StageGuard

Inspiration

Live broadcasts fail fast. A few seconds of packet loss, encoder instability, or routing degradation can immediately become dropped frames, broken streams, and lost viewers.

The problem is not a lack of observability data. Operators already have metrics, logs, dashboards, and alerts in tools like Grafana. The problem is that during an incident, a human still has to manually correlate all of that evidence, identify the real root cause, decide what action is safe, and then confirm that the system actually recovered.

We built StageGuard to turn observability into an operational response loop.

StageGuard doesn't generate the show. It keeps the show on air.

What it does

StageGuard is an AI-assisted incident commander for live media and broadcast operations.

Our demo simulates a three-camera live production where Camera 3 begins dropping frames. Instead of assuming the encoder is overloaded, StageGuard investigates the incident through Grafana using the official Grafana MCP integration.

It correlates multiple pieces of evidence:

• Camera 3 dropped-frame rate is elevated
• uplink-b packet loss is abnormal
• Camera 3 CPU and GPU utilization remain healthy
• peer cameras and the alternate uplink remain healthy

From this evidence, StageGuard identifies the real root cause:

uplink-b packet loss

StageGuard can then use Gemini to turn the verified evidence into a concise operator briefing.

But AI does not receive unrestricted infrastructure control.

The complete workflow is:

Observe → Investigate → Diagnose → Approve → Act → Verify

A remediation is bound to the exact incident evidence revision and requires explicit human approval. After execution, StageGuard does not trust a successful API response as proof that the incident is fixed.

It returns to Grafana and requires consecutive healthy telemetry samples before marking the incident as:

Recovery Verified

How we built it

StageGuard is built as a modular incident-response runtime around Grafana as the evidence plane.

Key components include:

• Grafana for operational visibility
• Official Grafana MCP for read-only telemetry access
• Prometheus for live broadcast metrics
• Gemini / Vertex AI for revision-bound operator briefings
• Python incident orchestration and deterministic diagnosis
• Docker Compose for the reproducible demo environment
• a deterministic live-production telemetry simulator
• an approval-gated remediation adapter
• post-action telemetry verification
• a dedicated operator incident cockpit
• authenticated incident checkpoints and tamper-evident audit state

The demo intentionally separates observation credentials from action credentials. Grafana/MCP can investigate the system, while infrastructure-changing actions go through a separate bounded remediation interface.

Challenges we faced

Distinguishing correlation from root cause

A frame-drop alert alone is not enough to diagnose an incident. We designed StageGuard to look for supporting evidence, contradictions, and healthy peers before committing to a diagnosis.

If evidence is incomplete or conflicting, StageGuard abstains instead of guessing.

Keeping AI useful without giving it unsafe authority

We wanted Gemini to improve incident response without allowing a model to freely execute infrastructure operations.

Our solution was to make Gemini an advisory briefing layer, while deterministic policy controls diagnosis, approval, remediation eligibility, and recovery.

Proving that a fix actually worked

A remediation endpoint returning 200 OK does not mean viewers are seeing a healthy stream.

StageGuard therefore performs a second observability loop after remediation and only closes the incident when Grafana telemetry proves recovery.

Making incident execution safe

We also had to handle stale approvals, retries, process restarts, and ambiguous action state. StageGuard binds approvals to evidence revisions, persists execution state, and prevents already-consumed remediations from being replayed after restart.

What we learned

The biggest lesson was that agentic systems become far more valuable when observability is part of their control loop rather than simply another dashboard.

Grafana gives StageGuard the evidence needed to reason about the production before an action, and the evidence needed to prove the action worked afterward.

We also learned that reliable operational agents should be designed around:

• bounded tools
• explicit evidence
• human control for consequential actions
• deterministic safety policies
• and independent verification of outcomes

What's next

StageGuard can expand beyond the current broadcast scenario into a broader incident-command platform for:

• live streaming infrastructure
• virtual production
• CDN and contribution networks
• cloud transcoding pipelines
• sports and event broadcasting
• multi-region production systems

Future versions could add more incident playbooks, traces and logs, additional media-control integrations, and fleet-wide incident coordination.

The long-term goal is simple:

turn observability from something operators watch into something that actively helps them keep production running.

Built With

• agents
• ai
• apis
• automation
• broadcast
• cloud
• dashboards
• devops
• docker
• gemini
• google-cloud
• grafana
• incident
• mcp
• monitoring
• observability
• prometheus
• python
• remediation
• security
• sre
• streaming
• telemetry
• vertexai