The **InfoTechCanon Observability Model** defines a canonical seed model for representing telemetry, signals, events, logs, metrics, traces, profiles, health, service levels, alerts, incidents as observed phenomena, dashboards, runbooks, investigations, and operational evidence.
It exists to make runtime understanding interoperable across systems, services, platforms, networks, security, delivery pipelines, data products, and agentic operations.
This standard provides a canonical vocabulary for:
- telemetry sources,
- resources,
- signals,
- metrics,
- logs,
- events,
- traces,
- spans,
- profiles,
- exemplars,
- attributes,
- dimensions,
- correlation context,
- service level indicators,
- service level objectives,
- error budgets,
- health states,
- alerts,
- notifications,
- incidents,
- investigations,
- dashboards,
- runbooks,
- observability evidence,
- and feedback loops.
---
# 2. Position in InfoTechCanon
The Observability Model is a **domain standard** within InfoTechCanon.
It depends on the existing seed standards as follows:
DevSecOps owns delivery events and deployment records.
Observability owns runtime signals used to verify deployments and measure change impact.
Example:
```text
DeploymentRecord produces DeploymentEvent
DeploymentHealthSignal verifies DeploymentRecord
ChangeFailure detected_by ObservabilitySignal
```
## 3.6 Boundary with Data
Data owns dataset, classification, lineage, quality, and retention semantics.
Observability telemetry may itself be data, but Observability owns telemetry-specific semantics.
Example:
```text
LogDataset classified_as Restricted
MetricStream has_retention RetentionRuleReference
TraceSample derived_from RuntimeWorkload
```
---
# 4. Research Basis and External Alignment
This seed standard draws on several mature observability and operations bodies of knowledge.
## 4.1 OpenTelemetry
OpenTelemetry provides a broad observability framework covering traces, metrics, logs, baggage, resources, semantic conventions, instrumentation, collection, and export. Its semantic conventions define common attributes that give meaning to telemetry across systems.
## 4.2 SRE and Service Level Objectives
SRE practice distinguishes Service Level Indicators, Service Level Objectives, Service Level Agreements, and error budgets. It emphasizes that SLOs should measure user-relevant reliability and guide operational decision-making.
## 4.3 Prometheus and OpenMetrics
Prometheus and OpenMetrics influence metric naming, metric exposition, labels, time series, counters, gauges, histograms, summaries, and scraping/pull-based metric collection.
## 4.4 CloudEvents
CloudEvents standardizes common event metadata for interoperability across services, platforms, and systems. It is a strong mapping target for event structure and routing metadata.
## 4.5 IT Operations and Incident Management
IT operations practice distinguishes alerts, incidents, problems, changes, runbooks, on-call, escalation, resolution, and post-incident review. The Observability Model provides signal semantics while Task and Governance own work and decision semantics.
## 4.6 AIOps and Event Correlation
AIOps practice emphasizes correlation, anomaly detection, event deduplication, root-cause analysis, topology-aware alerting, and automated remediation. These are advanced profiles rather than mandatory core concepts.
---
# 5. Seed Standard Design Stance
This standard is a **seed standard**, not a vendor-specific observability schema.
Those may be mapped, assimilated, profiled, or handled by adjacent standards.
---
# 7. Normative Language
The following terms are used normatively:
- **SHALL** indicates a mandatory rule for conformance.
- **SHOULD** indicates a recommended practice.
- **MAY** indicates an optional capability.
- **MUST NOT** indicates a prohibited practice.
- **SEED** marks a concept defined provisionally here but open to later refinement.
- **EXTRACT** marks a concept that may later move to a more specialized standard.
---
# 8. Core Principles
## 8.1 Observability Is More Than Monitoring
Monitoring checks known conditions.
Observability supports understanding system behavior, including unknown or emergent failure modes, through signals and correlation.
## 8.2 Telemetry Is Not Insight
Raw telemetry becomes useful through context, correlation, aggregation, interpretation, and action.
## 8.3 Signal Is Not Incident
A signal, alert, or event may indicate a possible problem.
An incident is an operationally relevant situation requiring response.
## 8.4 Alert Is Not Evidence by Itself
An alert indicates that a rule fired or condition was detected.
Evidence should include the underlying signals, query, thresholds, state, and context.
## 8.5 Metrics, Logs, Traces, Events, and Profiles Are Distinct
Each signal type has different strengths and should not be collapsed into one generic “event” concept.
## 8.6 Service Levels Must Be Explicit
SLIs, SLOs, and error budgets SHOULD be modeled explicitly when reliability is important.
## 8.7 Correlation Requires Identity
Telemetry SHOULD be linked to canonical landscape entities, deployment records, network endpoints, data resources, or security entities where possible.
## 8.8 Observability Must Support Feedback
Observability should feed tasks, incidents, governance reviews, deployment verification, security detection, reliability improvement, and standard evolution.
## 8.9 External Standards Are Mapped, Not Obeyed
The Observability Model MAY map to OpenTelemetry, Prometheus, OpenMetrics, CloudEvents, SRE SLO concepts, ITIL incident practices, and vendor schemas.
It MUST NOT subordinate its internal semantics to any single external model.
---
# 9. Canonical Seed Metadata
Every observability artifact SHOULD support structured metadata.
Recommended front matter:
```yaml
---
id: itc-obs:Metric
type: concept
standard: InfoTechCanonObservabilityModel
standard_version: RC1-seed
status: candidate
canonical_owner: InfoTechCanonObservabilityModel
preferred_label: Metric
related:
- itc-obs:TimeSeries
- itc-obs:SLI
- itc-obs:AlertRule
mappings:
- itc-map:metric-to-opentelemetry-metric
---
```
Recommended artifact statuses:
```text
idea
draft
candidate
release-candidate
adopted
stable
deprecated
retired
```
Recommended concept statuses:
```text
proposed
experimental
candidate
canonical
deprecated
retired
```
---
# 10. Root Observability Taxonomy
```text
ObservabilityEntity
├── TelemetryEntity
│ ├── Telemetry
│ ├── TelemetrySource
│ ├── ObservedResource
│ ├── ResourceAttribute
│ ├── Signal
│ ├── SignalSource
│ └── TelemetryPipeline
├── MetricEntity
│ ├── Metric
│ ├── MetricInstrument
│ ├── TimeSeries
│ ├── MetricPoint
│ ├── Counter
│ ├── Gauge
│ ├── Histogram
│ ├── Summary
│ └── Exemplar
├── LogEntity
│ ├── Log
│ ├── LogRecord
│ ├── LogStream
│ ├── LogLevel
│ ├── LogContext
│ └── StructuredLogField
├── TraceEntity
│ ├── Trace
│ ├── Span
│ ├── SpanEvent
│ ├── SpanLink
│ ├── TraceContext
│ ├── Baggage
│ └── TraceSample
├── EventEntity
│ ├── Event
│ ├── EventEnvelope
│ ├── EventSource
│ ├── EventType
│ ├── EventConsumer
│ └── EventCorrelationKey
├── ProfileEntity
│ ├── Profile
│ ├── ProfilingSample
│ ├── ResourceProfile
│ └── PerformanceProfile
├── ReliabilityEntity
│ ├── SLI
│ ├── SLO
│ ├── SLAReference
│ ├── ErrorBudget
│ ├── BurnRate
│ ├── HealthState
│ └── AvailabilityWindow
├── AlertingEntity
│ ├── AlertRule
│ ├── Alert
│ ├── Notification
│ ├── AlertRoute
│ ├── AlertSuppression
│ ├── AlertCorrelation
│ └── EscalationReference
├── OperationsEntity
│ ├── ObservedIncident
│ ├── Investigation
│ ├── Timeline
│ ├── Runbook
│ ├── Dashboard
│ ├── OperationalView
│ └── PostIncidentObservation
└── EvidenceEntity
├── ObservabilityEvidence
├── Query
├── QueryResult
├── Snapshot
├── Annotation
├── Correlation
└── RootCauseHypothesis
```
---
# 11. Core Concepts
## 11.1 ObservabilityEntity
An **ObservabilityEntity** is any identifiable concept used to represent telemetry, signals, correlation, health, service levels, alerts, incidents as observed phenomena, dashboards, runbooks, or operational evidence.
Recommended attributes:
```yaml
id:
entity_type:
canonical_name:
display_name:
lifecycle_state:
source_system:
created_at:
updated_at:
```
Optional attributes:
```yaml
owner:
steward:
observed_resource:
service:
environment:
source_confidence:
valid_from:
valid_to:
tags:
external_references:
```
---
## 11.2 Telemetry
**Telemetry** is machine-generated or manually recorded operational data about system behavior, state, performance, events, or activity.
Examples:
```text
metric sample
log record
trace span
event
profile sample
flow record
health check result
```
---
## 11.3 TelemetrySource
A **TelemetrySource** is a system, component, agent, collector, service, device, pipeline, or actor that emits or provides telemetry.
---
## 11.4 ObservedResource
An **ObservedResource** is the entity about which telemetry is emitted or collected.
Observed resources SHOULD map to Landscape, Network, Data, Security, or DevSecOps entities where possible.
---
## 11.5 ResourceAttribute
A **ResourceAttribute** is an attribute describing an observed resource.
Examples:
```text
service.name
service.version
deployment.environment
host.name
cloud.region
k8s.cluster.name
container.image.name
```
---
## 11.6 Signal
A **Signal** is an interpretable unit or stream of observability data.
Signal types include:
```text
metric
log
trace
event
profile
alert
health check
synthetic result
```
---
## 11.7 SignalSource
A **SignalSource** is the origin of a signal.
---
## 11.8 TelemetryPipeline
A **TelemetryPipeline** is a flow that collects, processes, transforms, samples, enriches, routes, stores, or exports telemetry.
---
## 11.9 Metric
A **Metric** is a measurement of a system, service, resource, or process over time.
Metrics may be used for alerting, dashboards, SLOs, capacity planning, anomaly detection, and evidence.
---
## 11.10 MetricInstrument
A **MetricInstrument** defines the kind of measurement instrument.
Seed instrument types:
```text
counter
gauge
histogram
summary
up_down_counter
observable_gauge
```
---
## 11.11 TimeSeries
A **TimeSeries** is a sequence of metric points over time for a metric and a set of dimensions or labels.
---
## 11.12 MetricPoint
A **MetricPoint** is a single measurement value at a time.
---
## 11.13 Counter
A **Counter** is a monotonically increasing measurement of occurrences or accumulated quantity.
---
## 11.14 Gauge
A **Gauge** is a measurement that can go up or down.
---
## 11.15 Histogram
A **Histogram** is a distribution of measurements across buckets or ranges.
---
## 11.16 Summary
A **Summary** is a metric representation of observations including quantiles or summary statistics.
---
## 11.17 Exemplar
An **Exemplar** is a representative sample connecting an aggregate metric point to a trace, log, or other detailed signal.
---
## 11.18 Log
A **Log** is a stream or collection of timestamped records describing events, state, actions, or messages.
---
## 11.19 LogRecord
A **LogRecord** is a single log entry.
Recommended attributes:
```yaml
timestamp:
severity:
message:
body:
resource:
trace_id:
span_id:
attributes:
source:
```
---
## 11.20 LogStream
A **LogStream** is a sequence of log records from a source or resource.
---
## 11.21 LogLevel
A **LogLevel** is a severity or importance category for log records.
Examples:
```text
trace
debug
info
warn
error
fatal
```
---
## 11.22 LogContext
**LogContext** is contextual metadata attached to log records.
Examples:
```text
request id
trace id
user id reference
tenant id
deployment version
environment
component
```
---
## 11.23 Trace
A **Trace** is a representation of a request, transaction, workflow, or operation as it moves through a distributed system.
---
## 11.24 Span
A **Span** is a single timed operation within a trace.
Recommended attributes:
```yaml
trace_id:
span_id:
parent_span_id:
name:
kind:
start_time:
end_time:
status:
attributes:
events:
links:
```
---
## 11.25 SpanEvent
A **SpanEvent** is a timestamped event attached to a span.
---
## 11.26 SpanLink
A **SpanLink** connects a span to another span or trace context.
---
## 11.27 TraceContext
**TraceContext** is propagation metadata that links operations across process, service, or network boundaries.
---
## 11.28 Baggage
**Baggage** is contextual metadata propagated across process boundaries.
Baggage SHOULD be governed carefully when it may contain sensitive data.
---
## 11.29 TraceSample
A **TraceSample** is a selected trace or subset of trace data retained or analyzed.
---
## 11.30 Event
An **Event** is a record of an occurrence and its context.
Events may be operational, domain, security, deployment, infrastructure, or business events.
---
## 11.31 EventEnvelope
An **EventEnvelope** is structured metadata around event data.
CloudEvents is a primary mapping target.
---
## 11.32 EventSource
An **EventSource** is the producer or origin of an event.
---
## 11.33 EventType
An **EventType** classifies the kind of occurrence represented by an event.
---
## 11.34 EventConsumer
An **EventConsumer** is an actor, system, service, or pipeline that consumes events.
---
## 11.35 EventCorrelationKey
An **EventCorrelationKey** links events to related traces, logs, requests, incidents, deployments, or resources.
Represent observability signals used for security detection, investigation, and evidence.
```
Included concepts:
```text
SecuritySignal
SecurityLogRecord
DetectionEvent
Alert
TraceEvidence
AccessSessionLog
AuditLogReference
SecurityEvidence
```
Security interpretation remains owned by the Security Model.
---
# 16. Mapping Model for the Observability Standard
Mappings relate InfoTechCanon observability concepts to external standards, tools, and products.
## 16.1 Mapping Types
Recommended mapping types:
```text
exactMatch
closeMatch
broadMatch
narrowMatch
relatedMatch
conflictMatch
gapMatch
derivedFrom
regulatoryReference
toolEquivalent
```
## 16.2 Mapping Record
Example:
```yaml
id: itc-map:span-to-opentelemetry-span
source_concept: itc-obs:Span
target_body: OpenTelemetry
target_version: "current"
target_concept: Span
mapping_type: closeMatch
scope:
- distributed tracing
not_valid_for:
- all event or log semantics
rationale: >
OpenTelemetry Span is the primary mapping target for timed operations in traces.
InfoTechCanon keeps Span as a canonical concept to allow mappings to other tracing systems.
confidence: high
status: candidate
owner: InfoTechCanonObservabilityModel
```
## 16.3 Seed Mapping Targets
The Observability Model SHOULD maintain mappings to:
```text
OpenTelemetry
OpenTelemetry Semantic Conventions
Prometheus
OpenMetrics / Prometheus exposition format
CloudEvents
W3C Trace Context
Google SRE SLI/SLO/Error Budget concepts
Grafana dashboards and alerting
Prometheus Alertmanager
Loki / LogQL
Jaeger
Tempo
Elastic Observability
Datadog
New Relic
Splunk
OpenSearch
ITIL incident concepts
NetFlow / IPFIX
VPC Flow Logs
Kubernetes events and metrics
service mesh telemetry
```
---
# 17. Assimilation Hooks
The Observability Model SHALL be able to receive new observability standards, tool models, telemetry schemas, incident practices, and operational patterns through the InfoTechCanon assimilation process.
## 17.1 Assimilation Triggers
Assimilation may be triggered by:
```text
new telemetry standard
new observability backend
new incident-management tool
new SLO practice
new dashboard model
new alerting model
new tracing model
new logging schema
new AIOps product
new runtime verification practice
new recurring signal classification conflict
```
## 17.2 Observability Assimilation Output
An observability assimilation SHOULD produce:
```text
source summary
extracted observability concepts
concept comparison matrix
gap list
conflict list
mapping file
candidate new concepts
candidate relationship changes
candidate pattern changes
candidate profile changes
open questions
```
## 17.3 Recommended First Assimilation Candidates
```text
OpenTelemetry specification and semantic conventions
Prometheus / OpenMetrics
CloudEvents
W3C Trace Context
Google SRE SLO chapters
Grafana dashboard and alerting models
Prometheus Alertmanager
Kubernetes events and metrics
VPC Flow Logs / NetFlow / IPFIX
ITIL incident management concepts
```
---
# 18. Integration with Other InfoTechCanon Standards
## 18.1 Landscape Model
Observability links signals to:
```text
ApplicationService
TechnicalService
RuntimeWorkload
Environment
Endpoint
DataStore
DeploymentRecord
NetworkEntity
```
## 18.2 Organization Model
Observability imports organization concepts for:
```text
service owner
on-call responder
team
escalation target
runbook owner
incident commander
```
## 18.3 Governance Model
Observability imports governance concepts for:
```text
evidence
control result
review
assurance
policy
SLA obligation
audit evidence
```
## 18.4 Task Model
Observability creates or references:
```text
incident task
investigation task
remediation task
follow-up task
reliability improvement task
```
## 18.5 Tagging Standard
Observability uses tags for:
```text
service
environment
severity
signal type
dashboard category
incident category
team
```
Tags must not replace ObservedResource, AlertRule, SLO, or Evidence records.
## 18.6 Access Control Model
Observability imports access concepts for:
```text
dashboard access
log access
trace access
incident tool access
telemetry pipeline access
sensitive telemetry access
```
## 18.7 Security Model
Security imports observability concepts for:
```text
security signal
detection evidence
security alert
audit log
trace evidence
incident timeline
```
## 18.8 Data Model
Data imports observability concepts when telemetry is treated as a dataset and for data freshness, quality, and lineage signals.
## 18.9 DevSecOps Model
DevSecOps imports observability concepts for:
```text
deployment verification
change failure detection
delivery metric
runtime feedback
SLO impact
```
## 18.10 Network Model
Network imports observability concepts for:
```text
flow logs
reachability test results
latency
packet loss
DNS logs
endpoint health
```
---
# 19. Canon Interface Card Usage
Subsystems that implement or produce observability knowledge SHOULD publish a Canon Interface Card.
Example:
```yaml
subsystem: prometheus-importer
implements:
- InfoTechCanonObservabilityModel
- PrometheusOpenMetricsProfile
produces:
- Metric
- TimeSeries
- MetricPoint
- AlertRule
- Alert
- QueryResult
consumes:
- ObservedResource
- Service
- Environment
relations:
- Metric emitted_by ObservedResource
- Alert triggered_by AlertRule
- Alert affects Service
source_of_truth:
metric_samples: Prometheus
alert_rule_state: Prometheus
known_deviations:
- resource identity depends on labels
- long-term retention may be external
```
---
# 20. Retrieval Requirements
The Observability Model is designed for markdown-based infospaces.
## 20.1 Required Retrieval Properties
Every major concept SHOULD provide:
- stable heading,
- stable identifier,
- short definition,
- longer explanation,
- examples,
- distinction notes,
- relationship examples,
- mapping hooks,
- profile references,
- and common mistakes.
## 20.2 Agent Brief
A mature Observability Model SHOULD include an `agent-brief.md` file with:
```text
purpose
scope
owned concepts
imported concepts
core distinctions
do / do not rules
relationship patterns
minimal examples
common mistakes
profile list
mapping list
```
## 20.3 Indexes
The observability information space SHOULD provide indexes by:
```text
concept
relationship
signal type
metric
log
trace
event
resource
service
alert
SLO
dashboard
incident
profile
pattern
mapping target
status
source system
```
---
# 21. Conformance Levels
## 21.1 Reference-Conformant
A document or system is reference-conformant if it uses Observability Model terminology consistently but does not implement structured metadata or validation rules.
## 21.2 Metadata-Conformant
A system is metadata-conformant if it uses stable identifiers, concept names, lifecycle states, source metadata, and relationship types.
## 21.3 Signal-Conformant
A system is signal-conformant if it distinguishes metrics, logs, traces, events, profiles, alerts, and health signals.
## 21.4 Resource-Correlated
A system is resource-correlated if observability signals can be linked to observed resources and canonical landscape entities.
## 21.5 SLO-Conformant
A system is SLO-conformant if it represents SLIs, SLOs, error budgets, burn rates, and measurement windows.
## 21.6 Evidence-Conformant
A system is evidence-conformant if observability claims, incidents, alerts, and service-level states can be linked to evidence.
## 21.7 Profile-Conformant
A system is profile-conformant if it implements a declared Observability Profile and passes its validation rules.
## 21.8 Assimilation-Conformant
A system or repository is assimilation-conformant if it can accept external observability concepts through the InfoTechCanon assimilation workflow and produce mappings, gaps, conflicts, and proposed changes.
---
# 22. Validation Rules
Initial validation rules:
```text
VAL-OBS-001: Metric, LogRecord, Trace, Span, Event, Profile, Alert, and Incident SHOULD be modeled as distinct concepts.
VAL-OBS-002: Telemetry SHOULD reference an ObservedResource where possible.
VAL-OBS-003: ObservedResource SHOULD map to a Landscape, Network, Data, Security, or DevSecOps entity where possible.
VAL-OBS-004: Metric SHOULD declare unit, instrument type, source, and dimensions where available.
VAL-OBS-005: TimeSeries SHOULD distinguish metric identity from labels/dimensions.
VAL-OBS-006: LogRecord SHOULD include timestamp, severity, source, and body where available.
VAL-OBS-007: Span SHOULD include trace id, span id, timing, name, status, and parent/link references where available.
VAL-OBS-008: Event SHOULD distinguish event data from event context metadata.
VAL-OBS-009: Alert SHOULD reference AlertRule or source condition where available.
VAL-OBS-010: AlertRule SHOULD reference query or condition, threshold, time window, owner, and runbook where applicable.
VAL-OBS-011: SLO SHOULD reference SLI, target, measurement window, service, and evidence source.
VAL-OBS-012: ErrorBudget SHOULD derive from an SLO.
VAL-OBS-013: Dashboard SHOULD NOT be treated as evidence unless a Snapshot or QueryResult is captured.
VAL-OBS-014: Incident SHOULD NOT be inferred solely from one alert unless profile permits it.
VAL-OBS-015: RootCauseHypothesis SHOULD remain distinguishable from verified cause.
VAL-OBS-016: Missing, stale, or delayed telemetry SHOULD be representable as signal state.
VAL-OBS-017: Tags MUST NOT replace resource identity, SLO definitions, alert rules, or evidence.
VAL-OBS-018: Imported external observability concepts SHOULD be represented through mapping records rather than silently reused.
VAL-OBS-019: Profiles MUST NOT redefine canonical concepts. They may constrain them.
VAL-OBS-020: Telemetry containing sensitive data SHOULD reference Data, Security, Access Control, or Governance constraints where relevant.
```
---
# 23. Anti-Patterns
## 23.1 Dashboard as Truth
Treating a dashboard view as evidence without preserving query, time window, data source, or snapshot.
## 23.2 Alert Equals Incident
Treating every alert as an incident.
## 23.3 Metric Soup
Collecting many metrics without ownership, resource identity, interpretation, or action path.
## 23.4 Logs Without Context
Logging messages that cannot be correlated to service, request, trace, tenant, deployment, or resource.
## 23.5 Traces Without Boundaries
Tracing calls without linking them to service ownership, deployment version, or runtime resource.
## 23.6 SLO Theater
Creating SLOs that do not reflect user experience or guide operational decisions.
## 23.7 Alert Without Runbook
Creating alerts without ownership, runbook, dashboard, or response expectation.
## 23.8 Missing Signal Blindness
Failing to alert when telemetry stops arriving.
## 23.9 Tool-Native Capture
Letting one observability backend define the internal observability model.
## 23.10 Telemetry Without Governance
Collecting sensitive logs, traces, or profiles without classification, retention, access control, or privacy consideration.
- Establish this standard as `InfoTechCanonObservabilityModel`.
- Add seed concepts, relationship vocabulary, patterns, and profiles.
- Define validation rules.
- Align with Landscape, Network, DevSecOps, Security, Data, Governance, Task, Access Control, and Tagging.
## Phase 2: First Assimilations
Recommended first assimilations:
```text
OpenTelemetry specification and semantic conventions
Prometheus / OpenMetrics
CloudEvents
W3C Trace Context
Google SRE SLO chapters
Grafana dashboard and alerting model
Prometheus Alertmanager
Kubernetes events and metrics
VPC Flow Logs / NetFlow / IPFIX
ITIL incident management concepts
```
## Phase 3: Profile Maturation
- Mature Small SaaS Observability Profile.
- Mature OpenTelemetry Profile.
- Mature Prometheus / OpenMetrics Profile.
- Mature CloudEvents Profile.
- Mature SRE Reliability Profile.
- Mature Incident Observability Profile.
- Mature Network Observability Profile.
- Mature Security Observability Profile.
## Phase 4: Tooling Integration
- Generate concept indexes.
- Generate agent brief.
- Create machine-readable YAML/JSON exports.
- Add validation scripts.
- Integrate telemetry pipelines, metrics, logs, traces, dashboards, alerts, incident tools, and service catalogs.
## Phase 5: Operational Intelligence Loop
- Connect telemetry to canonical resources.
- Connect alerts to tasks and incidents.
- Connect SLOs to governance and service ownership.
- Connect deployment records to runtime health signals.
- Connect security detections to security incidents.
- Connect network flows to reachability and exposure.
- Connect post-incident observations to improvements and standard evolution.
---
# 26. Summary
The InfoTechCanon Observability Model is the seed standard for representing telemetry, signals, metrics, logs, traces, events, profiles, alerts, SLOs, health, incidents as observed phenomena, and operational evidence.
Its most important commitments are:
```text
Separate telemetry, signal, metric, log, trace, span, event, profile, alert, and incident.
Link signals to canonical resources and landscape entities.
Treat SLOs, SLIs, error budgets, burn rates, and health states as first-class reliability concepts.
Use observability evidence to support governance, security, delivery, incident response, and operational review.
Map to OpenTelemetry, Prometheus/OpenMetrics, CloudEvents, SRE practices, and observability tools
without surrendering internal semantic autonomy.
Use profiles to make the model practical for SaaS systems, OpenTelemetry, Prometheus,
SRE reliability, incident response, network observability, and security observability.
```
This makes the Observability Model a core seed for runtime intelligence, production readiness, SRE practice, incident response, deployment verification, security detection, and agent-supported operations.
