The Self-HealingCodebase Agent
DeepOps gives your team an autonomous command layer that monitors live failures, diagnoses root cause, drafts the remediation path, and only breaks the glass when a human decision is actually needed.
Eight stages. One closed-loop incident system.
The landing page has to tell the truth about the system. These are the actual roles each sponsor tool plays inside the DeepOps remediation loop.
Normalizes runtime failures and app signals into a canonical incident input.
Persists the live incident record so every agent and operator sees the same truth.
Builds root-cause context from traces, symptoms, code signals, and runtime evidence.
Produces constrained fix plans, diff previews, test intent, and execution artifacts.
Handles approval, rejection, and human suggestion loops before risky changes go live.
Calls the human when blast radius, user cost, or revenue risk crosses the threshold.
Rolls out the selected fix and reports deployment truth back into the incident record.
Captures traces and optimization signals so the repair loop gets better over time.
Three flows that prove the system is real.
The demo is not one synthetic happy path. It is three escalating branches: autonomous remediation, human approval, and phone-based escalation with executable guidance.
The agent detects the regression, diagnoses root cause, drafts a fix, deploys it, and closes the loop without stopping the operator.
No human gate when the incident stays within the safe policy envelope.
The dashboard still shows live diagnosis, diff preview, and deployment progress.
Best demo path for showing the full machine-speed remediation loop.
The system reaches gating and waits for approve, reject, or suggest so the operator can steer the outcome before deploy.
Approve or reject the proposed plan, fix, and merge path.
Suggest constraints or alternate steps and let the agent re-plan around them.
This is the human-in-the-loop path reflected in the dashboard controls.
When the issue has major user or financial impact, Bland AI calls the human and turns voice guidance into an actionable hotfix plan.
If the human is away from the computer, they can still direct the fix over the call.
If they can operate live, the agent follows the guidance and keeps the backend synchronized.
This is the highest-signal hackathon moment because it proves escalation, approval, and execution together.
Every agent and operator works from the same object.
DeepOps does not pass opaque handoffs between tools. It maintains one canonical incident record with lifecycle state, diagnosis, fix, approval, deployment, and timeline context.
Live incident stream over SSE with polling fallback.
Canonical incident detail, severity, and state transitions.
Diff preview, plan status, and approval controls in one operator surface.
Deployment and webhook feedback reflected back into the same record.
Break the app. Let the system answer.
The landing page should set up exactly what the judges will see: live incidents, human approval when risk climbs, and phone escalation when the operator has to be pulled back into the loop.