AUTOBSERVE FOR SRE & PLATFORM ENGINEERING

Operate incidents—not alert streams.

Correlate production signals into incidents, assemble evidence, understand system impact and govern recovery—before on-call engineers become the correlation engine.

Signals → Human correlation → DecisionSignals → Incident → Evidence → Impact → Governed decision

See the Incident Workflow ↓Become a Design Partner See this perspective on Solutions →

Production

Incident ready

14:31:02Checkout latency
14:31:07Checkout errors
14:31:11Payment latency
14:31:18Failed traces
14:31:22Order errors
14:31:26Pod restart
14:31:29Checkout saturation

Correlating

Same incident window
Shared runtime path
Failure propagation
Recent deployment
Customer-facing impact

7 signals1 incident

Incident

Checkout degradation

Priority: HIGH
Confidence: HIGH
Origin: checkout-api
Decision: INTERRUPT

THE OPERATING PROBLEM

Telemetry scaled. Human attention didn't.

Distributed systems fail through relationships. Traditional monitoring often evaluates symptoms independently, leaving humans to reconstruct the incident across telemetry, topology, changes and ownership boundaries.

01
10 SERVICES
- Telemetry
- Alerts
- Dependencies
Humans can still reconstruct incidents.
02
100 SERVICES
- More telemetry
- More alerts
- More ownership boundaries
- More dependencies
- More deployments
Correlation becomes expensive.
03
1,000 SERVICES
- Signals × Dependencies × Changes × Teams
HUMANS BECOME THE CORRELATION ENGINE

THE REAL COST

The expensive part isn't receiving the alert.

It's everything humans have to reconstruct afterwards.

ALERT — checkout latency
Open dashboard
Payment alert
Check logs
Order alert
Check traces
Check Kubernetes
Check deployment
Inspect dependencies
Find owners
Build hypothesis
Run queries
Choose response
Watch recovery

THE AUTOBSERVE MODEL

Move humans later in the incident lifecycle.

Machines absorb correlation, prioritisation, evidence gathering and impact analysis. SREs govern policies, exceptions and consequential decisions.

TODAY

SIGNALS
HUMAN
Correlate
Prioritise
Investigate
Explain
Respond
Verify

AUTOBSERVE

SIGNALS
Correlatemachine
Prioritisemachine
Gather evidencemachine
Build incident contextmachine

OPERATIONAL BOUNDARY

Inspect / Decide
Governed response
Verify

HUMAN BOUNDARY MATURES

V1

Machine

Detect
Correlate
Investigate

Human

Respond

MATURE

Machine

Detect
Correlate
Investigate
Recommend
Policy evaluate

Human

Approve exception

01 / DECIDE

Not every abnormal signal deserves an engineer.

Observe, enrich, or interrupt—based on impact, correlation and confidence. Reliability includes knowing when not to page someone.

DECISION LANES

OBSERVE
CPU anomaly
inventory-worker
Deviation
High
Impact
Low
Correlation
Weak
DO NOT INTERRUPT
ENRICH
Search latency
search-api
Deviation
High
Impact
Medium
Confidence
Medium
GATHER EVIDENCE
INTERRUPT
Checkout failure
checkout-api
Impact
High
Correlation
Strong
Confidence
High
PAGE ON-CALL

Reliability includes knowing when not to page someone.

02 / CORRELATE

Turn symptoms into an incident model.

Related signals become one incident with origin, symptoms and evidence—not six competing alerts.

BEFORE

checkout latencyALERT
checkout errorsALERT
payment latencyALERT
order failuresALERT
trace failuresALERT
pod restartALERT
checkout saturationALERT

INCIDENT

Checkout degradation

checkout-api

LIKELY ORIGIN?

payment-api

SYMPTOM

order-api

SYMPTOM

RELATED EVIDENCE: pod restart

Correlated signals: 7
Notifications: 1

RELATIONSHIP SEMANTICS

LIKELY ORIGIN
PROPAGATED SYMPTOM
CONTRIBUTING FACTOR
RELATED EVIDENCE
UNRELATED

Correlation is not causation. Not every correlated event is a symptom.

RUNTIME

frontend

Healthy

checkout-api

◆ ORIGIN?

payment-api

→ SYMPTOM

order-api

→ SYMPTOM

payment-db

Healthy

TIME

14:30:00Deploy checkout-api v2.14.7
14:31:23Checkout degradation
+18s — Payment degradation
+29s — Order degradation

LIKELY FAILURE DIRECTION

checkout-api
payment + orders
customer impact

INCIDENT QUEUE

HIGH
INTERRUPT
Checkout degradation
Customer-facing · 3 services affected · Confidence HIGH
MEDIUM
INVESTIGATE
Search latency
Limited impact · stable · Confidence MEDIUM
OBSERVING
NO INTERRUPT
Inventory CPU anomaly
No downstream impact · Confidence LOW

03 / INVESTIGATE · 04 / IMPACT

Start on-call with context already assembled.

What happened across the system, how serious is it, and where should attention focus—before the first dashboard.

Developer asks why a service failed. SRE asks what happened across the system, how serious it is, and where to focus.

INCIDENT

Checkout degradation

Started: 14:31:23
Priority: HIGH
Customer-facing: YES
Likely origin: checkout-api
Blast radius: 3 services
Recent change: v2.14.7
Confidence: HIGH
Evidence: 8 items
Signals: 7 correlated
Notifications: 1 sent · 6 suppressed

Open Investigation

INVESTIGATION PLAN

Recent changes
Baseline comparison
New error patterns
Failed request paths
Dependency behaviour
Infrastructure hypothesis

CURRENT EXPLANATION

checkout-api v2.14.7 likely introduced a regression.

Confidence · HIGH

SUPPORTING

Deployment timing
Latency deviation
New exception
Trace divergence
Failure propagation

CONTRADICTING

Database normal

Inspect Evidence View Alternatives

04 / UNDERSTAND IMPACT

Know what's affected before chasing every symptom.

WEB

Healthy

CHECKOUT

◆ DEGRADED

PAYMENT

→ IMPACTED

ORDERS

→ IMPACTED

BANK

Healthy

SEARCH

Healthy

INVENTORY

Healthy

BLAST RADIUS

Directly degraded: 1 service
Downstream affected: 2 services
Unaffected critical paths: 3
Customer-facing: YES
Owners involved: Checkout · Payments

Topology powers localisation, suppression, impact, routing and response safety—not a visualisation feature.

05 / RESPOND · 06 / VERIFY

Turn diagnosis into a governed response—then verify recovery.

Recommend within policy, require approval when needed, and verify system outcomes—not only that a command ran.

CURRENT EXPLANATION
checkout-api v2.14.7
Confidence HIGH
↓
RESPONSE CANDIDATE
Rollback
v2.14.7 → v2.14.2
↓
RISK ASSESSMENT
Risk
LOW
Expected impact
Checkout only
↓
POLICY EVALUATION
Environment
Production
Service tier
Tier 1
Rollback
ALLOWED
Human approval
REQUIRED

RESPONSE AUTONOMY

CONTEXT
System gathers evidence.
RECOMMEND
System proposes response.
APPROVE
Human authorises action.
BOUNDED AUTONOMY
Pre-authorised action within policy.
Inactive
AUTONOMOUS RECOVERY
Future / policy dependent
Inactive

Autonomy is an operational control system—not an AI stunt. Unsupported levels stay inactive.

06 / VERIFY

Execution success isn't recovery.

Verify the system outcome—not only that a command completed.

Rollback

Completed 14:33:17

Verifying recovery

Checkout latency812 ms267 ms✓ RECOVERED
Error rate18.4%1.2%✓ RECOVERED
PaymentHEALTHY✓ RECOVERED
OrdersHEALTHY✓ RECOVERED

Recovery confidence: HIGH
Incident: RESOLVED

SRE INCIDENT WORKSPACE

One place for the operational decision.

Incident, investigation, system impact, timeline and governed response—together.

Incident

Checkout degradation

Priority: HIGH
Customer-facing: Customer-facing
Likely origin: checkout-api

Signals: 7 correlated
Notifications: 1 sent·
Decision: INTERRUPT

INCIDENT

Checkout degradation

Priority: HIGH
Started: 14:31:23

INCIDENT

Priority: HIGH
Impact: Customer-facing
Signals: 7 correlated
Notifications: 1 sent·
Decision: INTERRUPT

INVESTIGATION

Current explanation

checkout-api v2.14.7 likely introduced a regression.

Confidence · HIGH

Evidence

Change
Metrics
Logs
Traces
DB normal

SYSTEM

frontend
checkout
payment / orders

Blast radius: 3 services
Owners: Checkout · Payments

INCIDENT TIMELINE

Deploy
Signals
Incident
Diagnosis
Decision
Recovery

RESPONSE

Rollback checkout-api v2.14.7

Risk LOW · Policy allows rollback · Approval REQUIRED

Inspect Policy

FOR PLATFORM ENGINEERING

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

Shared incident models, policies, topology and investigation workflows—instead of every team reinventing alerts, runbooks and integrations.

BEFORE

TEAM A
- Grafana
- Custom alerts
- Slack scripts
- Runbooks
TEAM B
- Grafana
- Different alerts
- Pager scripts
- Wiki
TEAM C
- Datadog
- Custom alerts
- Webhooks
- Docs

PLATFORM ENGINEERING maintains everything

AUTOBSERVE

Platform team

PRODUCTION INTELLIGENCE LAYER

Incident models
Policies
Topology
Investigation
Response boundaries
Governance

TEAM A
same capability
TEAM B
same capability
TEAM C
same capability

PRODUCTION-DEBUGGING GOLDEN PATH

Create a production-debugging golden path.

Teams still instrument services. AutoObserve supplies the shared incident model, investigation workflow and response policy—not automatic onboarding of every service.

TODAY

NEW SERVICE
Instrument
Build dashboards
Create alerts
Configure routing
Create runbooks
Build integrations
Teach team the workflow

TARGET

NEW SERVICE
OpenTelemetry
Runtime context
Shared incident model
Shared investigation workflow
Shared response policy

POLICY

Policy is a first-class product object.

POLICY

Production · Tier 1 Services

INTERRUPTION

Minimum confidence: HIGH
Customer impact: REQUIRED

RESPONSE

Restart: APPROVAL
Rollback: APPROVAL
Database failover: MANUAL

VERIFICATION

Required: YES

AUDIT

Record all decisions: YES

CONTROL · TELEMETRY

Automate toil without surrendering operational control.

Explainable decisions, policy-bound actions, auditable operations and uncertainty-aware reasoning—on top of the telemetry estate you already run.

TRUST ARCHITECTURE

Explainable decisions
Why did AutoObserve interrupt, suppress, correlate, prioritise or recommend?
Policy-bound actions
Which actions can execute, under which environments and service tiers?
Auditable operations
Preserve evidence, decisions, approvals, actions and verification.
Uncertainty-aware reasoning
Missing evidence reduces confidence instead of disappearing.

AUTONOMY → POLICY → SAFE ACTION

Autonomy

Policy

Confidence
Criticality
Blast radius
Environment
Approval
Verification

Safe action

INCIDENT CONFIDENCE

Incomplete coverage reduces causal confidence.

Never silently transform partial telemetry coverage into a confirmed explanation.

WORK WITH YOUR TELEMETRY

Add incident intelligence without rebuilding your telemetry estate.

One incident context across specialised telemetry systems.

APPLICATIONS
OpenTelemetry
Collectors
Metrics · Logs · Traces
Existing telemetry systems
AutoObserve
Incident intelligence · Investigation · Runtime context · Governed response

Works alongside — not a replacement

Prometheus
Loki
Tempo / Jaeger
ClickHouse
Kubernetes

OPERATIONAL OUTCOMES

Optimise the work around incidents.

Fewer interruptions, less triage, faster diagnosis and verified recovery—without fake productivity percentages.

FEWER INTERRUPTIONS
Human interruptions vs incident candidates
LESS TRIAGE
Manual triage actions per incident
FASTER DIAGNOSIS
Incident created → viable explanation
FASTER RECOVERY
Incident created → recovery verified

MTTR alone compresses too much. The useful question is where the lifecycle bottlenecks—detection, triage, investigation, diagnosis, decision, action or verification.

LEARNING LOOP

Every resolved incident should improve the next one.

Opportunistic feedback—not mandatory postmortem bureaucracy.

INCIDENT
DECISION
INVESTIGATION
RESPONSE
OUTCOME
FEEDBACK
FUTURE DECISIONS

Was this interruption necessary?
Was the explanation correct?

POWERED BY AUTOBSERVE

One foundation behind the incident lifecycle.

Now that the workflow is clear, these names have meaning.

RELATED PATHS

Solve a specific operational problem.

Keep the detailed stories on their dedicated use-case pages.

Operate incidents—not alert streams.

Telemetry scaled. Human attention didn't.

The expensive part isn't receiving the alert.

Move humans later in the incident lifecycle.

Not every abnormal signal deserves an engineer.

CPU anomaly

Search latency

Checkout failure

Turn symptoms into an incident model.

Start on-call with context already assembled.

Checkout degradation

Know what's affected before chasing every symptom.

Turn diagnosis into a governed response—then verify recovery.

Execution success isn't recovery.

One place for the operational decision.

Checkout degradation

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

Create a production-debugging golden path.

Policy is a first-class product object.

Automate toil without surrendering operational control.

Explainable decisions

Policy-bound actions

Auditable operations

Uncertainty-aware reasoning

Incomplete coverage reduces causal confidence.

Add incident intelligence without rebuilding your telemetry estate.

Optimise the work around incidents.

Every resolved incident should improve the next one.

One foundation behind the incident lifecycle.

Solve a specific operational problem.

Reduce Alert Fatigue

Root Cause Analysis

Reduce MTTR

Debug Production