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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
The first time an AI assistant suggests "restart the service" during a live incident and nobody on the bridge can tell whether that suggestion came from a current runbook, a stale wiki page, or thin air, you stop caring about model benchmarks. You start caring about what the agent actually knew, where that knowledge came from, and whether you can trust the chain of reasoning behind it.
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By Mezmo
An interview series with the people building Mezmo’s open-source agentic harness for production operations. Builder in the loop is a Mezmo interview series focused on the engineers, product leaders, and operators shaping AURA, our open-source, MCP-native agentic 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. What should agents be allowed to do?
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By Mezmo
In a recent webinar, The Journey to Production AI, Andre Elizondo walked through what separates a working agent demo from an agent worth trusting on a 2 a.m. page. Live polls during the session put numbers behind a pattern most platform teams already feel. Most teams are early. The ones who are further along did not get there by shipping a flashier demo. They got there by treating production AI as a platform problem.
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By Mezmo
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.
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By Mezmo
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.
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By Mezmo
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.
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By Mezmo
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.
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By Mezmo
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.
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By Mezmo
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.
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By Mezmo
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.
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By Mezmo
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.
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By Mezmo
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.
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By Mezmo
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.
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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.