By Kandan Kathirvel, CEO, MantisGrid AI

2025 has been the year of “Who broke the Internet this time?”

AWS went down → half the world froze.

Cloudflare went down → the other half panicked.

ChatGPT, X, apps, stores, CI/CD pipelines, coffee machines… all blinking 503.

But here’s the real plot twist:

Nothing exploded. No hacker. No asteroid. No aliens.

Both outages were caused by tiny, boring, internal mistakes that spiraled into full-region chaos.

Let’s break down what actually happened, why existing tools didn’t stop it, and what an Autonomous Reliability AI can (finally) change.

Part 1 — What Actually Broke (and it’s not what you think)

AWS: A tiny DNS race condition nuked a region

Quote from AWS:

“The root cause was a latent race condition in the DynamoDB DNS management system… resulting in an incorrect empty DNS record.”

Translated to human language:

Two DNS robots got out of sync — one slapped an old map on top, and the other proudly deleted it. Boom: no map.

Suddenly:

• DynamoDB vanished
• EC2 couldn’t launch
• Lambda got confused
• IAM couldn’t sign in
• Kinesis fell over
• NLB freaked out
• 20+ downstream services faceplanted

All because one DNS entry went “poof.”

Cloudflare: A config file ate too many carbs and doubled in size

Cloudflare’s own words:

“The issue was triggered by a permissions change… causing a database to output multiple entries into a feature file that doubled in size.”

Translation:

A config file got fat → propagated across thousands of machines → every proxy choked → the Internet sneezed → half the apps went offline.

Not malware. Not DDoS. Not a disgruntled intern.

Just… a file that was too big.

2025 is wild.

Part 2 — So why did every fancy observability/security/workflow tool fail?

Let’s be honest:

AWS and Cloudflare operate with some of the most sophisticated dashboards, logs, and monitoring systems in the industry — yet even existing tooling can miss issues that arise from subtle automation behaviors or complex dependency interactions. It’s the same pain enterprises struggle with every day.

Why?

**Because today’s tools only tell you WHEN something breaks. Not THAT it’s ABOUT to break.**

Traditional tools:

• detect symptoms - after the blast
• drown teams in alerts - have no understanding of architecture and can’t predict multi-service cascades

They’re great at telling you the house is on fire.

They’re terrible at telling you the gas leak started 2 hours ago in the basement.

Part 3 — The real problem: Modern cloud is a Rube Goldberg machine

One silent failure → hits another subsystem → which breaks a dependency → which triggers throttling → which causes health checks to fail → which triggers auto-failover → which overloads another subsystem → which breaks the entire region.

As we said earlier:

The modern cloud is deeply interconnected — one fault can take out entire regions.

AWS didn’t “go down.”

Its dependency graph collapsed.

Cloudflare didn’t “get hacked.”

Its automation pipeline backfired.

No human could ever track these interactions in real time.

And dashboards don’t understand them.

Part 4 — Why existing tools failed enterprises

Observability tools:

React after failure → too late.

No context → no prediction.

Just dashboards + alerts.

Security tools:

Protect from attacks, not internal cascades.

Not useful for config drift or automation misfires.

Workflow tools:

Automate what humans say — don’t understand architecture.

They execute. They don’t think.

(This is why “automation” caused both outages.)

Infra monitoring:

Metric spikes ≠ root cause.

You can’t detect a DNS race condition by staring at CPU graphs.

LLM agents:

Great at summarizing logs.

Terrible at understanding distributed system failure dynamics.

Net result:

The tools we have were designed for post-failure reaction, not pre-failure prediction.

Part 5 — How MantisGrid AI could have prevented both outages

Let’s imagine MantisGrid AI was running inside AWS or Cloudflare — or even inside the enterprises that got hit!

1. AI that “listens” to millions of signals

Our architecture-level AI models would have spotted:

• unusual DNS planner/enactor drift
• conflicting execution timestamps
• rising retries
• mismatched plan generations
• empty record propagation

By comparing real activity against simulated normal behavior, the system could surface anomalies long before they turned into a region-wide failure.

2. AI that understands system architecture, not just logs

Our Reliability Graph understands:

• topology
• dependencies
• change propagation
• data plane vs control plane risk
• how one change cascades into another
• architecture weak points (single points of failure, missing redundancy paths)

This architectural awareness is something existing tools simply don’t provide.

3. Pre-change prediction at the Git level

MantisGrid AI scans:

• config changes
• code changes
• automation changes
• infra-as-code updates

All with full topology and architecture awareness — enriched by every ticket ever resolved and every playbook ever written.

It would have flagged:

Cloudflare:

“Feature file doubling in size — propagation blast radius too large — high risk of global failure.”

AWS:

“Robot 1 deployed version 10 and began deleting all older versions.

Robot 2, running late, deployed version 9 on top of it.

Robot 1 then deleted version 9 because it was “older” than version 10 — leaving no DNS entry at all."

MantisGrid AI could have raised early warnings about this conflicting robot behavior — before it cascaded into a full outage.

4. Preventive actions (with human approval)

Our AI & AI agents would have recommended:

• block propagation
• isolate faulty enactor
• prevent rollout
• isolate nodes
• auto-rollback
• throttling earlier
• redirecting traffic preemptively

Stopping the outage before users noticed.

Part 6 — The hidden revolution powering this

Last year, the world obsessed over LLMs.

Quietly, another revolution grew:

AI models built for system reasoning, topology awareness, pattern detection, causality, and infrastructure understanding.

This is the core of MantisGrid AI.

Not chatbots.

Not “copilot for alerts.”

But AI that thinks like a distributed systems engineer and prevents the failure chain.

This is the shift from:

• reactive → predictive
• monitoring → understanding
• automation → autonomous reliability

And this is why the next reliability layer will be built on AI, not dashboards.

Part 7 — Lessons from AWS & Cloudflare

Cloud and tooling complexity is exploding. There are millions of signals, changes, and dependencies — and no human can keep track of the what, how, when, or where.

You need AI that understands your topology, interprets business intent, and acts like an always-on SRE army in real time.

• One bug anywhere can become an outage everywhere.
• Automation is great — until automation makes the wrong move.
• Observability without prediction is theater.
• AI scale + cloud scale = a new reliability risk class.
• Enterprises must invest in preventive AI, not just monitoring.
• Reliability is no longer a “nice to have” — it’s a board-level mandate.
• The future cloud will not survive without autonomous reliability systems.

Part 8 - Moments of reflection

Cloudflare’s outage on November 18 didn’t just disrupt hundreds of apps — it wiped out about $1.6 billion in trading volume (Ref: Timesofindia)

(One config file got fat… and Wall Street got slimmer.)

AWS’s outage likely resulted in millions in losses — the full impact is still being assessed.

Until outages hit, nobody pays attention.

But when they do — everyone suddenly wants to know who touched what.

It wasn’t AWS or Cloudflare that broke.

It was the old model of reliability.

Now it’s time for AI to take the wheel — alongside human SREs.

We lived through this pain.

We’re solving this pain —so enterprises don’t have to live through it again.

Ref: https://aws.amazon.com/message/101925/ , https://blog.cloudflare.com/18-november-2025-outage/