehensiveness, and Fluency) to grade AI quality.

•

Agentic ROI: How deploying a "Planner Agent" to break down complex questions increased answer usefulness by 20%.

•

The "Reflection Agent": Using a secondary agent to critique and refine drafts in real-time.

•

Hallucination Detection: Why you should never rely on an LLM to judge its own hallucinations (and the deterministic code they use instead).

⏱️ TIMESTAMPS

00:00 - Intro: The $800 Billion AI Threat to Legal Tech

02:18 - Min Chen’s Journey: From Feature Engineering to Chief AI Officer

05:55 - Why Standard RAG Fails in Law (and How GraphRAG Fixes It)

10:40 - "Accuracy" is a Vanity Metric: The 8-Point Usefulness Score

14:20 - The "Auto-Eval" Framework: Human-in-the-Loop at Scale

16:40 - The Secret Sauce: Don't Use LLMs to Detect Hallucinations

21:15 - Agentic AI: How "Planner Agents" Drove a 20% Gain

22:00 - The "Reflection Agent": Self-Critique Loops for Drafting

30:30 - Distillation: Balancing Cost, Speed, and Quality

32:45 - Min’s Advice: Don't Build the Product First (Build the Metrics)

Presented by Outshift by Cisco Outshift is Cisco’s emerging tech incubation engine and driver of Agentic AI, quantum, and next-gen infrastructure. Learn more athttps://outshift.cisco.com.

About VentureBeat: VentureBeat equips enterprise technology leaders with the clearest, expert guidance on AI – and on the data and security foundations that turn it into working reality.

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Feb 18, 2026 36 min

Mastercard's 160 Billion Transactions: AI's Biggest Test

Season 1, Episode 6

Show notes

While most of the world is still running GenAI pilots, Mastercard is running AI inference on 160 billion transactions a year—with a hard latency limit of 50 milliseconds per score.

In this episode of Beyond the Pilot, Johan Gerber (EVP of Security Solutions) and Chris Merz (SVP of Data Science) open the hood on one of the world's largest production AI systems: Decision Intelligence Pro. They reveal how they moved beyond legacy rules engines to build Recurrent Neural Networks (RNNs) that act as "inverse recommenders"—predicting legitimate behavior faster than the blink of an eye.

AI Gets Real Here. This isn't just about defense. Johan and Chris detail how they are taking the fight to criminals by leveraging Generative AI to engage scammers with "honeypots," expose mule accounts, and map fraud networks globally.

In this episode, we cover:

•

The 50ms Inference Challenge: How Mastercard optimized their RNNs to score transactions at a peak rate of 70,000 per second.

•

"Scamming the Scammers": How GenAI agents are being used to automate honeypot conversations and extract mule account data.

•

The "Inverse Recommender" Architecture: Why Mastercard treats fraud detection as a recommendation problem (predicting the next likely merchant).

•

Org Design for Scale: The "Data Science Engineering Requirements Document" (DSERD) Chris used to align four separate engineering teams.

•

The Hybrid Infrastructure: Why moving to Databricks and the cloud was necessary to cut innovation cycles from months to hours.

🚀 CHAPTERS

00:00 - Intro: 160 Billion Transactions & 50ms Decisions

02:08 - Thinking Like a Criminal: Johan’s Law Enforcement Background

06:22 - Org Design: Why AI is the "Middle Lane" of Engineering

11:00 - The Scale: 70k Transactions Per Second

15:47 - Decision Intelligence Pro: The "Inverse Recommender" RNN

23:00 - The "Lego Block" Strategy: Aligning Data Science & Engineering

33:00 - Infrastructure: Why Cloud/Databricks was Non-Negotiable

37:00 - GenAI Offensive: Threat Hunting & "Scamming the Scammers"

46:40 - "Honeypots" and Detecting Mule Accounts

52:00 - Advice for Technical Leaders: Talent & Prioritization

Presented by Outshift by Cisco Outshift is Cisco’s emerging tech incubation engine and driver of Agentic AI, quantum, and next-gen infrastructure. Learn more athttps://outshift.cisco.com.

About VentureBeat: VentureBeat equips enterprise technology leaders with the clearest, expert guidance on AI – and on the data and security foundations that turn it into working reality.

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Feb 4, 2026 56 min

Inside LinkedIn’s AI Engineering Playbook

Season 1, Episode 5

Show notes

While the rest of the industry chases massive models, LinkedIn quietly achieved a major engineering breakthrough by going small.

In this episode of Beyond the Pilot, Erran Berger (VP of Product Engineering, LinkedIn) opens the "cookbook" on how they distilled massive 7B parameter models down to ultra-efficient 600M parameter "student" models—scaling AI to 1.2 billion users without breaking the bank.

AI Gets Real Here. This isn't theory. Erran details the exact architecture, the "Multi-Teacher" distillation process, and the organizational shift that forced Product Managers to write evals instead of specs.

In this episode, we cover:

•

The Distillation Pipeline: How to train a 7B "Teacher" and distill it to a 1.7B intermediate and 0.6B "Student" for production.

•

Synthetic Data Strategy: Using GPT-4 to generate the "Golden Dataset" for training.

•

Multi-Teacher Architecture: Why they separated "Product Policy" and "Click Prediction" into different teacher models to solve alignment issues.

•

10x Efficiency Hacks: Specific techniques (Pruning, Quantization, Context Compression) that slashed latency.

•

Org Design: Why the "Eval First" culture is the new requirement for AI engineering teams.

🚀 CHAPTERS

00:00 - Intro: LinkedIn's Massive "Small Model" Feat

04:00 - Why Commercial Models Failed at LinkedIn Scale

08:00 - The "Product Policy" Funnel & Synthetic Data Generation

12:00 - The Pipeline: 7B → 1.7B → 600M Parameters

19:00 - The "Multi-Teacher" Breakthrough (Relevance vs. Clicks)

23:00 - How They Achieved 10x Latency Reduction (Pruning/Compression)

31:00 - Changing the Culture: Why PMs Must Write Evals

35:00 - The "Bright Green Matrix": Measuring Success & Future Roadmap

Presented by Outshift by Cisco Outshift is Cisco’s emerging tech incubation engine and driver of Agentic AI, quantum, and next-gen infrastructure. Learn more athttps://outshift.cisco.com.

About VentureBeat: VentureBeat equips enterprise technology leaders with the clearest, expert guidance on AI – and on the data and security foundations that turn it into working reality.

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#EnterpriseAI #LLMDistillation #LinkedInEngineering #SmallLanguageModels #AIArchitecture #TechLeadership

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Jan 21, 2026 41 min

Most enterprise AI agents are Slop - here’s why they fail

Season 1, Episode 4

Show notes

The "TAM" for AI Agents isn't software. And there is a $10 Trillion opportunity.

In this episode, Replit CEO Amjad Masad reveals why 99% of today's enterprise AI agents are just "Slop"—unreliable, generic toys that fail in production. We dive deep into the engineering reality of building autonomous agents that actually work, moving beyond simple chatbots to systems that can navigate the messy reality of enterprise infrastructure.

Amjad breaks down Replit’s "Computer Use" hack that makes agents 10x cheaper than generic models, explains why "Vibe Coding" is the future of the C-Suite, and issues a warning to technical leaders: If you want to ship fast in the AI era, you need to kill your product roadmap.

In this episode, we cover:

•

The "Slop" Problem: Why most LLM outputs are generic and how to inject "taste" back into software.

•

The Computer Use "Hack": How Replit built a programmatic verifier loop that outperforms vision-based models.

•

Vibe Coding: Why non-technical domain experts (HR, Sales, Marketing) will build the next generation of enterprise software.

•

The $10T Market: Why the Junior Developer role is disappearing and being replaced by the "Manager of Agents."

🚀 CHAPTERS

0:00 - Intro: Why most AI Agents are "Toys"

03:02 - The only 2 AI use cases making money right now

06:00 - The "Crappy Product" Strategy (Shipping fast)

10:00 - What is "AI Slop"? (And how to fix it)

14:30 - The "Deleted Database" Incident: Solving Reliability

18:00 - The "Squishy" Divide: Why Marketing Agents fail

21:45 - Vibe Coding in the Enterprise

26:00 - Model Wars: Claude Opus vs. Gemini vs. OpenAI

28:10 - The "Computer Use" Hack (10x Cheaper, 3x Faster)

36:00 - Why Product Roadmaps are Dead

43:00 - Replit is the #1 Software Vendor (Ramp Data)

49:00 - The Unit Economics of Agents (Token Costs vs. Value)

53:00 - Open Source vs. Closed: The "Cathedral of Bazaars"

59:00 - The $10 Trillion Opportunity: Replacing Labor

🔗 CONNECT WITH US

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#AgenticAI #Replit #VibeCoding #EnterpriseAI #LLM #SoftwareEngineering #FutureOfWork #AmjadMasad #ArtificialIntelligence #DevOps

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Jan 7, 2026 1 hr 3 min

How JPMorgan Engineered a 30K AI Agent Economy

Season 1, Episode 3

Show notes

Inside the 'Agent Economy': How 30,000 AI Assistants Took Over JPMorgan

While most enterprises were scrambling after ChatGPT launched, JPMorgan Chase was already two years ahead. 🚀

In this episode of Beyond the Pilot, we sit down with Derek Waldron, Chief Analytics Officer at JPMorgan Chase, to reveal how the world’s largest bank built an internal AI platform that is now used by 1 in 2 employees daily.

Derek shares the contrarian insight that drove their strategy: AI models are commodities; the real moat is connectivity.

Learn how they scaled from zero to 250,000+ users, why they empowered employees to build 30,000+ of their own "Personal Agents," and how they are solving the data privacy challenge at an enterprise scale.

🔥 IN THIS EPISODE:

•

The "Super Intelligence" Thought Experiment: Why raw intelligence is useless without enterprise connectivity.

•

The Agent Economy: How JPM enabled non-technical staff to build 30,000 custom AI assistants.

•

The Adoption Playbook: How to break through the "30% wall" and get the majority of your workforce using AI.

•

Build vs. Buy: Why JPM built their own "LLM Suite" instead of waiting for vendors.

⏳ CHAPTERS:

00:00 - Introduction: The JPMorgan AI Story

01:45 - The 3 Core Principles Behind JPM’s Strategy

03:25 - The "Super Intelligence" Thought Experiment

05:00 - Data Privacy: Why JPM Doesn't Train Public Models

06:00 - Viral Adoption: From 0 to 250k Users

09:20 - Evolution of LLM Suite: From RAG to Ecosystem

14:00 - The "Moat" is Connectivity, Not the Model

23:00 - The Agent Economy: 30,000 Employee-Built Assistants

31:00 - Governance & Guardrails for AI Agents

33:00 - Crossing the Chasm: Getting to 60% Adoption

40:00 - The "Product" Mindset: Solving Business Problems First

42:30 - The Future: End-to-End Process Transformation

46:25 - The "Unsolved" Problem Derek Wants to Fix

🙏 SPECIAL THANKS TO OUR SPONSOR:

This episode is presented by Outshift by Cisco.

Learn more about their work on the Internet of Agents and the open-source Linux Foundation project:

🔗 https://www.agentcy.org

🎙️ GUEST:

Derek Waldron | Chief Analytics Officer, JPMorgan Chase

HOSTS:

Matt Marshall | VentureBeat

Sam Witteveen | VentureBeat

#EnterpriseAI #JPMorgan #GenerativeAI #AgenticAI #FinTech #ArtificialIntelligence #Innovation #BeyondThePilot

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Dec 17, 2025 46 min

How Booking.com Boosted Agent Accuracy 2x with Mini LLMs with Pranav Pathak

Season 1, Episode 2

Show notes

We built AI agents by accident... and it worked. 🤯

In this episode of VentureBeat’s Beyond the Pilot, we go inside the engineering brain of Booking.com with Pranav Pathak (Director of Product Machine Learning). Pranav reveals how they "stumbled" into agentic architectures before the term even existed, how a simple text box revealed a massive missed revenue opportunity (the "Hot Tub" story), and exactly how they stack LLMs, RAG, and Orchestrators to handle millions of travelers without breaking the bank.

If you are building Enterprise AI, this is the blueprint for moving from "cool demo" to production scale.

🚀 In this episode, we cover:

•

The "Hot Tub" Revelation: How free-text AI search exposed features customers desperately wanted but couldn't find.

•

Real ROI Metrics: How LLMs drove a 2x increase in topic detection accuracy and freed up 1.5x of agent bandwidth.

•

The Booking.com AI Stack: A full breakdown of their Orchestrator → Moderation → Agent → RAG architecture.

•

Latency vs. Intelligence: Why they don't use GPT-5 for everything and how they decide between small models and big brains.

•

The Memory Problem: How to build AI that remembers user preferences without being "creepy”.

00:00 Introduction to Agentic Architectures

00:30 Meet Pranav Pathak from Booking.com

01:24 Evolution of Travel Recommendations

03:41 Impact of Gen AI on Customer Service

07:29 Building an Effective AI Stack

10:32 Agentic Systems and Best Practices

13:45 Choosing Between Building and Buying AI Solutions

18:51 Evaluating AI Models for Business Use

24:10 Challenges in Human Evaluation

25:06 Recommendation System and Data Utilization

27:26 Innovations in Travel Search

29:04 Journey and Challenges in ML Integration

32:08 Managing Memory and User Data

38:07 Future of Travel Assistance

41:33 Advice for New AI Integrations

43:57 Final Thoughts and Farewell

🔗 LINKS & RESOURCES:

•

OutShift by Cisco (Sponsor): outshift.cisco.com

•

VentureBeat: www.venturebeat.com

#ArtificialIntelligence #GenAI #Bookingcom #MachineLearning #AgenticAI #LLM #TechPodcast #EnterpriseAI

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