How much can Mezmo reduce MTTR?

Teams typically reduce Mean Time to Resolution (MTTR) by up to 80–90% with Mezmo’s agentic RCA feature, which prioritizes context, deduplicates logs, and automates multi-step analysis.

Does Mezmo integrate with incident management tools?

Yes, Mezmo supports out-of-the-box integrations with PagerDuty, Slack, and other platforms to fit seamlessly into your incident response workflows.

Is Mezmo’s RCA feature secure and scalable?

Yes, Mezmo is designed for enterprise-grade security and scalability, supporting petabytes of data and multi-region deployments.

Agentic SRE for root cause analysis improves MTTR from hours to minutes

Are you challenged by: ‍

‍Manual debugging workfows that slow down incident resolution and waste valuable engineering time.
‍Disconnected tools that require switching between multiple interfaces to analyze logs, metrics, and traces.
‍Complex observability queries that require specialized knowledge to extract meaningful insights.
‍Teams struggling to isolate root causes due to noise and context, especially during high-pressure incidents.

Mezmo's AI-powered root cause analysis (RCA) can help you accelerate incident resolution by up to 90%.

Schedule Demo Free Trial

Mezmo's AI-powered SRE agent for for root cause analysis is engineered to break the incident cycle. It leverages agentic AI workflows and context refinement to instantly surface root causes, automatically correlating across your entire stack, and turning resolution from hours into seconds. This happens directly within your existing developer ecosystems (IDEs, tools), meaning there is zero context switching.

How Mezmo's agentic SRE for RCA works

Powered by the MCP (Model Context Protocol) server, Mezmo's agentic SRE focused on root cause analysis implements a practical workflow that makes AI-driven incident resolution accessible to your team. While the MCP server handles the complex orchestration of agentic workflows behind the scenes, your team experiences a streamlined process.

Smart data processing

Deduplicate and cluster log data before sending to AI models, reducing cots and improving speed.

Contextual analysis

AI correlates telemetry data with system behavior to provide actionable insights.

IDE MCP integration

Access root cause insights directly from your development environment, eliminating context switching and accelerating incident response.

Key capabilities for agentic root cause analysis

Capabilities ingest and normalize messy, multi-source telemetry, improving data quality and structure so RCA can correlate signals and pinpoint the true root cause faster.

Agentic RCA workflows

Automated, multi-step analysis plans that travers your infrastructure, validate findings, and converge on a defensible root cause.

Noise-free, high fidelity signals

Only the most relevant data is analyzed, reducing token usage and improving MTTR up to 80%.

Smart data processing

Deduplication and clutering of log data before AI analysis, cutting costs and improving spped.

Structured recommendations

Receive clear RCA summaries with technical details and step-by-step remediation guidance.

Third-party integrations

Out-of-the-box support for PagerDuty, Slack, and more with expanded ecosystems coming soon.

Scalability and security

Designed to handle petabytes of data, with enteprise-grade security, multi-region deplooyment and robust failover mechanism.

Real results from real teams

90%

Cost reduction

From $1-$6 per incident to $0.06 due to prioritized context over excessive prompting.

-Mezmo benchmarking

90%

Faster MTTR via agentic diagnosis

On a daily basis, troubleshooting tasks that used to take our engineers 10-30 minutes of log spelunking are now resolved in under 5. It directly gives us back engineering days, allowing us to focus on innovation instead of firefighting.

— Senior Software Engineer

1st-try accuracy

Root cause analysis with less prompting

Clean context beats clever prompting. Mezmo's context-first pipeline reduced prompt bloat and stabilized outputs, improving quality while cutting per-incident costs.

— AI Engineer