Compare
Streamwake vs. NPAW

Two persistent agents for streaming ops.
Different architectural bets.

Streamwake vs. NPAW.
Different layers, not different scores.

NPAW ships a persistent AI agent that runs against the NPAW Suite. Streamwake ships the same loop, on whatever stack you already run — and proves every action.

NPAW persistent ai agent for streaming operations (nala sentinel 3). Streamwake doesn't replace NPAW — we add the detection → classification → remediation → postmortem loop on top of the signals it already produces.

Where each product sits

Two different layers, end to end.

Honest framing of what the other tool does well, and where Streamwake compounds on top. Both layers run together in production.

What NPAW does well
NPAW's product line — Spotlight QoE probes plus the broader NPAW Suite (their own telemetry, probes, monitoring, and CDN Balancer) — pairs a long-running AI agent, Nala Sentinel 3, with an operational strategy aimed squarely at streaming operations. The agent is announced as persistent across the workspace, capable of cross-layer root-cause analysis that joins encoder, CDN, and player-side signals into a single incident, and ships with a remediation playbook library that covers the failure modes that recur across paid live and OTT catalogs. The execution policy is explicit and consistent across NPAW's own framing: each action either moves through a human-approval gate or runs autonomously, with the same audit trail either way. The architecture is Suite-centric — the autonomous layer is designed to land on top of the NPAW-owned telemetry, and the agent can ingest third-party signals on top of that.
Where Streamwake compounds it
We're vendor-neutral by design. Streamwake joins whatever stack the customer already runs — Mux, Bitmovin, AWS MediaLive, Broadpeak, Wowza, any CDN, PagerDuty, player telemetry — without asking the customer to consolidate onto Streamwake-owned monitoring, and without asking them to consolidate onto the agent vendor's telemetry stack either. The differentiator on top of that is governed execution: every action the agent takes is explainable end-to-end — the reason it was selected, the evidence that supported it, the confidence level it ran with, the human approval step (or the documented reason it bypassed one), the exact change that was applied, whether the change succeeded, whether viewer QoE recovered, whether rollback was needed, and what the system learned.
The comparison

Six rows,
same loop, different layer.

Read top to bottom — the left column is what a traditional monitoring / analytics platform does today; the right column is what happens when Streamwake is sitting on top of it.

Step 01

Monitor

→ Monitor

Both watch the same signals — QoE, encoder health, CDN egress, DRM handshake latencies.

Same signals, same event bus. Nothing changes here on day one.

Step 02

Alert

→ Detect

Threshold trips fire alerts and a human triages them. Anomalies arrive as a flat list.

Every anomaly is prioritized by class — encoding regression, manifest drift, edge brown-out, DRM handshake — so the noise is structured before it ever reaches a human.

Step 03

Dashboard

→ Correlate

Charts group events by source for an operator to read between the lines.

The agent joins the signal across encode, edge, and DRM into a single incident — so the chart and the cause tell the same story.

Step 04

Engineer investigates

→ Agent classifies

A human investigates the chart, names the failure, and decides what to do.

The agent names the failure mode itself, checks whether it is safe to act, and returns a typed next-action rather than a partial chart.

Step 05

Engineer remediates

→ Agent can remediate

A human writes the playbook, runs it, and watches the recovery by eye.

The agent picks the smallest safe remediation — reroute egress, roll a flag, re-package the title, quarantine a node — and verifies recovery before closing out.

Step 06

Engineer writes postmortem

→ Automatic postmortem

A human hand-writes the post-incident writeup when the dust settles.

A structured postmortem — what happened, what was tried, what changed — lands in Slack, Linear, or PagerDuty the moment the incident closes.

The architectural split

Same signals.
Different obligation.

NPAW does monitoring and analytics well. Streamwake doesn't replace that — it adds the detection → classification → remediation → postmortem loop on top of the same signal bus.

The left column ends with a chart and an alert. The right column ends with the incident closed and a writeup in the team's inbox. That's the whole difference — and it doesn't ask you to throw away anything you already run.

Layer

Telemetry + analytics

On top

Detect · classify · fix · write it up

End state

Incident closed, not a chart

Frequently asked

Questions teams ask
before adding another layer.

Anything specific to your NPAW + Streamwake rollout — write to us.

See the loop run

Bring us your Nala Sentinel 3 stack —
we'll wire Streamwake on the same signals.

Streamwake sits on the same signal bus a persistent AI agent already reads — encoder health, CDN egress, player QoE, DRM handshakes — and runs the same detect → classify → remediate → verify → postmortem loop with every action on the wire: why, evidence, confidence, approval, change, success, QoE impact, rollback, lesson. No consolidation onto Streamwake-owned telemetry. No agent to install on your infrastructure. The architecture stays yours; the loop becomes provable.

See the four-stage loop on /how-it-works →

Run the loop on your existing stackWrite to us