Your prod
immune
system

Rethink observability for the AI era. Agents watch prod 24/7, and when something breaks they contain it, deploy a fix, and learn from it.

The manifest

Match prod velocity with agentic observability.

Software ships faster every year. Models now write code, open pull requests, and take actions inside systems that people depend on. Most of the tooling built to watch prod still assumes a slower world, one where a human reads every alert and makes every call.

Today we are building Edge Delta around a different assumption. Monitors patrol continuously, investigations begin before anyone is paged, and the root cause is waiting with its evidence by the time an engineer opens a laptop.

This asks for more than a model bolted onto a dashboard. It needs telemetry processed where it is produced, baselines the system learns from your own traffic, agents that carry proof for every conclusion they reach, and a rollback path written before any change ships.

Autonomy is earned. An agent starts at Propose: it reads your telemetry freely, drafts changes, and asks before any write. It works beside the stack you already run and takes on more authority in increments you approve and can revoke at any time. Every write crosses a gate a human defined. We would rather move at the speed a team can actually trust than at the speed a demo can survive.

We are engineers who have carried pagers and been woken by prod at three in the morning. We are building the system we wanted on those nights.

Ozan Unluchief executive officerpreviously Sumo Logic
Fatih Yildizchief technology officerpreviously Twitter

The system

How an anomaly becomes a fix.

Edge Delta keeps monitors on your telemetry around the clock. When one trips, agents correlate the signal, hold it inside the detection layer, and arrive with a remediation and the evidence behind it. Every write waits at a gate you define, and these four steps run in order on every incident.

01Detect

It notices before you're paged.

Monitors patrol r2 continuously. An OOM crashloop on checkout-svc trips a baseline the agents learned themselves: no threshold was hand-set, and nobody was paged to notice it.

time to detect
min → secauto
baselines learned from your own telemetry
02Intercept

Bounded at r2, before blast radius.

The signal is caught inside the detection layer, before it reaches anything that can change prod. Correlation happens here: many alerts collapse into one analyzed issue.

blast radius
bounded at r2held
nothing past this ring can write
03Gate

Autonomy is earned, not enabled.

Every write crosses an approval gate. Chat-initiated actions are approved action-by-action; scheduled work runs pre-approved playbooks. There is no path from a log line to a tool call.

writes without approval
0enforced
the same gate for humans and agents
04Remediate

The fix ships with its evidence.

The remediation arrives with root cause, timeline, and the change it wants to make. A human approves it, or a pre-approved playbook runs it. Rollback is automatic and already written.

mttr
4h → 30m↓ 87%
time to first correct action, median

Trusted in prod by teams shipping at AI speed

  • Boeing
  • Nvidia
  • Snowflake
  • American Airlines
  • Box
  • Taco Bell
  • Panasonic
  • Bugcrowd
from the edge delta platform

Every day, 810B events meet agents on watch around the clock.

9,400hours of AI investigation
9.4Mevents processed per second
31Kagents on duty
21.3Tevents processed this month
Where agents plug in
Kubernetes34%
GitHub21%
Custom MCP16%
Slack8%
AWS4%
Atlassian3%
Microsoft Teams2%
PagerDuty2%
CircleCI1%
Models powering investigations
Claude Opus 4.649%
GPT 5.416%
Gemini 3.5 Flash15%
GPT 5.6 Sol9%
Gemini 3.6 Flash5%
Claude Opus 4.83%
GPT 5.53%
05The landscape

AI SRE capability map

As of October 2, 2026

From our comparison pages, where every claim is dated and linked to its source. All comparisons

Autonomous
Autonomous with guardrails
Manual
Not documented
How much of each incident stage runs on its own, by product, as ofOctober 2, 2026.
productdetectinvestigatereasonactverifylearn
Edge Deltadetect: autonomousinvestigate: autonomousreason: autonomousact: autonomous with guardrailsverify: autonomouslearn: autonomous
Datadog Bits AIdetect: autonomousinvestigate: autonomousreason: autonomousact: autonomous with guardrailsverify: manuallearn: autonomous
Resolve AIdetect: autonomousinvestigate: autonomousreason: autonomousact: autonomous with guardrailsverify: not documentedlearn: autonomous
Traversaldetect: autonomousinvestigate: autonomousreason: autonomousact: manualverify: autonomouslearn: autonomous
06The path forward

Two weeks. One namespace. Turn it off anytime.


Approval before any write

Agents read, correlate and draft fixes. Every write waits for a name.

Your own baseline

Measure side-by-side against the stack you already run, nothing rerouted.

Clean removal

helm uninstall in one command. Your collectors and your data are untouched.

Nothing is migrated. Nothing is rerouted. Nothing is reconfigured.

07questions

Agents, answered.

What is an AI SRE?

An AI SRE is an AI agent that performs the work of a site reliability engineer: it detects prod issues, investigates them across logs, metrics, traces, and deployment context, identifies the root cause with supporting evidence, and remediates with approval and verification.

Is Edge Delta an observability tool or an AI SRE?

Edge Delta is a telemetry-native AI SRE. It is built on the telemetry pipeline architecture the company has run in prod for years, which is what lets its agents reason over live data instead of querying tools from the outside. Observability is the architecture underneath; the AI SRE is what runs on top of it.

What is an agent?

An agent is autonomous software that takes on a specific engineering job, not a chatbot waiting for prompts. Edge Delta ships four out of the box: an AI SRE that triages alerts and runs root cause analysis, an AI Security Engineer that reviews PRs for vulnerabilities and monitors for suspicious activity, an AI Software Engineer that reviews code in depth and opens targeted fixes, and an AI Work Tracker that keeps tickets in sync with what's actually happening. You can also build custom agents scoped to your own workflows.

Which agent should we start with?

Whichever job hurts most. SRE teams typically start with the AI SRE, which begins investigating the moment an alert fires so the root cause, evidence, and timeline are ready before an engineer even looks. Security teams start with the AI Security Engineer for around-the-clock PR review and environment monitoring. Engineering teams adopt the AI Software Engineer for deep PR review and proposed fixes directly in their repos. The AI Work Tracker fits every team. It opens or updates tickets when work is missing or unowned and links them back to the source. They share one platform, so adding more later takes minutes.

Can we create our own agents?

Yes. Beyond the four out-of-the-box agents, you can create custom agents tailored to your team's workflows, tools, and processes, scoped to the job you define, with the same connectors, guardrails, and auditability as the built-in ones.

How do the agents actually work?

Each agent works autonomously within its job. When an incident fires, the AI SRE groups related alerts, correlates real-time telemetry from across your environment, identifies the most likely root cause, and suggests a resolution. The AI Security Engineer and AI Software Engineer review every PR as it opens. The AI Work Tracker watches alerts, security findings, and incidents, and files the tickets nobody else did. Every conclusion comes with supporting evidence you can verify.

Will the agents replace our engineers?

No. The agents take on the toil that consumes engineering time: alert triage, data correlation, initial investigation, first-pass code and security review, and ticket hygiene. Your engineers stay in control and are pulled in when a human decision is required, with the groundwork already done. Engineers shift from first responders to decision-makers.

How are these agents different from AIOps tools or copilots?

AIOps tools surface anomalies, and copilots answer questions when prompted. These agents work autonomously: they start investigations on their own, gather evidence, review code, and file tickets without waiting for a human to ask.

Do they work with our existing stack?

Yes. The agents plug into the services, data sources, and tools you already run through out-of-the-box connectors, with new connectors added regularly. They start work already knowing your environment, with no onboarding doc and no six-week ramp.

How long does setup take?

Minutes. Connect your stack, activate the agents you want, and see the first results inside an hour. No credit card is required to start, and SSO and SAML are included.

How is our data kept private and secure?

Your data is your data, and Edge Delta never uses it to train models. Agents start at the Propose trust level, where they read and draft changes but apply nothing without your approval, and every action is logged for auditability. Connector credentials are encrypted, and the platform is SOC 2 Type II attested.

How much do the agents cost, and is there a free trial?

Pricing is based on token usage. You can start for free, pick the agents you need, out-of-the-box or custom, and run them in prod within minutes.

Your prod immune system.

Two weeks in one namespace, beside your stack, with every write waiting for approval. Turn it off and nothing breaks.