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AutoObserve
Design Partner

SOLUTIONS

Production looks different depending on where you sit.

Developers debug code. SREs protect reliability. Platform teams build the operating environment. Engineering leaders manage the consequences when production fails. AutoObserve gives each team the evidence, explanations and decisions they need from the same production intelligence platform.

Find Your Solution ↓See How AutoObserve Works →

SEE AUTOOBSERVE AS

CHECKOUT DEGRADATION

14:31:00

YOUR QUESTION

What changed in my service?

CURRENT EXPLANATION

Likely regression

checkout-api v2.14.7

Confidence
91%

See Developer workflow ↓

BUILT AROUND HOW YOU WORK

Start with your responsibility in production.

Debug production · Operate incidents · Improve the production system

  • Developers

    Debug

    Ship software. Understand failures without becoming an observability expert.

    YOU NEED TO KNOW

    • What changed?
    • Where did it fail?
    • Is my code responsible?
    • What evidence supports that?
    AutoObserve for Developers →On this page ↓

  • SRE & Operations

    Operate

    Operate incidents—not alert streams—before humans become the correlation engine.

    YOU NEED TO KNOW

    • Is this real?
    • How severe is it?
    • What is affected?
    • What probably caused it?
    • What should happen next?
    AutoObserve for SRE →On this page ↓

  • Platform Engineering

    Operate

    Build the incident intelligence layer once. Give it to every team.

    YOU NEED TO KNOW

    • What depends on what?
    • What changed?
    • Where is degradation propagating?
    • Where is telemetry incomplete?
    AutoObserve for Platform Engineering →On this page ↓

  • Engineering Leaders

    Improve

    Know where production needs engineering attention.

    YOU NEED TO KNOW

    • Where is production risk concentrated?
    • What keeps consuming engineering attention?
    • Why do the same incidents keep returning?
    • Where should the next reliability investment go?
    • Are we actually getting better?
    AutoObserve for Engineering Leaders →On this page ↓

THE SHARED PROBLEM

Different teams. Same fragmented production reality.

Teams don't merely lack telemetry. They spend valuable incident time constructing a shared understanding of what production is doing.

TODAY

Something breaks

Alert

  • Developer

    • Logs?
    • Traces?
    • Code?

  • SRE

    • Metrics?
    • Alerts?
    • Logs?

  • Platform

    • Kubernetes?
    • Deployments?
    • Topology?
  1. Dashboards
  2. Queries
  3. Slack
  4. Meeting
  5. Shared theory
  6. Mitigation

WITH AUTOOBSERVE

Production

AutoObserve

Production Intelligence

  • Evidence
  • Explanation
  • Decision

Shared production understanding

  • Developer
  • SRE
  • Platform

Engineering leadership consumes the operational outcome above this layer.

FOR DEVELOPERS

Debug production without reconstructing the incident by hand.

AutoObserve brings changes, metrics, logs, traces and runtime dependencies into an evidence-driven investigation so you can understand what changed, where the failure started and what most likely explains it.

Before

Production issue

  1. Where is the data?

  2. Which tool?

  3. Which query?

  4. Which service?

  5. Correlate manually

With AutoObserve

Production issue

  1. What changed?

  2. Where did it fail?

  3. What evidence supports that?

  4. What explanations fit?

  5. What contradicts them?

  6. Current explanation

Explore Investigation →Explore AutoObserve for Developers →

FOR SRE & OPERATIONS

Operate incidents—not alert streams.

SRE attention is scarce. AutoObserve correlates production signals into incidents so interruption decisions come with evidence—not after a manual dashboard tour.

Before

Alert fires

  1. Is it real?

  2. Open dashboard

  3. Assess severity

  4. Check impact

  5. Find owner

  6. Investigate

  7. Escalate?

With AutoObserve

Production signals

  1. Correlate into incident

  2. Assess impact

  3. Assemble evidence

  4. Decide interrupt / observe

  5. Investigate when needed

  6. Govern response + verify

Explore AIDDE →Explore AutoObserve for SRE →

FOR PLATFORM ENGINEERING

Build the incident intelligence layer once. Give it to every team.

Platform engineers need shared incident models, policies, topology and investigation workflows—not six tools and a whiteboard per team.

Before

Service problem

  1. Kubernetes

  2. Cloud

  3. Telemetry

  4. Deployment

  5. Dependencies

  6. Reconstruct system state

With AutoObserve

Service problem

  1. Runtime context

  2. Topology

  3. Changes

  4. Evidence

  5. Telemetry

  6. Affected system model

Question

What could be affected?

Direct vs indirect blast radius from Checkout

Blast origin

Checkout

Degraded · v2.14.7 · ORIGIN

Downstream

  • Inventory
  • PaymentDIRECT
  • OrdersDIRECT

Phase: BLAST. What could be affected? Direct vs indirect blast radius from Checkout Selected entity: Checkout. Upstream dependents: Web. Direct dependencies: Inventory, Payment, Orders. Incident state: Checkout: Degraded (ORIGIN); Payment: Impacted (DIRECT); Orders: Impacted (DIRECT); Stripe: Unknown (INDIRECT).

Explore Topology →
Change
checkout-api v2.14.7
Impact
3 downstream paths
Evidence coverage
94%
Telemetry gap
None detected
Explore Topology →Explore AutoObserve for Platform Engineering →

FOR ENGINEERING LEADERS

Know where production needs engineering attention.

AutoObserve turns incidents, dependencies, changes and recovery behaviour into production intelligence—so engineering leaders can see recurring failures, operational burden, systemic risk and where reliability investment will have the highest leverage.

Before

Something breaks

  1. Wait for update

  2. Join bridge

  3. Reconstruct from dashboards

  4. Ask for status

  5. Chase ownership

  6. Guess at recovery

With AutoObserve

Production intelligence

  1. Assess burden

  2. Surface recurrence

  3. Map systemic risk

  4. Prioritise investment

  5. Track improvement

  6. Verify outcomes

Explore AutoObserve for Engineering Leaders →

ONE PRODUCTION REALITY

Different responsibilities shouldn't mean different versions of the truth.

Each team gets the level of abstraction appropriate to its responsibility without creating separate incident narratives.

CHECKOUT INCIDENT

  • Shared system model
  • Shared evidence
  • Shared investigation

  • Developer

    Code-level explanation

  • SRE

    Operational decision

  • Platform

    System-level context

  • Engineering Leader

    Burden + recurrence + risk + trend

One incident. One shared investigation.

  1. checkout-api v2.14.7 deployed

  2. Checkout latency increases

  3. AIDDE detects meaningful degradation

    SRE

  4. AIDDE hands off to Production Investigation

    Developer

  5. Deployment hypothesis strengthened

    Developer

  6. Relevant change and evidence surfaced

    Developer

  7. Blast radius identified

    Platform

  8. Interruption decision

    SRE

  9. Shared incident status available

    Engineering Leader

  10. Mitigation begins

SOLVE A SPECIFIC PROBLEM

Looking for an operational outcome instead?

Solutions describe who you are and how your work changes. Use cases describe the operational problem you need solved.

Explore all use cases →

ONE PLATFORM

Different workflows. One production intelligence foundation.

Every solution is powered by the same architecture. Explore each capability on the Platform pages.

TELEMETRY · CHANGES · RUNTIME CONTEXT

Explore the Platform →