The Agentic Reliability AI SRE Suite

Agents do the work.
You set the rules.

Regen triages, coordinates, and resolves incidents on autopilot — on-call management that learns from every incident.

No credit card required · Live in under 10 minutes

Active early adopters of our OpenSource Suite

Trusted by teams running critical infrastructure

Acme IncNorthwindVertex LabsSolaceMeridianAcme IncNorthwindVertex LabsSolaceMeridian

One flow, four connected agents

From the first alert to the finished post-mortem — click a step to see who's driving it.

Regen ingests the alert, routes it to the right responder, and starts a structured incident record — the moment before anyone's phone rings.

01Regen

On-call coordination, reimagined

The problem

On-call today means noisy pages and zero context by the time someone actually picks up the incident.

How Regen solves it

Regen handles on-call scheduling, alert routing, and incident coordination — similar in function to PagerDuty or Opsgenie, but designed to feed structured incident context into the rest of the FluidifyAI chain. When an incident fires, Regen creates an organized, contextualized record of what was happening at the time.

See how Regen works

SEV1 · INC-7451

Checkout Service Cascade

1

Alert fires

Datadog detects p99 latency >2,400ms

2

AI triages

Root cause: missing DB index blocking connection pool

3

Team coordinated

Circuit breaker enabled, pool scaled 200→800

4

Resolved in 12 min

Post-mortem auto-drafted, index queued

02Neuri

Root cause, automatically

The problem

Root cause hunting means tabbing between five dashboards while the incident keeps burning.

How Neuri solves it

Neuri takes the structured incident record from Regen and runs root cause analysis automatically — correlating logs, metrics, traces, and deployment events simultaneously, generating hypotheses, testing them against evidence, and converging on a root cause with a confidence score and a full explanation of its reasoning.

See how Neuri works
neuri-demo.fluidify.ai / investigations / rca-001live
SEV1rca-001linked · INC-4821

payments-api OOMKill cascade — 2026-07-04

payments-api · payments-worker

91%

confidence

Review history

APPROVED by @priya · 14:31

Confirmed — matches deploy diff and metric onset

Evidence

14:01:54View deploy diff

Reasoning

1

Deploy a3f9c2 lowered the payments-api memory limit from 512Mi to 256Mi

2

Working-set memory under normal load exceeds the new 256Mi ceiling within ~40 seconds of traffic

3

Kubernetes OOMKills the container repeatedly, producing a restart cascade across all 3 replicas

suggested_next_step

Revert the memory limit in the deploy manifest, or right-size it against p99 working-set usage before re-rolling.

03Reflex

Diagnosis into action

The problem

A confident diagnosis that just sits in a dashboard is still a slower incident.

How Reflex solves it

Reflex receives Neuri's confidence-scored diagnosis and acts on it. When confidence is high enough, Reflex executes the remediation autonomously. When it isn't, it hands the pre-diagnosed incident to a human so they walk in knowing the answer, not starting from scratch.

See how Reflex works

Automation flow

Neuri diagnosis received

confidence-scored root cause

IF confidence ≥ 85%

Restart pod: orders-v2-7f9

Remediation executed autonomously · verified recovered
04Gills

Ask, in plain English

The problem

Getting an answer usually means learning yet another dashboard or query language.

How Gills solves it

Gills is the natural language layer that runs across all three modules. Any engineer can ask Gills — in plain English — what happened, what Neuri found, and what Reflex did. No dashboards, no query languages, no platform expertise required.

See how Gills works

Ask, in plain English

What caused the latency spike?
Slow DB query in deploy #a3f91c · p99 120ms → 2.4s, traced to a missing index.
Ask Gills anything
Evaluation benchmarks

Measured against real incident data

Regen is scored against replayed production incidents, not synthetic prompts. Pick a benchmark to see how it stacks up.

Root cause accuracy

Top-1 root cause suggestion matches the confirmed post-mortem root cause.

FluidifyAI94%
Generic LLM copilot61%
Manual on-call71%
Legacy runbook automation38%

412 production incidents · 6 companies · Sev1-Sev3

fluidify.ai / evals / resolution
eval_report_resolution.html PASS · 94.2%
datasetincident-bench-v2 (n=412)
top-1 accuracy94.2%
top-3 accuracy98.7%
avg. time to hypothesis18s
confidence calibration (ECE)0.031

Plugs into your existing stack

Alerting, observability, and on-call tools you already use - connected out of the box.

Built to run without babysitting

Security defaults that most vendors upsell - included from day one.

SAML/SSO free forever

Enterprise-grade single sign-on isn't gated behind an enterprise tier - it's included for every team.

Non-root container

Runs as UID 1001 with all Linux capabilities dropped - no root access inside the container, ever.

Read-only filesystem

The production root filesystem is mounted read-only, shrinking the blast radius of any compromise.

Data never leaves your infra

Self-hosted deployments keep incident data, logs, and traces inside your own infrastructure - always.

Full methodology and chaos test scripts are in the repo. RELIABILITY.md · SECURITY.md

Stay ahead of the next incident

Product updates and reliability engineering notes - straight to your inbox.

Frequently asked questions

Everything teams usually ask before switching.

Yes. Regen is open-source (AGPLv3) and free to self-host forever - no seat limits, no feature paywalls on the core product. A managed cloud tier is available if you'd rather not run the infrastructure yourself.