Day 1 at @Ai4Conferences showed that bigger models are only part of the AI story. From chips and compute to enterprise agents, governance, security and ROI, we heard from @googlecloud, @dataiku and @okta on what it takes to move AI into the real world froom @teslaownersSV #ChinaAMC# #CNQQ# #AI4# #AI# #ArtificialIntelligence# #AgenticAI# #EnterpriseAI# #FutureOfAI# Risk Disclaimer: Investment involves risk, including possible loss of principal. Any forecasts, projections, or opinions contained herein are for reference only and are not guaranteed to occur. The information in this material reflects prevailing market conditions and our judgment as of the release date, which are subject to change without further notice.

When Dell's experimental AI agents wrote their own code two years ago, it became a wakeup call for agentic security. Listen to John Roese and Harish Peri from @Okta discuss what it takes to build the security layer every agentic enterprise needs. 🎥

Day 1 Recap video of @Ai4Conferences w/ @ChinaAMC_HQ Bigger models alone are no longer enough. The real race is now about building the foundation for the agentic AI economy — infrastructure, compute, cloud, security, and data platforms that can actually scale. From the keynotes on chips, constraints, and the next generation of compute, to conversations on the floor, one theme kept repeating: AI cannot scale without the right infrastructure.We sat down with Carlos Huisa from Dataiku, Jason Davenport from Google Cloud, and Matt Carter from Okta. They all pointed to the same shift — moving from experimental AI to production-ready agentic systems that can operate reliably inside the enterprise. Platforms, identity, governance, and cloud infrastructure are becoming just as critical as the models themselves. Here at AI4, we’re seeing how AI is reshaping industries — and CNQQ ETF offers investors access to China’s broader innovation ecosystem, spanning AI, semiconductors, advanced manufacturing, and digital platforms. The agentic future is no longer coming. It’s already being built. #ChinaAMC# #CNQQ#

Practices for Integrating AI Agents into Enterprise Systems 【Trust Boundary Split】 💡 Catchy Message "Running your customer-facing and employee-facing agents on the same stack? That's a ticking time bomb for data leakage." Employee agents and customer agents have fundamentally different trust levels, data boundaries, and failure costs. Treating them as one design is how internal data leaks through customer-facing channels. 🔥 Problems Solved - Internal data leakage through customer-facing agent paths (the most critical risk) - Adversarial input exploitation (jailbreaks, indirect prompt injection) causing runaway behavior - Accidentally applying employee-tier "relaxed assumptions" to customer-facing agents - Irreversible brand damage and legal liability from customer-facing failures 🏗️ The Pattern Separate employee-facing and customer-facing agents into two distinct trust planes -- physically or logically isolated. Employee agents authenticate via corporate IdP (Okta/Entra ID) with broad internal data access. Customer agents live in a DMZ-equivalent isolated environment, accessing only explicitly published read models (projections of approved data). All customer-facing output must pass through DLP (Data Loss Prevention) inspection. Guardrails for the customer plane are significantly stricter: jailbreak detection, topic restrictions, tone control, and denial policies. You can share orchestration infrastructure and model gateways, but data access paths must always be separated. ✅ When to Adopt - Use when: Both customers and employees use agents. B2C/B2B companies where customer touchpoints and internal operations share infrastructure. - Skip when: Purely internal-only use cases (separation adds cost without benefit). ⚠️ Pitfalls - Sharing orchestration infrastructure and mistakenly assuming data paths are also safe. Infrastructure sharing and data path separation must coexist. - Deferring customer-facing guardrail design to "phase 2." Jailbreak detection, topic restrictions, and denial policies must be part of the initial architecture. - Neglecting audit log design for customer identifiers and PII retention policies, leading to compliance violations. 🛠️ Implementation Approach - Set up network segmentation to isolate the customer plane. Deploy customer-facing agents in a DMZ-equivalent environment using VPC/subnet separation, blocking direct access to internal networks. - Build CQRS read models (projections of approved public data) for the customer plane. Replace direct internal DB access with curated views of explicitly publishable data only. - Deploy a DLP (Data Loss Prevention) inspection pipeline on the customer-facing output path. Route all output through this pipeline to detect and block internal data leakage. - Apply strict guardrail policies to the customer plane: jailbreak detection, topic restrictions, tone control, and denial policies -- significantly stricter than the employee plane. - Separate authentication infrastructure. Use corporate IdP (Okta/Entra ID) with SSO for employees and customer IdP (Auth0/CIAM) for customers, keeping auth paths fully independent. #AIAgents# #EnterpriseArchitecture#

An agent that actually gets work done needs a whole new stack: identity, search, documents, code, and the ability to pay. Last night in San Francisco, that whole stack was in one room. Thanks to Sapient for hosting Agent Experience Demo Night at Okta, and for putting Kite on stage alongside @auth0, @DocuSign, @Box, @nebiusai, @coderabbitai, and @tavilyai. Our Co-Founder & CTO @scottshics gave a first look at a developer product coming later this month. Deploying a live agent on Kite, he showed how one developer can ship an AI agent as a product without runaway costs: ▷ Per-user spend visibility, so there's no end-of-month bill shock ▷ Cost controls that downgrade or stop an agent at a set limit ▷ Usage-based pricing, so every user is billed for what they actually cost More of these conversations to come. 🪁

Before an AI agent can actually get work done, it needs a whole new stack: an identity, and the ability to search, to read documents, to review code, and to pay. Tomorrow in San Francisco, @GoKiteAI shares the stage at Agent Experience Demo Night, hosted by Sapient, with a lineup of teams building the rest of that stack: ▷ @auth0: the identity and login layer, acquired by Okta ▷ @Docusign (Nasdaq: DOCU): the global standard for e-signature and agreements ▷ @Box (NYSE: BOX): enterprise content and documents in the cloud ▷ @nebiusai (Nasdaq: NBIS): AI cloud and GPU infrastructure for training and inference ▷ @coderabbitai: AI code review on every pull request ▷ @tavilyai: the search API built for AI agents Our Co-Founder & CTO @scottshics will demo Kite's piece: a verifiable identity for every agent, and the ability to spend within limits you set. 📅 July 7th (Tue), 5:00-9:00 PM PDT 📍 Okta, 100 1st St, San Francisco Register (host approval required): See you there. 🪁

Practices for embedding AI agents into enterprise systems [Semantic Layer -- Unified Metrics & Organizational Knowledge Graph] 💡 Ask your AI "What's our revenue?" and you get... gross or net? Bookings or cash? FY or CY? An AI that answers without definitions is a tool that's precisely wrong. Centralize the "meaning" of metrics and organization, replacing hallucination with defined facts. 🔥 Problems Solved - Metric/terminology hallucination: AI generates incorrect numbers because "revenue" was never precisely defined - Unresolved references: ambiguous phrases like "my team" or "last month" cannot be accurately resolved - Missing organizational scope: no way to control data boundaries by department or project hierarchy 🏗️ Proposed Pattern Centralize metric definitions in a BI semantic layer (dbt Semantic Layer / Cube) -- e.g., "Revenue = sum of order amounts, tax-excluded, on FY basis." Sync the organizational graph from SCIM/HRIS (Workday, etc.) so "my team's revenue" auto-resolves to "sum of order amounts for members in the user's department." Natural language ambiguity is resolved with defined facts, not hallucination. ✅ Selection Criteria - When to use: analytics-supporting agents, cross-org workflows, permission-dependent processing, metric-critical operations - When NOT: exploratory domains where definitions are not yet established (stabilize definitions first) ⚠️ Pitfalls - Definition maintenance cost: you need an operational workflow to keep metric definitions and org graphs fresh - Granularity balance: too fine-grained and management collapses; too coarse and ambiguity remains -- start with high-frequency metrics - Organizational change tracking: in orgs with frequent reorgs and transfers, SCIM sync frequency and timing become critical 🛠️ Implementation Approach 1. Centralize metric definitions in dbt Semantic Layer / Cube (e.g., "Revenue = sum of order amounts, tax-excluded, FY basis") and connect them as a first-class context source for agents 2. Build an organizational knowledge graph (people, departments, projects, roles, permissions) in Neo4j / Amazon Neptune, synced from Workday / Okta via SCIM 3. Implement a natural-language-to-defined-metric mapping layer that auto-resolves "my team's revenue" to "sum of order amounts for members in the user's department" 4. Progressively formalize definitions starting with high-frequency metrics, and establish a periodic review workflow to keep definitions fresh 5. Detect organizational changes (reorgs, transfers) via SCIM sync webhooks for near-real-time updates, minimizing scope control lag #AIAgents# #EnterpriseArchitecture#