AI AgentDevOps Agent
DevOps Agent that automates it & engineering workflows using your data and tools.
The challenge
When a release goes wrong the team hunts through dashboards to work out which change caused it. Alerts arrive faster than anyone can read them, the runbook lives with whoever was on call last time, and the same incident gets solved from first principles again.
The outcome
Azure Application Insights, Azure Monitor and Log Analytics feed a Microsoft Foundry agent that correlates the failure to the change behind it and finds the runbook in Azure AI Search. Known failures with a safe, pre-approved action recover through Azure Automation; novel ones page an engineer with the evidence.
At a glance
- Type
- ai agents
- Category
- technical
01 — Architecture
End-to-end architecture
A commit builds, scans and ships through progressive rollout with health gates. Telemetry from Application Insights, Monitor and Log Analytics flows back to the Foundry agent, which correlates a failure to the change that caused it and proposes the runbook — executing only pre-approved actions with a known blast radius.
- Team: Developer / SRE
- Source: GitHub, Docker
- Pipeline: Container Registry, Azure Kubernetes
- AI & agent: Microsoft Foundry Agent, Azure OpenAI models, Azure AI Search, Azure AI Content Safety
- Signals: Application Insights, Azure Monitor, Log Analytics
- Action & insight: On-call engineer, Automation runbooks, Power BI / Fabric
02 — Workflow
Process & decision workflow
How a change is built, released, observed and recovered — then branched. A known failure with a safe, pre-approved action is remediated and verified automatically, while a novel failure or a wide blast radius goes to the on-call engineer with the correlation and the options.
- Build: Commit builds, scans and produces a versioned image
- Test: Unit, integration and load gates run on the change
- Release: Progressive rollout with health gates per stage
- Observe: Metrics, traces, logs and error budget watched
- Triage: Failure correlated to the change and the runbook
- Remediate: Rollback or fix proposed with the evidence
- Path 1 · known failure, safe action — Run the approved runbook: Rollback or restart executed and verified
- Path 2 · novel or wide blast radius — On-call engineer decides: Correlation, evidence and options presented
03 — Components
Key Microsoft components
Delivery speed only counts with fast recovery — pipeline, telemetry, remediation and evidence all stay on the Microsoft stack.
GitHubSource control, actions and pull-request based change.
Azure Container RegistryVersioned images with provenance and rollback.
Azure Kubernetes ServiceProgressive rollout, health gates and fast rollback.
Azure Load TestingPerformance gates before a release is promoted.
Microsoft Foundry Agent ServiceTriage, correlation and remediation orchestration.
Azure OpenAI modelsFailure narratives, diff explanation and release notes.
Azure AI SearchRetrieval over runbooks, past incidents and change history.
Azure AI Content SafetyGuardrails on generated commands and privileged actions.
Azure Application InsightsTraces, dependencies, exceptions and user impact.
Azure MonitorMetrics, alerts, health gates and error budgets.
Azure Log AnalyticsLog queries and correlation across the estate.
Azure AutomationPre-approved remediation runbooks with audit trail.
Azure Key VaultPipeline credentials, signing keys and secrets.
Power BI / Microsoft FabricDelivery, reliability and change-failure dashboards.
04 — AI
What the agent consumes
The capabilities the agent applies to every release and incident, and the line it does not cross.
AI capabilities embedded in the agent
- Build and test orchestration
- Change correlation
- Log and trace analysis
- Anomaly detection
- Retrieval-augmented generation
- Failure summarisation
- Rollback recommendation
- Blast-radius estimation
- Release-note drafting
- Alert deduplication
- Delivery metric rollup
- Approval routing
AI responsibility boundaries
The agent executes only pre-approved runbooks with a known blast radius. Novel failures, production data changes and anything affecting customer traffic at scale need an on-call engineer. Every automated action logs the trigger, the evidence, the command run and the verification result.
05 — Personalization
Personalization & evolving process
The same methodology applies to every agent in the catalog. Tune the service estate, the delivery template, the runbooks and the value model — the page structure stays identical.
Service & environment profile
Define the services, environments, release cadence, service levels and error budgets in scope. The personas are the developer, the site reliability engineer, the on-call responder and the service owner.
Delivery template
One consistent flow for every delivery agent: build, test, release progressively, observe, triage, remediate, verify and feed the learning back into the runbook.
Runbooks & blast radius
Declare the approved runbooks, the blast radius of each action, the health gates and the escalation thresholds. An action outside the approved set is proposed, never executed.
Value model
Capture baseline metrics first, then map the expected benefits: deployment frequency, lead time, change failure rate, time to restore, alert noise and manual toil.
06 — Impact
Key outcomes & business impact
Starting targets for the value case — validate each one against the customer baseline during discovery.
- Time to restore−50%Failures are correlated to the change within minutes.
- Change failure rate−30%Health gates stop a bad release before full rollout.
- Alert noise−60%Deduplicated, correlated alerts instead of a wall of pages.
- Action evidence100%Every automated action logs its trigger and verification.
Illustrative improvement index
Manual baseline = 100. Illustrative targets, not a commitment — confirm against the customer baseline.
- Time to restore: manual baseline 100, AI-assisted target 50
- Alert noise: manual baseline 100, AI-assisted target 40
- Manual toil: manual baseline 100, AI-assisted target 45
Related & recommended
Derived automatically from our solution knowledge graph.
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Technologies
What powers this solution.
TechnologyAzure Functions
Serverless compute for event-driven workloads.
TechnologyMicrosoft Copilot Studio
Low-code platform to build custom copilots and agents.
TechnologyAzure AI Foundry
Platform to design, evaluate and operate AI apps and agents.
TechnologyMicrosoft Fabric
Unified analytics and data platform.
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