How long does the SRE Program take to show results?

Blueprint (4 to 6 weeks) produces the SLO/SLI design per service, the error budget policy, and the alert noise audit. Most teams see measurable alert noise reduction within 8 to 12 weeks of starting Forge. On-call load reduction targets of 40 percent or higher are designed in Blueprint and verified at the end of Forge. MTTR improvement is tracked at p95 throughout, not averaged across incidents.

What deliverables does the client own at the end of the SRE Program?

An SLO/SLI framework per service with documented error budgets, error budget dashboards in Datadog, New Relic, or Grafana, a runbook library covering the top ten incident categories, an on-call load baseline and 90 day improvement trajectory, an AI agent observability dashboard tracking deploy origin and incident attribution, quarterly chaos experiment results with documented findings, and the full method transfer: ADRs, re-scored maturity radar, DORA baseline.

What happens if the SRE Program does not reduce on-call load?

The on-call load reduction target is 40 percent or higher by end of Forge. That target is designed from the Blueprint alert noise audit: specific alerts are identified for retirement or refinement, specific runbooks are built for the recurring incident categories that were previously handled ad-hoc. If the target is not met at the end of Forge, the delta is documented with root cause and the Sustain retainer addresses it.

How is success measured in the SRE Program?

Three primary signals. MTTR tracked at p95 (not averaged), comparing pre-engagement baseline against post-Forge state. On-call load measured in alerts received per engineer per week, with a target of 40 percent or higher reduction. Error budget consumption rate per service, which reveals whether the SLO design is calibrated correctly to actual reliability levels.

Do we need existing SLOs before starting the SRE Program?

No. Most engagements start from informally defined reliability expectations or from aspirational SLOs that lack instrumentation. Blueprint designs the SLO/SLI framework from scratch: which services need SLOs, what the appropriate reliability targets are given the service tier, how to instrument the SLIs in the existing observability stack, and what error budget policy enforces the SLOs.

Does the SRE Program include AI agent incident tracking?

Yes. AI agent observability is built into Forge. The instrumentation distinguishes agent-originated from human-originated changes in the deployment pipeline and correlates that provenance data with the incident record. The AI agent observability dashboard tracks the percentage of incidents traceable to agent changes, the review rate differential between agent and human pull requests, and the deploy origin breakdown.

What does the Sustain retainer include?

Monthly reliability reviews against the SLO baseline, quarterly chaos experiments with documented findings, MTTR tracking at p95, AI incident pattern analysis to detect shifts in agent-originated failure modes, and continuous on-call load reduction. The retainer is optional. It is commonly requested because it preserves the gains from Forge rather than letting them degrade over time as the team and systems evolve.

Is the SRE Program compatible with our observability stack?

The SRE Program has been delivered with Datadog, New Relic, Grafana and the LGTM stack, PagerDuty, OpsGenie, and AWS CloudWatch. Blueprint assesses the existing observability toolchain and designs the SLI instrumentation and dashboards for the tools already in place. New tooling is recommended only when existing tools cannot support the required instrumentation.

Services/SRE Program

SRE Program

6 to 12 months plus optional retainer · Blueprint → Forge → Sustain

When on call burn becomes a retention risk and incident frequency outpaces your team's ability to respond, the problem is structural. AI agent generated changes only sharpen the curve. We design the SLO framework, instrument your systems including agent traffic, cut alert noise, and leave you with a Chaos Engineering program to prove the system holds.

Discuss the SRE program See related engagements

≥ 40%

On call load reduction target by end of Forge

Source. Google SRE Workbook, on call practices

≥ 50%

Alert noise reduction target after audit

Source. Honeycomb engineering benchmark

p95

MTTR tracked at percentile, not mean, for honest baseline

Source. Foundations Framework, Signal Integrity pillar

Targets baselined in Horizon, designed against in Blueprint, verified in Sustain. Industry research references, not Clouditive guarantees.

AI agent incidents tracked separately

Agent-originated incidents have different failure modes than human-originated ones. Your SLO framework needs to account for both.

When an AI agent generates a change that causes an incident, the failure pattern differs. The blast radius is often wider. The rollback is less predictable. The review trail is shorter. This engagement instruments agent deploy origin in your observability stack from Blueprint, so the Forge phase delivers SLO thresholds calibrated to human and agent traffic separately, not averaged together.

How the engagement runs

Three phases with defined exit criteria. The Sustain retainer is optional but commonly requested after Forge.

Blueprint

SLO/SLI design per service. Error budget policy. Alert noise audit. On call rotation review. AI agent failure mode catalogue.

Forge

Instrumentation with OpenTelemetry. Error budget dashboards in Datadog or Grafana. Runbook library. On call optimization. AI agent deploy origin tracking.

Sustain + retainer

Monthly reliability review. Quarterly chaos experiment. MTTR tracking. AI incident pattern analysis. Continuous on call load reduction.

Exit artifacts

SLO/SLI framework per service with documented error budgets
Error budget dashboards in Datadog, New Relic, or Grafana
Alert noise reduction target 50 percent or higher per Honeycomb benchmark
Runbook library covering top ten incident categories
On call load baseline and 90 day improvement trajectory
AI agent observability dashboard. Deploy origin and incident attribution
Quarterly chaos experiment with documented findings

Observability stack

DatadogNew RelicGrafana / LGTMOpenTelemetryPagerDutyOpsGenieAWS CloudWatch

We work with your existing tooling where possible. If instrumentation is missing, we add OpenTelemetry with minimal overhead.

Ready when you are

Incidents are a symptom. Let's fix the root cause.

Book a 30-minute call to walk through your current incident patterns and see if this engagement fits.

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