How to evaluate an AI SRE platform: a practical checklist

TL;DR

• Before a demo, request sample investigation records with linked telemetry, approval logs, and deployment documentation you can inspect.
• Evaluate diagnosis and incident investigation against the evidence each tool used and the escalation decisions it recorded.
• Check safe execution, telemetry integration, and deployment and model control against your own requirements.
• Run the same incidents through every option, then score what you can verify. The scorecard leaves ratings to you rather than ranking products.

AI SRE platform vs. SRE agent

An SRE agent triages alerts, investigates incidents, and proposes or performs remediation under human oversight. An AI SRE platform may also supply telemetry access, model connections, and incident workflows. Check what each option includes rather than assuming every platform bundles the same components. Some companies sell the agent and surrounding stack together. Open-source projects can offer an agent that connects to tools you already run.

Before you score an option, identify which components it supplies and which you must provide. Evaluate the agent on its diagnoses, escalation decisions, and controls over production actions. Evaluate the surrounding stack on telemetry access, deployment options, and model choice. If you already own parts of that stack, test how well an agent works with them rather than treating the absence of a bundled replacement as a missing feature.

What evidence-backed diagnosis actually looks like

Require each option to link its diagnosis to the telemetry it used. In a demo, give the tool an incident with a known cause and ask it to show the log entries, metrics, and deployment events it retrieved, including their timestamps and query scope. Check the retrieved observations before accepting the explanation.

Ask how the tool ruled out competing explanations. If error rates rose after a deployment, the deployment may be relevant, but timing alone does not establish the cause. Have the vendor show which observations support that conclusion, which alternatives the tool checked, and where it reports missing or inconclusive data. You should be able to follow the investigation without accepting the model’s summary on trust.

Check whether investigation stays read-only until a person authorizes an action. Have the vendor show the tool permissions used during diagnosis and the point at which it can propose a change. That demonstration lets you assess diagnostic reasoning separately from the controls governing remediation.

Incident investigation and escalation: following the trail from alert to owner

A useful investigation test follows one alert through to a finding and a human handoff. Give each option a past incident with a known cause. Ask the tool to show which service it investigated, what evidence narrowed the scope, and what the receiving person would see. A handoff should identify the affected service, the supporting telemetry, and any uncertainty the investigator could not resolve.

Long-running incidents test whether the tool retains context. After the first investigation pass, provide a later alert or a new log entry and ask the tool to continue. Check whether it reuses earlier findings, revises them when the new evidence conflicts, or starts over. Ask where that investigation history lives and who can review it.

Escalation also needs a visible decision trail. Ask how the tool selects an owner when an alert spans several services, whether it checks an on-call schedule or service ownership record, and whether it logs the reason for its choice. Have the vendor demonstrate what happens when ownership information is missing.

Safe execution: human approval, read/write separation, and audit trails

Test whether approval controls block an incorrect proposed change. For each option, identify which tools can read production data, which can change it, and whether an investigator can invoke an operator’s tools. In a test environment, deny one proposed action and let another approval request expire. Require evidence that neither action ran, and ask what happens when the approval service cannot be reached.

Ask to inspect the policy that determines which actions require approval. In its documentation, Mezmo describes AURA’s gates for mutating tools and its separate investigator and operations roles. The AURA repository describes how configured approvals handle denials, timeouts, and transport failures.

For the audit trail, choose one completed action rather than accepting a generic description. Ask the vendor to show the initiating request, evidence the agent reviewed, proposed command, and approver’s decision. Then check whether the recorded tool call matches the approved command, including its target and arguments, and whether the trail records the outcome.

Telemetry and tool integration: working with what you already run

Require each option to demonstrate access to your existing telemetry before considering a migration. Give each vendor the same incident and ask it to retrieve the relevant logs, metrics, and traces from the tools you already run. Ask which connections use OpenTelemetry for telemetry, which use MCP to call tools, and which require a proprietary collector or schema changes. OpenTelemetry export alone does not establish that a product can ingest your existing data.

Request a connection-by-connection list rather than an integration count. For each tool on your shortlist, ask what the agent can read, whether it can take actions, and what setup or permissions each operation needs. Mezmo’s AURA documentation describes MCP-based tool connections and OpenTelemetry output for agent activity. Test each connection’s available operations rather than inferring them from those protocols.

Finally, ask the vendor to identify any data that must enter its own pipeline. Compare that requirement with your security rules and the telemetry access you can provide during a proof-of-value test.

Deployment and model control: who holds the keys

Deployment and model choice affect where incident data goes. Licensing determines whether you can inspect or modify the code, while access controls determine what you can inspect or change in a running deployment. For each option, check whether it can run on customer infrastructure, in an air-gapped environment, or as a hosted service. Then ask which model providers it supports, whether it requires a particular provider, and what data leaves your environment during an investigation. Request a deployment diagram and a sample configuration rather than relying on a list of supported options.

For an open-source example, Mezmo’s AURA documentation describes standalone operation and a choice of model providers, including a local option. AURA is an SRE agent and production execution harness, not an end-to-end observability platform. You can inspect its code and configuration in the Apache 2.0 AURA repository.

A hosted, closed product can still fit if you prefer the vendor to operate it. Ask that vendor how you would review agent behavior, change models, and export configurations if your requirements change.

Running a proof-of-value test with the same incidents

Choose past incidents with confirmed root causes and give each finalist the evidence available at the same point in each incident. Include the original alerts and a fixed window of telemetry, but withhold the postmortems and later findings until scoring. Otherwise, a tool may appear to diagnose an incident by reading its documented answer.

Keep the test conditions consistent. Give each finalist the same telemetry access, time window, and approval scope. If a tool needs a different connection or data format, record that setup work rather than quietly giving it more context. Use a safe replay environment, and require human approval before any proposed write action. Test each option against the same incidents instead of following a prepared demo script.

Score each run using the same scorecard criteria and a scoring scale you define before testing. Inspect the evidence behind the diagnosis, the investigation and escalation record, the handling of proposed actions, the work needed to connect telemetry, and the deployment and model controls used in the test. Record missing evidence as well as successful steps. Have your evaluators assign the scores after reviewing the outputs, and keep the incident inputs and scoring notes so they can repeat the comparison.

Vendor-reported benchmarks and customer outcomes can inform your questions, but they cannot replace results from incidents you controlled.

Buyer scorecard template

Where an open-source agent like AURA fits in this checklist

AURA is an SRE Agent for Production. Mezmo built this free, Apache 2.0 open-source agent as an autonomous execution harness and first responder for investigating and remediating production incidents. Mezmo’s AURA documentation describes connected tools, human approval for configured actions, standalone operation, and configurable model providers. AURA does not replace an end-to-end observability platform.

Evaluate AURA against the same agent-level questions as other options, including evidence links and control over production changes. You can inspect AURA’s code and license. AURA works independently of Mezmo Governance, a separate, agent-agnostic offering that is not open source. For named-product comparisons, see the AI SRE tools guide.

Conclusion

Use the completed scorecard to question each finalist about missing evidence or behavior you could not verify. Keep the incident inputs and scoring notes so you can revisit the decision when your production requirements change.

FAQs

• Is an SRE agent the same as an AI SRE platform? No. An SRE agent investigates incidents and may propose or carry out actions under human oversight. An AI SRE platform may provide a broader stack around that work, such as telemetry connections, model access, and incident workflows; check which components each option actually includes.
• Can an open-source agent work without buying a vendor’s full platform? Yes, if the agent supports standalone use and connects to tools and models you can provide. Mezmo’s AURA documentation describes standalone operation and tool and model connections, and you can inspect AURA’s source code.
• What is the minimum proof-of-value test when time is short? Give each finalist the same past incident with a known cause, the same telemetry access, and the same approval limits. Ask each option to show the evidence behind its diagnosis and what it would do before requesting or taking action.
• How should cost factor into the comparison? Compare deployment, model usage, and operating costs alongside your test results. For an open-source project, account for the resources needed to run and maintain it.