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By Mezmo
Authored by Sven Delmas, VP of Research at Mezmo I wanted to know what Mezmo's new trace features feel like with real telemetry behind them, so I built the smallest honest rig I could: the OpenTelemetry demo application running in a local Kubernetes-in-Docker cluster on my machine, one collector, and one deliberately simple Mezmo pipeline.
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By Mezmo
Builder in the loop is a Mezmo interview series with the engineers, product leaders, and operators shaping AURA. Each installment looks past the product layer to explore the decisions, tradeoffs, and lessons involved in building agents for real production work. This installment features Mike Shearer, the engineer who built AURA and, until recently, its only developer. AI agents are easy to believe in when the task is small.
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By Mezmo
Most of the AI features that shipped into observability tools this year summarize alerts. You get a paragraph that restates the dashboard you were already looking at, and the agent never reads the cluster itself, because giving it cluster access is a security conversation nobody wanted to start. This walkthrough starts it.
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By Mezmo
Software systems are getting better at understanding themselves. The mix of richer telemetry, smarter pipelines, and agentic AI is shifting observability from a passive record of events into something more active and useful. That shift is what we mean by context engineering. We recently partnered with O’Reilly on a report by David Beale that introduces the discipline. Before you read it, here are five things worth knowing.
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By Mezmo
AI has changed how software gets built. It hasn't changed how software gets run. Most of the AI money in software has gone into the IDE: code generation, copilots, developer assistants, faster pull requests. That work matters. But writing software is one slice of the lifecycle. The harder problem, and the more expensive one, is running that software in production. Production is where systems fail in ways nobody predicted. Incidents don't stay inside one service.
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By Mezmo
PagerDuty fires. You open the incident. Title, timestamp, nothing else. Whatever context exists is in someone's head, in a Slack thread from two weeks ago, or in a runbook nobody has touched since the last reorg. We got tired of that. So we put an AURA agent behind a Slack bot and pointed it at our own production environment.
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By Mezmo
By: Tucker Callaway, CEO at Mezmo The market keeps saying “SaaS is dead.” That’s probably true, but it’s also incomplete. What’s actually dying is the idea that value lives inside a vendor-controlled black box. The next era is about utilities: unlimited coding capacity and unlimited analytical capability. And if those two utilities are real, then the vendor model has to change.
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By Mezmo
Builder in the loop is a Mezmo interview series focused on the engineers, product leaders, and operators shaping AURA, an open-source, MCP-native agent harness for production operations. This installment features Tony Rogers, whose work on AURA is less about building new features and more about trying to break them before users can.
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By Mezmo
Builder in the Loop is a Mezmo interview series focused on the engineers, product leaders, and operators shaping AURA, an open-source, MCP-native agent harness for production operations. The goal is to get past the polished product layer and talk through the decisions that matter when AI starts interacting with real systems. Key questions include: What should agents be allowed to do? How do they get better over time? Where should humans stay in the loop?
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By Mezmo
Single agents are a useful starting point for SRE workflows. They are not where the architecture should end. The first version is simple enough: connect an LLM to a few tools, give it a system prompt, and point it at your infrastructure. It can summarize an alert, pull logs, answer questions, and draft a useful next step. Then the workflow gets real. You add GitHub for runbooks, Kubernetes for cluster state, PagerDuty for incident context, Prometheus for metrics, and Mezmo for telemetry.
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By Mezmo
Checkout starts failing because the cart service keeps dying. The real cause is a recent pull request that let memory grow with every add to cart.
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By Mezmo
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.
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By Mezmo
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.
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By Mezmo
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.
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By Mezmo
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.
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By Mezmo
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.
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By Mezmo
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.
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By Mezmo
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.
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By Mezmo
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.
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By Mezmo
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.
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By Mezmo
Logging in the age of DevOps has become harder and more critical than ever because it is key to maintaining visibility and security in today's fast-moving, highly dynamic environments. With these needs and challenges in mind, Mezmo has prepared this eBook to offer guidance on how best to approach the log management challenges that teams face today.
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By Mezmo
A growing number of log management solutions available on the market today are offered as cloud-only services. Although cloud logging has its benefits, many organizations have requirements that can only be fulfilled with self-hosted/on-premises log management systems.
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By Mezmo
Here's a complete guide covering all core components to help you choose the best log management system for your organization. From scalability, deployment, compliance, and cost, to on-prem or cloud logging, we identify the key questions to ask as you evaluate log management and analysis providers.
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By Mezmo
Despite having an extensive feature set and being open source, organizations are beginning to realize that a free ELK license is not free after all. Rather, it comes with many hidden costs due to hardware requirements and time constraints that easily add to the total cost of ownership (TCO). Here, we uncover the true cost of running the Elastic Stack on your own vs using a hosted log management service.
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Log Management Modernized. Instantly collect, centralize, and analyze logs in real-time from any platform, at any volume.
Why Mezmo?
- Powerful Logging at Scale: Get powerful log aggregation, auto-parsing, log monitoring, blazing fast search, custom alerts, graphs, visualization, and a real-time log analyzer in one suite of tools. We handle hundreds of thousands of log events per second, and 20+ terabytes per customer, per day and boast the fastest live tail in the industry. Whether you run 1 or 100,000 containers, we scale with you.
- Easy, Instant Setup: Mezmo's SaaS log management platform sets up in under two minutes. Instantly collect logs from AWS, Docker, Heroku, Elastic, and more with the flexibility to deploy anywhere - cloud, multi-cloud, or self-hosted. Logging in Kubernetes? Logs start flowing in just 2 kubectl commands. Whether you wish to send logs via Syslog, Code library, or agent, we have hundreds of custom integrations.
- Affordable: Mezmo’s simple, pay-per-GB pricing model eliminates contracts, paywalls, and fixed data buckets. Try our free plan, or only pay for the data you use with no overage charges or data limits. Our user-friendly, frustration-free interface allows your team to get started with no special training required, saving even more time and money.
- Secure & Compliant: Our military grade encryption ensures your logs are fully secure in transit and storage. We offer SOC2, PCI, and HIPAA-compliant logging. To comply with GDPR for our EU/Swiss customers, we are Privacy Shield certified. The privacy and security of your log data is always our top priority, and we are ready to sign Business Associate Agreements.
Blazing fast, centralized log management that's intuitive, affordable, and scalable.