Operations | Monitoring | ITSM | DevOps | Cloud

The same agent that reads a Kubernetes cluster reads a Windows machine. One MCP server, one config entry, and the questions are the ones you already ask.

You get an answer from the agent and no way to check how it got there. The route it took is recorded, and so is the reason it gave for taking it. AURA emits OpenTelemetry spans, and the Docker quickstart wires them straight into Phoenix. Four services come up together: AURA Web Server as the persistent agent harness, LibreChat as a browser interface for chatting with the agent, Phoenix to receive the spans, and MongoDB to store stateful data for LibreChat. The Compose file arrives pre-configured to point AURA at Phoenix and to enable content recording for the local demo.

Building an agent and not sure how to approach something? Bring it. AURA office hours are recurring working sessions with the people who build it. The team has been talking to people trying out AURA and hearing the same good questions come up more than once. Office hours are the answer to that: a standing slot on a schedule, rather than one conversation at a time. The format is deliberately loose. Nobody is arriving with thirty slides to spend an hour talking at you. The session goes wherever the questions go.

See how Splunk delivers end-to-end visibility across payment rails — monitoring transaction flows in real time to catch failures, latency, and anomalies before they hit customers. Learn how financial institutions use that insight to protect uptime, reduce fraud risk, and keep every payment moving.

A big tool result does not have to be a big context cost. AURA moves it to disk and hands the model a pointer plus the tools to navigate what is there. A large MCP tool result can consume or overflow an agent's context window, and on a third-party server you do not control how much comes back. Scratchpad breaks the link between how big a tool result is and how much context it costs: the full output goes to disk, and only the slice the model asks for ever enters the window. Errors always pass through inline, so the model can react to them.

The model, the serving layer, and the agent asking questions all sit on local hardware. The first thing it debugs is the AI stack it is running on. No external inference provider in the path.

Give one agent every tool and every incident is a question of trust. This one hands each job to a worker that can only reach what that job needs. One AURA configuration defines a coordinator and three specialist workers. Qdrant stores the runbooks, Prometheus measures workload health, and Kubernetes provides inspection and remediation, and each of the three is wired to one worker.

On this episode of Masters of Data, we sat down with Patrick Kobly, who runs security for a boutique MSSP serving fintech, crypto, and gaming clients, to dig into how phishing has evolved past the obvious tells. Kobly walks through how attackers spin up reverse proxies behind Cloudflare, route through residential IPs to dodge reputation-based blocking, and can take a fake domain from registration to full attack in under five hours. The conversation turns into an unexpected case for treating SEO as a security discipline, since search rank and AI-generated results are now part of the attack surface too.

This video provides an overview of Macros in Sumo Logic and how they can be used in search queries. It also introduces nested Macros and Macros with arguments, with examples of how Macros can be incorporated into searches.

This video demonstrates how to use Mobot to investigate issues, interpret its findings, and identify recommended next steps. Follow along as Mobot responds to a prompt by understanding your intent, gathering relevant data, performing multi-step analysis, reasoning across data sources, surfacing insights, and recommending next steps.

An on-call question returns the threshold and the escalation owner from your own runbooks, and the answer comes back without a call to anyone outside. AURA runs against Bedrock as its model provider, using Claude Sonnet 5 served by AWS in the same region. Authentication is the normal AWS credential chain: a profile on a laptop, an IAM role in EKS.

This video introduces Mobot, an AI-powered chat assistant built into Sumo Logic, and provides an overview of its key capabilities, the AI features that power it, alongside the Unified Conversational Interface. It gives you a foundation for understanding how Mobot can support and accelerate investigative workflows.

#claudecode #anthropic #devops

Welcome to Episode 4 of Live Laugh Logs, the podcast from the Coralogix Developer Relations team. This week, Chris Cooney joins Annie to share five key DevOps skills that have become even more important in the age of agentic code development, and gives you five key actions you can do today to start levelling up these skills. Subscribe to our channel for more insights into observability and AI.

The platform shows you which service is failing and which paths it touches, and stops there. Point AURA at the same telemetry and the cause comes back too. Dash0 shows the product catalog service in a failed state across the selected window, with errors on the path from the frontend service.

You ask for a routine health check and expect a clean baseline. What came back was a pod that had restarted 788 times, unrelated to the question. AURA is connected to a Kubernetes cluster and to Prometheus through read-only MCP servers, running as one coordinator with two specialized workers. The prompt is one sentence: check the health of the cluster, and confirm whether all the pods are running. What comes back is not a baseline. AURA names the state as CrashLoopBackOff and attaches the restart count to it.

#loopengineering #claudecode #programming #observability

Knowing why it broke is not the same as having it repaired. Point the agent at the repos behind the service and the change comes back as a pull request. A Govee integration crash-loops under Home Assistant because the container cannot write to a directory it does not own. That much was already established: the previous homelab video stopped at the root cause on purpose, so the next pass could improve the agent's configuration first.

In this session, we will introduce your new "digital teammate"—the supercharged Splunk AI Assistant. We’ll demonstrate how the new Agent Mode provides the context, reasoning, and recommendations necessary to reduce your mean time to resolution (MTTR) from hours to minutes.

Tech Talk features a live demonstration of the new SPL2 Search Mode and explores how to leverage SPL2 modules and apps to streamline your investigations. Whether you are building custom solutions or investigating security threats, learn how to turbocharge your use cases with a flexible language that supercharges our original SPL.

During this Tech Talk we’ll share how AI Canvas in Splunk can help your team unify across domains via a shared, synchronous workspace where operators, agents, and stakeholders see the same pinned evidence, timelines, and conclusions in real time.

During this Tech Talk, we will demonstrate how to move beyond manual toil with automated field extraction, guided onboarding, auto-schematization, and self-healing pipelines. You will learn how to streamline building data pipelines, reduce operational overhead, and ensure your data is always analytics-ready for your most critical use cases.

Discover how our latest tools and insights help you maximize value, optimize total cost of ownership, and accelerate innovation. We’re committed to helping you achieve outcomes that matter most to your business, today and into the future by turning your data into doing.

We’re diving into how you can bring Infrastructure as Code (IaC) principles to your Splunk environment. Whether you’re already a Terraform pro or just starting to explore how IaC can simplify your workflow, this Tech Talk will give you a solid look at what’s possible.

Introducing the Splunk Agent Launchpad! Let’s face it—your team is busy. Between managing alerts, digging through investigations, and constant context-switching, it’s hard to stay ahead of the noise. What if you could turn your existing operational knowledge into custom AI agents that do the heavy lifting for you? And the best part? No coding required. Watch this exclusive look at the Splunk Agent Launchpad. We’re showing you how to build, deploy, and manage AI agents that help you investigate, enrich, summarize, and act—all without leaving the Splunk environment you already know and trust.

A deployment fails validation and the sync stops. Argo CD hands the report to AURA, which finds the wrong version, fixes it, and re-runs the sync. Normally, a failed sync means a person opens the application, reads the hook logs, and works out which value is wrong. Here, the sync fail hook sends AURA a short failure report and an incident ID over the agent-to-agent protocol, then exits. It does not say how to investigate or what to change.

A scheduled Lambda quietly stops completing and nothing pages you. AURA finds the function, reads its logs, and comes back with a three-second timeout. The usual path is opening the console, tracking down the right log group, and reading CloudWatch by hand. Here AURA connects to AWS through the MCP proxy AWS publishes, run locally with uvx against an AWS CLI that is already configured, so there are no new credentials to issue.

Some agent work should pause for a person. This is the other case: a health check every two minutes, one bounded action, and a result nobody approved. Each scheduled run starts the normal AURA image in one-shot mode: check one workload, act if something is wrong, write the result to the job log, and exit. Overlapping runs are forbidden.

A coworker says checkout is broken and nothing else. That is the whole prompt. AURA reads the live logs and comes back with the payment service. Normally a message like this is the start of guessing at a service and opening dashboards until something looks wrong. Here it is the entire input: no service named, no error string, no time range.

A root cause is not a fix. AURA keeps working the problem, taking what you find on the host and coming back with the user ID mismatch behind the failure. What follows a root cause is normally manual: check the mount, compare ownership on the host against the user inside the container, and get it wrong at least once before it lands.

One integration token, one config file, no code. AURA routes the question to a runbook-reader worker, queries Notion, and answers from what your team wrote.

The most common question since we started 31 Days of AURA: how do you plan to make money? The short answer is the control plane, not the agent. Mezmo sells an enterprise-grade control plane for running large numbers of agents across large environments, where coordinating across environments, governance, access control, and the efficiency of preprocessing MCP data start to matter. If a hundred people run AURA and three or four of them need that, the model works. The more people running AI agents in production, the bigger the market for the tooling underneath them.

Wondering what the catch is on a free, vendor-backed agent? There is not one in the license. AURA stays Apache 2, fully capable, and free to run. If you are weighing an open source tool with a company behind it, the first question is what the catch is. You have seen the project that turns out to be open core, or that is quietly hindered in one key way. This is Mezmo's answer for AURA.

AURA does not have to live on your laptop. Install it into the cluster with Helm and it is still there the next time something breaks. AURA is a fully open source AI agent built specifically for SRE work. Rather than one general assistant, you configure workers: separate agent roles, each scoped to a job like inspecting the cluster.

This video introduces Sumo Logic's SOC Analyst Agent, an AI-powered tool within the Dojo AI platform designed to transform security operations centers.

Learn about the Sumo Logic MCP Server -- allowing the use of Sumo Logic data and functionality in conjunction with your AI-driven tools (such as Claude Code or Github Copilot) to support agentic AI workflows for your organization.

Reaching a tool and being allowed to use it are two separate lists in the config. Put the mutable Kubernetes tools on the second one and each call pauses.