AI DevOps for .NET teams should not mean asking a coding assistant to guess at production.
The useful version connects the application model, repository, deployment pipeline, telemetry, cloud resources, and current runtime state. It lets the agent collect evidence and prepare a change while a person still controls production.
.NET teams already have strong building blocks. Aspire can describe a distributed application and target Docker Compose, Kubernetes, Azure Container Apps, Azure Kubernetes Service, and other environments. OpenTelemetry can carry logs, metrics, and traces across services. The missing piece is often the operating loop between those tools.
That is where Clanker DevOps fits.
Begin with the .NET application model
A typical production .NET system is not one process. It may include:
- ASP.NET Core APIs.
- Background workers and scheduled jobs.
- SQL, PostgreSQL, Redis, queues, or object storage.
- Frontends and gateways.
- Identity and secrets.
- Containers or Kubernetes workloads.
- CI/CD pipelines and infrastructure as code.
Aspire gives developers a code-centric way to describe those relationships. Current Microsoft documentation describes a deployment pipeline in which target resources contribute publish and deploy steps. Its Kubernetes integration can generate Helm artifacts from the application model or deploy through the current cluster context.
That model helps an AI agent understand what should exist. It does not by itself explain what is happening in production right now.
For operational work, connect three views:
intended application model
+ repository and deployment history
+ live infrastructure and telemetry
= evidence for the next decision
Give the agent questions, not unrestricted production access
The first AI DevOps workflows should be read-only investigations.
Useful prompts include:
Compare the Aspire application model with the deployed Kubernetes workloads. List missing services, image mismatches, unhealthy dependencies, and the evidence for each finding.
Explain the increase in API latency using OpenTelemetry traces, recent GitHub changes, pod restarts, and cloud database metrics. Do not change production.
Check whether this deployment changed environment variables, resource limits, ingress, or database connectivity. Prepare a rollback plan with the exact affected resources.
Those prompts force the investigation to cite reality. They are much safer than "fix production" or "make the cluster healthy."
Clanker DevOps can bring cloud, Kubernetes, GitHub, cost, security, and deployment context into the same workspace. Clanker CLI and MCP let configured agents query that context through the user's local provider credential path.
Use OpenTelemetry as the shared language
.NET supports OpenTelemetry through familiar logging, metrics, and tracing APIs. Microsoft documents ILogger, Meter, and ActivitySource as core instrumentation paths, with exporters available for open-source and commercial observability systems.
For an AI-assisted incident, telemetry should answer four questions:
- What changed?
- Which request path or dependency is failing?
- Which users or workloads are affected?
- What evidence would prove recovery?
An agent can help correlate those signals, but it should not invent causality. Require links, timestamps, resource names, commit identifiers, and queries that another engineer can inspect.
An incident summary should look like this:
Symptom: checkout API p95 increased after 14:05 UTC
Evidence: trace span database.checkout rose from 80 ms to 920 ms
Change: deployment abc123 changed connection-pool settings at 13:58 UTC
Scope: 31% of requests in production-eu
Proposed action: restore the previous setting through the normal deployment path
Verification: p95 below 150 ms for 15 minutes; error rate below 1%
That is operationally useful. "The database may be slow" is not.
Keep deployment generation separate from deployment approval
Aspire, CI/CD, Helm, Terraform, Bicep, and other tooling can all produce or apply changes. An AI agent can help draft configuration and explain a plan, but production approval should remain a separate step.
Use this sequence:
- Read the current application and infrastructure state.
- Identify drift or conflicting changes.
- Draft the smallest patch.
- Build and test the affected .NET projects.
- Produce the infrastructure or deployment diff.
- Explain impact and rollback.
- Ask the responsible person to approve.
- Apply through the normal delivery path.
- Verify health using the same evidence that found the problem.
Review-before-apply is not anti-automation. It keeps fast automation from turning an uncertain assumption into a production incident.
Practical workflows for .NET teams
Deployment investigation
Compare the AppHost model, container images, Helm output, current workloads, health probes, configuration, and recent commits. Return mismatches before proposing a fix.
Container and Kubernetes debugging
Gather pod status, events, resource limits, deployment history, service endpoints, ingress, and dependency health. Connect the result to .NET logs and traces.
Cloud cost investigation
Map the cost delta to Azure, AWS, GCP, or Kubernetes resources, then connect those resources to services and recent scaling or deployment changes. A useful result names the owner and next action, not only the expensive SKU.
Security review
Inspect public routes, identities, secrets paths, dependency changes, container configuration, and infrastructure drift. Separate findings from remediation and keep high-impact changes behind review.
Pull request operations review
Ask the agent to explain whether a code change also requires configuration, migration, capacity, monitoring, or rollback changes. This brings production thinking into the pull request before deployment.
A sensible adoption path
For a small .NET team, start with one repository and one environment. Connect read-only infrastructure context, choose an incident or deployment question the team already answers manually, and compare the agent's evidence with the normal investigation.
For a larger organization, define environment and resource boundaries, separate read and write capabilities, attach evidence to change records, and keep existing observability, CI/CD, IaC, and approval systems authoritative.
The goal is not to replace the .NET toolchain. It is to let people and agents understand the same production system without opening ten consoles and guessing how they connect.
Sources
Ask Clanker Cloud what your cluster is doing
Install the local app, connect your kubeconfig, and turn cluster state, workload health, cost context, and safe next steps into one readable answer.