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AI assistants can generate devhelm.yml files, suggest assertions, and help you maintain monitoring configs as your infrastructure evolves.

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Generating configs from scratch

Describe your infrastructure and let the AI create a monitoring config:
“I have a REST API at api.example.com with /health, /v1/users, and /v1/orders endpoints. Create a DevHelm config that monitors all three with appropriate assertions.”
An AI assistant with access to DevHelm’s MCP tools or YAML schema knowledge can produce: 
version: 1
monitors:
  - name: API Health
    type: HTTP
    config:
      url: https://api.example.com/health
      method: GET
    frequency: 30
    assertions:
      - type: StatusCodeAssertion
        config:
          expected: "200"
          operator: equals
      - type: ResponseTimeAssertion
        config:
          maxResponseTime: 2000

  - name: Users API
    type: HTTP
    config:
      url: https://api.example.com/v1/users
      method: GET
    frequency: 60
    assertions:
      - type: StatusCodeAssertion
        config:
          expected: "200"
          operator: equals

  - name: Orders API
    type: HTTP
    config:
      url: https://api.example.com/v1/orders
      method: GET
    frequency: 60
    assertions:
      - type: StatusCodeAssertion
        config:
          expected: "200"
          operator: equals

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Reviewing and improving existing configs

Share your current devhelm.yml and ask for improvements:
“Review this config. Are there any gaps in monitoring coverage or assertions that could reduce false positives?”
The AI can identify:
  • Missing assertions — endpoints without response time checks
  • Inconsistent frequencies — critical and non-critical services at the same interval
  • Missing alerting — monitors without notification policies
  • Coverage gaps — services mentioned in code but not monitored

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Maintaining configs as code evolves

When you add new API endpoints, the AI can update your monitoring config:
“I just added a /v1/payments endpoint. Add a monitor with the same pattern as the existing ones, but check every 30 seconds since it’s payment-critical.”
This keeps monitoring in sync with your codebase without manual editing.

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Best practices for AI-assisted config

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Always validate

Run devhelm validate on any AI-generated config before deploying: 
devhelm validate -f devhelm.yml
AI models can produce syntactically valid YAML that uses wrong field names or invalid enum values. Validation catches these.

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Use plan before deploy

Preview changes before applying them: 
devhelm plan -f devhelm.yml
Review the plan output to confirm the AI made the changes you expected.

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Keep humans in the loop

AI-generated configs are a starting point. Review:
  • Are the assertions appropriate for each endpoint?
  • Are the frequencies correct for the service criticality?
  • Are secrets referenced correctly (not hardcoded)?
  • Are the right alert channels attached?

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Version control everything

Commit AI-generated configs to Git like any other code change. This gives you review through PRs and rollback through Git history.

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Workflow: AI agent + MaC

The most powerful pattern combines AI agents with monitoring-as-code workflows:
  1. Agent generates config based on your description
  2. You review the generated YAML in a PR
  3. CI validates with devhelm validate
  4. CI previews with devhelm plan --detailed-exitcode
  5. You approve the PR
  6. CI deploys with devhelm deploy --yes
The AI handles the tedious config generation; your CI/CD pipeline handles safe deployment.

YAML file format

Complete YAML schema reference.

MCP Server

Connect your AI agent to DevHelm.