F1 meets enterprise tech in Austin! Oct. 22, 2026, @PatrickMoorhead and @danielnewmanUV will be on their home turf at the @F1 US Grand Prix, speaking with @TotoWolff_ and @HPE CEO @AntonioNeri_HPE about their Mercedes-AMG PETRONAS F1 partnership and how AI, data, and advanced computing drive performance. Stay tuned!

The bStocks market map keeps expanding. 💻 Enterprise Tech | $CRMB 🩺 Digital Health | $HIMSB Two new additions, now available on bStocks. Use eligible bStocks as margin collateral and trade on margin.

“We’re seeing a ton of demand from companies who want to own their intelligence and not rent it.” In partnership with @Wing_VC's 2026 Enterprise Tech 30, Yash Patil, Co-Founder & CEO of @AppliedCompute, joins Nasdaq to explore the enterprise infrastructure shift their remarkable trajectory. Watch the full interview here:

“Private. Permissioned. Anchored to Ethereum. In use by @Memento_Bc : an institutional entry point to the Elastic Network designed for compliant asset origination.” — @zksync announcement on Prividium 🧵 (CryptoNews) 1/ What is Prividium? Prividium is a new model of institutional blockchain infrastructure from @zksync. It lets banks and enterprises run their own private, permissioned blockchain instances inside their infrastructure or cloud — not a consumer app, but enterprise tech designed for real-world compliance and privacy. (ZKsync Docs) 2/ Privacy & Control Unlike public chains where every transaction is visible, Prividium keeps transaction data, balances, and business logic completely private inside the institution’s environment. Only cryptographic proofs and state commitments are published to Ethereum — so integrity is verifiable without exposing sensitive details. (ZKsync Docs) 3/ Anchored to Ethereum Prividium doesn’t live in isolation. It uses zero-knowledge proofs to anchor every batch of private activity to Ethereum, giving enterprises Ethereum-level security and finality while keeping their data confidential. This hybrid model combines off-chain privacy with on-chain trust. (ZKsync Docs) 4/ Why This Matters for Web3 & Enterprise Adoption Traditional finance can’t put customer data on a public chain — regulators and auditors require privacy and control. Prividium solves this by offering enterprise governance, role-based access, and selective disclosure options while still connecting to the broader Ethereum ecosystem. (ZKsync Docs) 5/ Core Properties That Enable Institutional Adoption • Privacy — sensitive transaction details stay off public networks. (ZKsync Docs) • Compliance & Control — built-in support for access policies, auditability, and regulated workflows. (ZKsync Docs) • Connectivity — anchored proofs on Ethereum let institutions interoperate with DeFi, assets, and broader Web3. (MEXC) • Security — validity proofs posted to Ethereum ensure tamper-proof correctness. (ZKsync Docs) 6/ Prividium = Institutional Infrastructure This isn’t about consumer crypto — it’s about on-chain finance that meets enterprise demands: privacy, compliance, control, interoperability, and the cryptographic security of Ethereum. Prividium represents a bridge between TradFi and Web3. ( 👇 Curious about how this works under the hood or why big institutions are exploring it? Learn more about @zksync and Prividium — it’s shaping a new chapter for institutional blockchains.

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# Decision Points for Embedding AI Agents in Enterprise Systems # Online vs Offline Intelligence — When to Run Inference 🎯 The Hook Are you trying to run all your agent's inference in real time? Online (real-time at request time) and offline (pre-computed in batch) inference have fundamentally different profiles across latency, cost, and accuracy. Most production systems use both, with a hybrid architecture where offline pre-computes results and online applies the finishing touches 🔑 📋 Overview Online inference is essential for interactive use cases where user input cannot be predicted in advance — a Zendesk live chat agent answering in real time, or a Salesforce agent advising sales reps on next actions from an opportunity screen. Offline inference excels when processing large volumes of data on a regular schedule — analyzing all employee skill data overnight, or batch-optimizing all product descriptions for SEO monthly. In enterprise environments, mixing both according to workload characteristics is the standard approach 📊 🔍 Decision Points Two axes drive this decision: immediacy requirements and processing volume/cost. If the response must arrive within seconds, online is non-negotiable. Users abandon when latency exceeds roughly 30 seconds. If the processing target is hundreds of items or more as a batch, consider offline. Processing them one by one in real time makes costs prohibitive. If per-item inference cost is high (roughly $0.05 or more per item), offline batch processing is typically more cost-efficient. If the results can tolerate a staleness of several hours, offline becomes a strong candidate. If you need characteristics of both, adopt a hybrid: pre-compute offline, then apply differential corrections online ⚡ 💡 Key Details Three hybrid patterns to understand: Offline pre-compute, online finish — Generate product recommendation candidates in a nightly batch, then adjust the final ranking online based on the user's real-time behavior. Heavy inference is done offline; online handles only lightweight adjustments, balancing latency and cost. Online results aggregated offline — Analyze real-time customer interaction logs in offline batches to auto-update FAQs and improve response templates. Online experience feeds back into offline quality improvement — a virtuous cycle. Semantic cache layer — Accumulate responses for frequent queries in a semantic cache and serve similar queries from cache to reduce online costs. The key is matching on semantic similarity, not exact string matching 🔄 ⚖️ Trade-offs Resist the temptation to make everything online. Running large-scale data analysis synchronously pushes latency past 30 seconds, causing user abandonment and hitting API timeouts. Worse, failing to estimate monthly costs of routing all requests through an LLM leads to post-launch budget surprises 😩 On the flip side, offline freshness management matters. When batch execution frequency does not match data update frequency, you serve stale results. Showing inventory status with a one-day delay has real business impact. Without a mechanism to invalidate offline computation results when master data changes, data consistency breaks down. Offline batch error handling is another blind spot. If a batch fails midway and only partial results are reflected, data consistency is compromised ⚠️ 🛠️ Use Cases Customer support agent (Zendesk integration): Live chat answers are generated online in real time. Meanwhile, a nightly offline batch extracts frequent patterns from past interaction logs and updates the FAQ. Online response quality is continuously improved by offline analysis — a feedback loop 📚 Skill analysis agent (Workday integration): Analyze all employee skill data in a nightly offline batch to generate skill gap reports. When a manager asks online "Who are the best internal candidates for this position?", the agent responds instantly based on pre-computed skill matching results 🏢 Product recommendation agent (Shopify integration): Pre-generate product similarity calculations and per-segment recommendation candidates offline. Adjust the final recommendation ranking online in response to the user's real-time browsing behavior 🔧 Practical tip: Start by defining the target response time (P95 latency) for online interactions. Any processing that exceeds that target should be carved out to offline. Always estimate costs on a monthly basis and optimize the online-to-offline ratio accordingly 💪 #AIAgents# #EnterpriseArchitecture#

Practices for embedding AI agents into enterprise systems [Feedback Flywheel] 💡 Is your agent "deploy and forget"? You need a mechanism to channel production user feedback into a continuous improvement cycle. This pattern systematically collects user feedback, converts it into eval datasets, and drives ongoing quality improvement. 🔥 Problems it solves - No post-deployment quality improvement cycle exists - Quality degradation from model changes, data drift, or new query patterns goes undetected - No data to prioritize which problems to fix first 🏗️ Proposed pattern Collect both implicit signals (retries, abandonment, manual corrections) and explicit signals (thumbs up/down, text feedback, human escalations). Convert low-rated and escalated cases into eval datasets and add them to regression tests. Prioritize improvements by frequency and severity of negative feedback, then apply fixes to prompts, tools, routing, or RAG. Validate all changes through eval gates before redeployment. Since reviewing every interaction is impossible, start by sampling 100% of negative cases and 5-10% of successful ones, then adjust based on measured review capacity and improvement yield. ✅ Selection criteria - Good fit: Every production agent intended for ongoing operation - Not a fit: Throwaway experiments or PoCs ⚠️ Pitfalls - Ignoring implicit signals means you miss quality degradation that users never explicitly complain about - Without an operational process connecting collected feedback to improvement actions, data piles up with no impact - Failing to update golden datasets causes eval gates to become stale and miss new failure patterns 🛠️ Implementation Approach 1. Collect feedback via in-app UI and Slack reactions (thumbs up/down), and automatically detect implicit signals (retries, abandonment, manual corrections) as trackable events 2. Build a pipeline that automatically converts low-rated and escalated cases into eval datasets and adds them to the regression test suite 3. Create a dashboard that visualizes improvement priorities by frequency and severity of negative feedback (start with 100% of negative cases and 5-10% of successful ones as the sampling baseline) 4. Validate all prompt, tool, and routing changes through eval gates (P26) to confirm no quality regression before redeployment 5. Set up an A/B testing infrastructure combining LLM-as-judge automated evaluation with human review to quantitatively measure improvement impact #AIAgents# #EnterpriseArchitecture#

Practices for embedding AI agents into enterprise systems [Prompt Supply Chain Management] 💡 Has a "quiet prompt tweak" ever changed production behavior unexpectedly? This pattern treats prompts as code artifacts -- versioned, reviewed, signed, and eval-gated before deployment. 🔥 Problems it solves - Prompt changes cannot be tracked or reviewed, making it impossible to identify the cause of quality regressions - External inputs injected into prompt templates cause unintended behavior - No way to trace which prompt version produced a given result, destroying reproducibility 🏗️ Proposed pattern Manage prompts in Git with a dedicated registry. Require pull requests and code reviews for every change. Mandate eval gate passage on each update, automatically running quality checks against a golden dataset. Sign production-deployed prompts for tamper detection. Apply injection countermeasures -- delimiters, sanitization, and least-privilege scoping -- to all template variable insertion points. In essence, apply the same CI/CD quality process used for software to your prompts. ✅ Selection criteria - Good fit: Production agents with multiple contributors - Not a fit: Throwaway experiments or PoCs (governance overhead is not justified) ⚠️ Pitfalls - Without a culture that treats prompt changes as seriously as code changes, the process becomes hollow - Skipping signing and tamper detection leaves malicious or accidental changes undetectable - Failing to continuously update the golden dataset means new failure patterns slip through unnoticed 🛠️ Implementation Approach 1. Manage prompts, system instructions, and templates in a Git repository, requiring pull requests and code reviews for every change 2. Integrate an eval gate into the CI/CD pipeline to automatically run quality checks against a golden dataset on every prompt change 3. Sign production-deployed prompts with HMAC or similar mechanisms and verify integrity at deploy time for tamper detection 4. Implement injection countermeasures for external inputs in template variables -- delimiters, sanitization, and least-privilege scoping 5. Centralize version and deployment history in a prompt registry, enabling instant rollback when issues are detected in production #AIAgents# #EnterpriseArchitecture#

Practices for embedding AI agents into enterprise systems [Agent Registry / Internal Marketplace] 💡 Are similar agents proliferating across your organization with no coordination? This pattern catalogs all enterprise agents, tools, and skills to enable discovery, reuse, and centralized governance. 🔥 Problems it solves - Teams independently build similar agents, fragmenting resources and effort - Agents without proper eval or testing make it into production - Agents become unmaintained after their developer transfers to another team - No visibility into which agents hold what permissions and access what data 🏗️ Proposed pattern Attach metadata to each agent -- owner, target department, required permissions, eval results, cost history, SLA, and version -- and register them in a ServiceNow or Backstage-style service catalog. Enforce governance through request-and-approval workflows, and require eval gate passage before any new agent goes live. Track change history via versioning, and manage retirement through a staged lifecycle: deprecation, dependent notification, then shutdown. ✅ Selection criteria - Good fit: Organizations where agents are proliferating and duplicate development or orphaned agents are becoming a problem - Not a fit: Early stages with only a handful of agents (registry overhead is not justified) ⚠️ Pitfalls - Making registry enrollment optional leads to an empty catalog; embed it in the CI/CD pipeline to enforce compliance - Neglecting metadata freshness erodes trust in the catalog - Deleting agents without a deprecation process will break downstream agents and workflows without warning 🛠️ Implementation Approach 1. Leverage a service catalog platform (ServiceNow / Backstage) to centrally register metadata for every agent -- owner, target department, permissions, eval results, cost, and SLA 2. Embed registry enrollment into the CI/CD pipeline so that unregistered agents are automatically blocked from deployment 3. Require eval gate passage before any new agent goes live, preventing agents that fail quality standards from reaching production 4. Introduce versioning and change-history tracking so that every modification is auditable -- who changed what and when 5. Automate the retirement lifecycle (deprecation flag, dependent notification, grace period, shutdown) to prevent cascading failures from abrupt removal #AIAgents# #EnterpriseArchitecture#

Practices for embedding AI agents into enterprise systems [Department-Scoped Agent] 💡 Is your sales question being answered with IT department knowledge? This pattern defines agent scope along organizational boundaries, naturally separating permissions, knowledge, and tone. 🔥 Problems it solves - Cannot reflect department-specific data access permissions and domain knowledge in agents - Agent ownership and accountability boundaries become unclear - Sales jargon, IT terminology, and HR policies get mixed together - Knowledge scattered across Slack, docs, and tickets becomes inaccessible when projects end 🏗️ Proposed pattern Structured around two axes: department scope (permanent) and project scope (temporary). For department scope, sync the org graph and permissions from Workday or Okta, and configure each department agent with its own tools, data boundaries, and tone. A supervisor routes queries to the right department agent. For project scope, automatically provision agent workspaces when Jira or Asana projects are created, and archive them on completion -- distilling useful knowledge into the organizational knowledge graph. ✅ Selection criteria - Good fit: Large enterprises where workflows, tools, and regulations differ significantly by department; organizations with project-based work - Not a fit: Organizations where cross-functional work dominates and department boundaries are meaningless; purely routine operations ⚠️ Pitfalls - If agent scopes don't track org changes, permission mismatches will occur - Routing accuracy for cross-department queries needs empirical tuning - Neglecting knowledge distillation at project close means valuable insights are lost forever 🛠️ Implementation Approach 1. Sync the org graph from Workday / Okta via SCIM and dynamically maintain agent scopes based on department membership 2. Deploy a supervisor/router at the entry point to classify user intent and automatically route queries to the appropriate department agent (Sales, IT, HR, etc.) 3. Constrain each department agent's tools, data boundaries, and system prompt at the department level, managing permissions with ReBAC (Relationship-Based Access Control) 4. Trigger automatic provisioning of project-scoped agent workspaces on Jira / Asana project creation events, and archive them on project close 5. Build a pipeline that automatically distills valuable project knowledge into semantic memory and the organizational knowledge graph at project end #AIAgents# #EnterpriseArchitecture#