Show Notes
Parker explores a future where code can learn to heal itself. He lays out a concrete flow that ties observability, AI agents, and automated patching together to shorten debug cycles from hours to seconds.
Core idea: self-healing, agent-driven codebases
- Turn runtime and production observability signals into automatic repairs.
- Use a chain: telemetry → anomaly detection → agent API trigger → self-healing patch generation → test pass → environment-appropriate deployment.
- OpenTelemetry-enabled telemetry (via Dino) is central; Prometheus watches for abnormal spikes and triggers the AI workflow.
Tech stack you might use
- Observability: OpenTelemetry, Prometheus; logs and traces as the data backbone.
- Runtime telemetry: Dino (Deno) with native OpenTelemetry support.
- Visualization: Grafana (optional dashboards to monitor health and patches).
- AI agents: Google ADK (Agent Development Kit) to run self-healing agents and sub-agents.
- Frontend: client app using V/ TanStack (for API calls and hooks).
- Backend: Deno-based routes, health checks, webhook endpoints, and telemetry utilities.
End-to-end flow: from bug to patch
- A bug is triggered in prod or during local development.
- Dino emits telemetry spans covering the incident.
- Prometheus detects an error spike or anomaly and fires a webhook to the agent API.
- Google ADK-powered agents read the error trace and source context.
- The agent generates a patch and runs the test suite.
- If tests pass, the system decides the next step:
- For dev: patch live for faster iteration.
- For prod: create a PR that passes CI before going live.
- Optional: Grafana dashboard to visualize telemetry, patches, and health trends.
Architecture sketch
- Frontend: client app (Vite/V and TanStack hooks) communicating with backend APIs.
- Backend: Dino-based server with routes, health check endpoint, webhook receiver, and telemetry utility.
- Agents: self-healing agent (with possible sub-agents) orchestrated via the Google ADK.
- Data layer: OpenTelemetry spans → Prometheus metrics → potential Grafana dashboards.
Practical takeaways
- Start with strong telemetry: ensure OpenTelemetry is ingrained in runtime to feed the agent loop.
- Build a safe patching loop: automate patch generation and running tests, but keep strict CI/PR gates for prod changes.
- Consider dashboards early: Grafana visibility helps you confirm patches aren’t masking bigger issues.
- Use TDD as a bridge: pair self-healing workflows with test-driven development to improve patch quality.
- Start small: prototype with a single failure type and expand to others, layering sub-agents as needed.
Notes and caveats
- Patching live in production carries risk; implement guards, rollbacks, and traceability.
- Observability maturity is a prerequisite; under-specified signals will stall the automation.
- Orchestration complexity can grow; plan for clear ownership and escalation paths.
Links
- OpenTelemetry - Observability framework for telemetry data
- Prometheus - Monitoring and alerting toolkit
- Grafana - Observability and data visualization platform
- Deno - Modern JavaScript/TypeScript runtime with native OpenTelemetry support
- Google Agent Development Kit (ADK) - Framework for building AI agents
- VI AI Community - Community for AI builders