标签：#Cisco

Google and industry partners announced Agentic Resource Discovery (ARD) Specification, an open standard for publishing, discovering, and verifying AI tools, APIs, and agents. ARD introduces a discovery layer built on catalogs and registries, enabling dynamic capability discovery while leveraging existing protocols such as MCP and OpenAPI for execution and emphasizing trust and interoperability. By Leela Kumili

Luca Mezzalira shares proven learnings from guiding hundreds of teams through the migration from monolithic web applications to distributed frontend architectures. He explains the core architectural difference between components and micro-frontends, outlines a 6-step decision framework spanning client vs. server rendering, and discusses how to utilize edge compute for safe, iterative rollouts. By Luca Mezzalira

This is Part 1 of a 5-part series on Cisco EEM. We start here with the fundamentals and a few working applets, then build toward self-healing networks, automated diagnostics, compliance guardrails, and a complete real-world deployment. Ask ten network engineers what they use EEM for, and nine will say the same thing: "Oh, I have an applet that auto-recovers err-disabled ports." Then they never touch it again. That's a shame, because Embedded Event Manager is one of the most capable tools already sitting inside every Cisco IOS device you own — and almost nobody uses more than 1% of it. It's a full automation engine that lives on the box . No external server. No API gateway. No orchestration platform. Just the router or switch, watching itself, ready to react the instant something happens — even if the WAN is cut, the NMS is down, and it's three in the morning. This series is about using that other 99%. By the end you'll have a toolkit of applets you can deploy and, more importantly, a way of thinking about on-box automation. But we start at the foundation: what EEM actually is, how it's put together, and why "on the box" is a bigger deal than it sounds. What EEM actually is Embedded Event Manager is an event-driven automation framework built into Cisco IOS, IOS-XE, and NX-OS. Strip away the jargon and it's a very simple idea: When something happens, do something about it — automatically, on the device itself. That "something happens" is an event . That "do something" is one or more actions . Bundle an event with its actions and you have an applet — the basic unit of EEM. That's the whole model. The power comes from how many different things can be an event , and how much an action can do. Events EEM can watch for include: A syslog message matching a pattern (an interface flapping, an HSRP state change, a config being saved). An SNMP OID crossing a threshold (CPU over 85%, a power supply going absent, temperature rising). A CLI command being entered (someone typing wr

Raj Ummadisetty and Ken Kurzweil share Netflix's architectural pivot to CloudStream, a repeatable capture, conversion, and deployment framework. They discuss shifting key-value abstractions from stateless to stateful to move terabytes of bulk data safely. Software architects will learn to exploit data access patterns, use "Pathfinder" prototypes, and maintain a 99% faster rollout. By Rajasekhar Ummadisetty, Ken Kurzweil

But the devices could "easily be reflashed."

Aaron Erickson explains how NVIDIA designs and tests purpose-built AI agent hierarchies. For senior developers and architects, he outlines why balancing deterministic tools with agentic discovery is crucial. Discover how to leverage rare context, implement LLM-as-a-judge test pyramids, and avoid the paradox of choice to build highly reliable, production-grade AI systems at scale. By Aaron Erickson

The speakers discuss Netflix’s architecture for surviving extreme traffic spikes. They explain the mechanics of prioritized load shedding embedded in their Envoy sidecar proxy, allowing user-initiated requests to steal capacity from non-critical traffic. They share automated platform strategies for continuous chaos load testing, config generation, and retry storm mitigation. By Anirudh Mendiratta, Benjamin Fedorka

Sriram Madapusi Vasudevan discusses industry-converging patterns for securing autonomous AI agents in production. He explains the critical vulnerabilities hidden inside the ReAct loop across context, reasoning, and tool execution. He shares how to mitigate risks like memory poisoning and rogue tool execution using defense-in-depth strategies, LLM-as-a-judge critics, and MAESTRO threat modeling. By Sriram Madapusi Vasudevan

Spencer Judge discusses the architectural pattern of building a shared core in Rust with language-specific layers on top. Drawing from his work on Temporal's SDKs, he shares lessons on navigating FFI boundaries, bridging async concepts, and managing memory safely. He explains the limitations of native extensions and how emerging tech like WebAssembly can streamline cross-language architecture. By Spencer Judge

Sean Klein discusses why "human error" is a dangerous myth in complex systems. Sharing the inside story of Azure’s 2023 global WAN outage, he explains how modern incident analysis looks past the "Five Whys" to uncover systemic issues. Learn how engineering leaders can move away from blame, improve Standard Operating Procedures, and design resilient systems that actively protect their engineers. By Sean Klein

We constantly hear that Ansible and Python are apparently the only ways to automate networks, today I even listen in a conversation "Python is the industry standard" probably I missed the RFC document or probably the guy was referring to a sales standard, but back to us what happens when the framework, the platform or the software we are using becomes heavier than the problem to solve? There is a moment where automation becomes necessary, not because we want to look modern, not because every task deserves a framework and not simply because adding automation automatically means we are doing things better. It becomes necessary because repeating the same command collection manually across many devices is slow, risky, boring and almost impossible to diff and validate properly especially under pressure. For this reason I built the Cisco Go Collector during a real migration activity with a very practical goal: collect configuration and command outputs from Cisco devices in an easily repeatable way, without forcing every colleague involved in the process to become developers or to install an automation stack just to run a super simple flow. The idea was simple: define the devices in a CSV which is the comfort zone for everyone define the commands in the same CSV file, super simple and organized to manage one row per device run a portable Go binary against that CSV file collect the outputs in organized text files archive the result as operational evidence that can be easily diff That is it! super lightweight to run no Python virtual environment no Ansible playbook structure no inventory hierarchy no framework onboarding no additional runtime or software on corporate managed workstations just a CSV file and a compiled binary The automation and AI trap when the solution is heavier than the problem to solve I love automation and I fully support AI if used the proper way, but we have to find a balance and recognize when to choose one tool over the other and specially one progra

Vinay Chella and Akshat Goel discuss the challenges of running traditional CDC across heterogeneous databases during peak order traffic. They explain how Debezium hit limits under high load and share how they built Write-Ahead Intent Log (WAIL) - a custom architecture that utilizes a dumb producer proxy and a smart consumer pattern to cleanly separate the intent from the state payload. By Vinay Chella, Akshat Goel

Paul Klein discusses the distributed systems challenges of scaling cloud-hosted browser infra for AI agents. He explains how to manage bursty, stateful multi-tenancy and secure Chromium environments against remote code execution using Firecracker. He also shares how to leverage the Model Context Protocol (MCP) to turn complex websites into accessible agentic tools. By Paul Klein

Cindy Zhang discusses the evolution of XDS, a unified UI system powering 10,000+ internal tools. She shares actionable insights for architects and engineering leaders on managing large-scale community contributions, executing safe monorepo refactors using JS AST and AI codemods, mitigating breaking changes via feature flags, and expanding UI libraries into full-stack platform systems. By Cindy Zhang

Netflix engineer Casey Bleifer shares how to achieve rapid, automated code changes across a massive, diverse software fleet. She discusses building an event-driven orchestration platform using composable, Lego-like steps, and explains how Netflix utilizes automated canary validation, compliance checks, and a custom "confidence metric" to eliminate the long tail of manual engineering migrations. By Casey Bleifer

The speakers discuss the architectural challenges of executing safe data deletion across distributed datastores. Balancing durability, availability & correctness, they explain how to orchestrate multi-system deletion propagation without impacting live traffic. They share lessons on controlling tombstone accumulation, building continuous audit loops, and gaining trust with a centralized platform. By Vidhya Arvind, Shawn Liu

Several real estate listings in the San Francisco Bay Area are offering to exchange a home for a piece of the AI startup.

Kyle Lexmond explains how to handle the high-pressure environment of severe production outages. He discusses the critical distinction between mitigation and root-cause resolution, sharing personal experiences from harrowing incident rooms. He shares valuable operational strategies on overcoming cognitive overload, establishing blameless cultures, and optimizing systems for faster recovery. By Kyle Lexmond

Trisha Ballakur discusses her journey from a backend software engineer to CTO and CEO, using her startup Pointz as a case study. She explains how to implement bottom-up customer discovery to find product-market fit, effectively delegate to global contractors to reduce build times, customize open-source repos like Valhalla, and apply engineering test-case models to business development. By Trisha Ballakur

Joseph Stein discusses engineering an enterprise AI-as-a-Service platform within a private cloud data center. He explains how to maximize underutilized GPU pools via multi-namespace scheduling, leverage Valkey and Lua for atomic priority queuing and backpressure management, mitigate OWASP Top 10 LLM risks via central proxy gateways, and scale batch pipelines using a custom S3-to-Kafka proxy. By Joseph Stein