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Most automation fails the same way: quietly, until it doesn't. One Platform. Every Signal. Finally. body, table, td, a { -webkit-text-size-adjust: 100%; -ms-text-size-adjust: 100%; } table, td { mso-table-lspace: 0pt; mso-table-rspace: 0pt; } img { -ms-interpolation-mode: bicubic; border: 0; outline: none; text-decoration: none; display: block; } body { margin: 0; padding: 0; background-color: #f0edf7; font-family: Arial, sans-serif; } @media only screen and (max-width: 100%; } .hero-text { font-size: 26px !important; line-height: 34px !important; } .btn { width: 100%; } } Sponsored by Datadog via AWS Marketplace datadog For network engineers managing production AI infrastructure More Network.More AI Models.Same Visibility Window. Production networks have always been complex. Add AI agents, LLM inference endpoints, and multi-cloud pipelines and the blast radius of a blind spot just got a lot bigger. Datadog gives network engineers and infrastructure teams unified observability across every layer — from physical hosts to cloud services to the models running on top of them. Request a Demo → When something breaks at 2am — a degraded BGP peer, a spiking latency on your inference API, a cloud NAT gateway silently dropping packets — the last thing you want is to stitch together signals from five different monitoring tools. You want answers, not archaeology. Datadog consolidates infrastructure monitoring, network performance, APM, logs, and security into a single platform with over 600 vendor-backed integrations. One agent. One console. Everything correlated automatically so you spend time fixing, not finding. Full-Stack Visibility for Modern Infrastructure 🌐 Network Performance Monitoring Real-time traffic flows, latency mapping, and packet loss detection across cloud and on-prem — without deploying dedicated probes or managing a separate tool. 🤖 AI & LLM Observability Monitor AI model inference latency, token throughput, and error rates alongside the infrastructure running them — purpose-built visibility for AI workloads in production. 📊 Infrastructure & APM Host, container, and Kubernetes metrics unified with distributed traces — correlate a network anomaly with a degraded upstream service in seconds, not a war-room call. 🔍 Watchdog AI & Anomaly Detection ML-powered anomaly detection surfaces issues before they become incidents — no manual threshold-tuning, no alert storms. Watchdog flags what matters and explains why. 4.4/5 G2 Rating 771 Verified Reviews 600+ Integrations "Datadog gives us one place to look when something goes wrong — network, infrastructure, and application all in the same pane. We stopped context-switching between tools and started actually fixing things faster." — Verified G2 Review, 2026 Available on AWS Marketplace Starts at $27/host/month. Quick Launch via AWS CloudFormation — no procurement overhead, billed through your existing AWS account. Annual contracts save up to 20%. Stop Chasing SignalsAcross Five Different Dashboards. Get full-stack observability — infrastructure, network, AI workloads, and security — in one place. Available on AWS Marketplace. Request a Demo → This is a message delivered in partnership with Datadog via AWS Marketplace, delivered via The AI Network Engineer by Packt. Six months ago the question most teams were asking was whether AI could do anything useful in infrastructure. That one's settled. The question that replaced it is harder: how do you make it work reliably, without it falling over the first time something unexpected happens in production.That thread runs through the next three sessions, and I'll be in all of them.Engineering Agentic Network Operations | June 30th⚡ 4 Days Left - Last 10 SeatsThis is the one to not miss this week. John Capobianco and William Collins are running a live, hands-on workshop Monday morning and the honest focus is not "how to build an MCP server" but how to run one in production without it becoming another thing to babysit.You'll leave with:Production-ready MCP skills you can reuse across your environment, not one-off integrations that only one person understandsA spec-driven approach to actually constrain agent behavior before it creates operational riskAgentic loops designed to recover when they get stuck with safe handoffs, not silent failuresLive labs. Real environments. OpenClaw, NetClaw, Selector AI, Containerlab, Arista cEOS.📅 Tuesday, June 30th | 9:00 AM – 12:00 PM EDTUse code FINAL40 to get 40% off - LAST 10 SEATSCLAIM YOUR SEAT The same problem lives one layer down. Agents in the network layer still run on Linux systems, and the shell doesn't ask for confirmation before it acts. That's what makes July 9th worth your time even if networking isn't your primary domain.Agentic Linux - From Commands to AI Agents | July 9th⚡ Free to Join - Register nowThe shell doesn't ask for confirmation, which is exactly why this session keeps human review at the centre throughout.Imran Afzal has trained 1M+ students and spent 25+ years running Linux infrastructure at Fortune 500s before he started teaching it. This is hands-on with ChatGPT CLI, Shell Genie, Aider, and Goose CLI, generating commands from plain English, reading logs, and reviewing output before anything runs.Free to join. Certification and e-book add-ons available if you want to take it further.📅 Thursday, July 9th | 11:30 AM EDTGET YOUR FREE PASSAgentic DevOps with Claude | July 23rd⚡ Early Bird Live Now, 40% OffClaude Code is the engineer. You're watching it work.Four hours. A 33-component AI-native IDP built live on a real Kubernetes cluster, ArgoCD, Backstage, kgateway, observability stack included. The cluster is provisioned for you. You leave with the repo and a working reference architecture to take back to your team.Michael Rishi Forrester from Accenture, prev- KodeKloud is running this one. Limited Seats📅 Thursday, July 23rd | 11:00 AM EDTUse code EARLY40 for 40% off.LOCK IN EARLY BIRDThree sessions. The Linux layer, the network layer, the DevOps layer, the full agentic infrastructure stack, all before August.These are the wrong ones to bookmark and come back to later. The useful questions come up live, in the labs, when the agent does something slightly unexpected and everyone has to work through what should have happened next.Hope to see you there.Cheers,Sayali PEditor-in-ChiefP.S. June 30th is Tuesday. Use code FINAL40 at checkout, seats are nearly gone.*{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{font-size:75%;line-height:0}#converted-body .list_block ol,#converted-body .list_block ul,.body [class~=x_list_block] ol,.body [class~=x_list_block] ul,u+.body .list_block ol,u+.body .list_block ul{padding-left:20px} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;display:none;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}.social_block .social-table{display:inline-block!important}} body, table, td, a { -webkit-text-size-adjust: 100%; -ms-text-size-adjust: 100%; } table, td { mso-table-lspace: 0pt; mso-table-rspace: 0pt; } img { -ms-interpolation-mode: bicubic; border: 0; outline: none; text-decoration: none; display: block; } body { margin: 0; padding: 0; background-color: #f0edf7; font-family: Arial, sans-serif; } @media only screen and (max-width: 100%; } .hero-text { font-size: 26px !important; line-height: 34px !important; } .btn { width: 100%; } }

This week’s stories ask what it takes to build AI that can be trusted AI_Distilled #142: What’s New in AI This Week Tame Your AI Monsters: CLAUDE EDITION Join the First Episode of the Exclusive AI Agent Governance Lab Series. Rubrik runs Claude. This is what we learned. We're the first enterprise to put Rubrik Agent Cloud to work governing our own Claude implementation — and on June 30, we're opening that experience up. Secure Your Spot LLM Expert Insights, Packt P.S. If Denis Rothman’s Expert Insight ends up being your kind of read, you’ll probably enjoy Agentic Engineering, our new publication for builders navigating AI beyond the demo. Early subscribers are still receiving a free copy of AI Agents in Practice by Valentina Alto. Subscribe Here LATEST DEVELOPMENTS 🏛️ Governments take a closer interest in frontier AI models - Reports suggest the U.S. government is taking a more active role in overseeing the release of advanced AI models, with OpenAI reportedly limiting access to future frontier models and Anthropic previously facing scrutiny over model deployment. The developments underscore how national security concerns are becoming an increasingly important factor in how the most capable AI systems are released. ☁️ Amazon deepens AI infrastructure investment in India - Amazon will invest an additional $13 billion to expand its AI and cloud infrastructure in India, bringing its planned investment in the country to $48 billion by 2030. The expansion will grow AWS data center capacity in Mumbai and Hyderabad, reinforcing India’s position as a key market in the global race to build AI infrastructure. 🧪 AI distillation comes under renewed scrutiny - Anthropic has accused Alibaba of using “adversarial distillation” to replicate the capabilities of its Claude models, an allegation Alibaba denies. The dispute has thrust AI distillation into the spotlight, highlighting growing concerns over whether using another company’s models to train new AI systems crosses the line from accepted optimization to intellectual property infringement. 💳 Airwallex raises $320 million to expand AI-powered finance - Airwallex has raised $320 million at an $11 billion valuation as it accelerates its push into AI-native financial software and agentic commerce. The fintech also unveiled new AI products designed to automate corporate finance workflows and enable AI agents to make delegated payments, reflecting growing investment in autonomous financial systems. 📈EXPERT INSIGHTS Denis Rothman on the illusion of autonomous agents (part 1) The AI community is caught in a contradiction. On one hand, there is a push to deploy autonomous agents into enterprise environments, expecting them to reason, plan, and execute complex, multi-step workflows. On the other hand, the methodology relied upon to build these agents is overwhelmingly based on prompt engineering, which is merely an attempt to cajole predictable, deterministic behavior out of fundamentally stochastic, black-box LLMs. This creates a pervasive dissonance: the expectation of industrial reliability built atop probabilistic generation. The current noise suggests that if we scale the parameters, refine the system prompts, or throw more compute at the problem, true autonomous reasoning will spontaneously emerge. To cut through this noise, we must confront an uncomfortable reality. Unconstrained probabilistic generation cannot serve as the kinematics for reliable robotic or enterprise execution. If we are to build truly agentic systems, we must move beyond the brittle, zero-context art of prompting and embrace the rigorous, transparent discipline of context engineering. Read the Full Article on Substack If this Expert Insight article made you think, you’ll probably enjoy our new publication, AgenticEngineering. We’re less interested in asking whether agents can do something, and more interested in what it takes to make them work reliably in production. Early subscribers are still receiving a free copy of AI Agents in Practice by Valentina Alto. Subscribe and grab your free e-book instantly Built something cool? Tell us. Whether it's a scrappy prototype or a production-grade agent, we want to hear how you're putting generative AI to work. Drop us your story at nimishad@packtpub.com or reply to this email, and you could get featured in an upcoming issue of AI_Distilled. 📢 If your company is interested in reaching an audience of developers and, technical professionals, and decision makers, you may want toadvertise with us. If you have any comments or feedback, just reply back to this email. Thanks for reading and have a great day! That’s a wrap for this week’s edition of AI_Distilled 🧠⚙️ We would love to know what you thought—your feedback helps us keep leveling up. 👉 Drop your rating here Thanks for reading, The AI_Distilled Team (Curated by humans. 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