API Monitoring Glossary
Definitions of key terms in API monitoring, observability, and API quality.
Agentic AI Monitoring
Continuous verification that the APIs and services consumed by autonomous AI agents — MCP servers, tool endpoints, vector databases, orchestration APIs — are behaving correctly and within their declared contracts. Unlike traditional API monitoring, agentic AI monitoring must account for semantic correctness (does the tool return what the model expects?), schema drift (has the response format changed without a version bump?), and multi-step workflow integrity. As AI agents increasingly run autonomously in production, reliability and contract integrity of the underlying API layer becomes a critical operational concern.
API Availability
The proportion of monitoring checks over a time period in which an API endpoint responds successfully — typically defined as returning an expected HTTP status code within an acceptable latency window. Usually expressed as a percentage (e.g. 99.9%). Availability measured from a single location may overstate actual quality: an endpoint reachable from a US data center may be unavailable or severely degraded for users in Southeast Asia. Multi-location availability across geographic PoPs provides a more accurate picture of real-world reliability.
API Conformance
The degree to which an API's live production behavior matches its stated specification — typically an OpenAPI document — and any applicable standards and business rules. API conformance is a complement to API contract testing, but more expansive: it tests not only the technical contract but also the business rules that need to be enforced by the API in context. A conformant API returns the documented status codes, headers, response schemas, and error formats in all tested scenarios. Continuous conformance testing catches schema drift and behavioral regression that availability monitoring misses.
API Latency
The elapsed time between a client sending a request and receiving the complete response, measured from the client's perspective. Reported at multiple percentiles — p50 (median), p95, and p99 — because averages obscure long-tail degradation affecting a significant proportion of users. Composed of DNS lookup, TCP connection, TLS negotiation, server processing, and data transfer. External synthetic monitoring measures full client-perceived latency including network transit and edge processing that internal APM cannot observe.
CASC Score
A composite API quality metric developed by APIContext that combines latency percentiles (p50–p99), availability, geographic consistency across monitoring locations, and conformance pass rate into a single number on a 0–10 scale. A CASC score above 9 indicates healthy performance; below 6 indicates a quality problem requiring immediate attention. Designed to make API quality understandable and comparable across endpoints, providers, and time periods without requiring deep technical knowledge to interpret.
FAPI 2.0
An OpenID Foundation security profile specifying minimum security requirements for APIs in financial and high-value contexts, building on OAuth 2.0 and OpenID Connect. Mandates Pushed Authorization Requests (PAR), DPoP sender-constrained access tokens, PKCE, JARM response mode, and mTLS or private_key_jwt client authentication. Referenced by or incorporated into the UK Open Banking Standard, Brazil's Open Finance framework, Australia's Consumer Data Right, and the US CFPB 1033 rule. Testing FAPI 2.0 conformance requires executing complete authentication flows with real credentials, not just checking endpoint availability.
mTLS
A variant of TLS in which both client and server present certificates to authenticate each other before establishing a connection, as opposed to standard TLS where only the server is authenticated. Used in API contexts where strong client identity is required — open banking (TPPs presenting qualified certificates to bank APIs) or service-to-service communication within zero-trust networks. Monitoring APIs that require mTLS requires the monitoring client to manage valid certificates and present them correctly on every check.
Multi-Step Workflow Testing
A form of synthetic monitoring in which a sequence of dependent API calls is executed as a single test scenario, preserving state — tokens, session identifiers, response data — across steps. Tests complete user journeys, for example: authenticate → retrieve account list → fetch transactions → initiate payment. Necessary for detecting integration failures that per-endpoint checks miss, and for validating APIs that require prior authentication before the target endpoint is reachable.
Open Banking
A regulatory and industry framework requiring banks and financial institutions to share customer financial data with authorized Third Party Providers via standardized APIs, with customer consent. Major implementations include the UK Open Banking Standard, Australia's Consumer Data Right (CDR), Brazil's Open Finance, and the US CFPB 1033 rule. Open banking APIs typically require FAPI-compliant security, machine-readable specifications, and demonstrable availability and performance standards.
OpenTelemetry (OTEL)
An open-source observability framework (CNCF project) that standardizes the collection of traces, metrics, and logs across distributed systems using a vendor-neutral wire format and SDK. In API monitoring, OTEL traces instrument each hop in a request path — DNS resolution, TLS handshake, connection, request transmission, response — providing precise latency attribution. APIContext generates OTEL-compatible spans at every stage of the synthetic request chain, making external API observations correlatable with internal APM data in Datadog, Splunk, or Dynatrace.
Point of Presence (PoP)
A geographically distributed network location from which monitoring checks are executed. Typically deployed in major cloud regions (AWS, GCP, Azure) and network exchange points to represent real-world client location diversity. Running checks from multiple PoPs simultaneously allows monitoring platforms to distinguish between global outages and regional performance degradation, and to detect CDN or edge-layer failures invisible to single-location checks. APIContext operates 125+ PoPs worldwide.
Private Node
A monitoring probe deployed inside a customer's own infrastructure — on-premise, private cloud, or corporate network — rather than in a public cloud PoP. Allows organizations to monitor internal APIs not exposed to the public internet, verify API behavior from inside a firewall or VPN, and test from network positions reflecting specific partner or internal consumer access patterns. Common in financial services and enterprise IT where sensitive APIs are not internet-accessible.
SLA (Service Level Agreement)
A contractual commitment between a service provider and a customer defining minimum acceptable service levels, typically expressed as percentage uptime or maximum response time over a measurement period. Failure to meet SLAs can trigger credits, penalties, or termination rights. Measuring SLA compliance requires an agreed-upon methodology, which is why third-party synthetic monitoring from external locations is commonly used as the reference measurement rather than internal system metrics.
SLO (Service Level Objective)
An internal target for service reliability or performance, typically more stringent than the SLA, used to drive engineering priorities before a contractual threshold is breached. Commonly expressed as an error budget — the permissible number of failures within a time window. Unlike SLAs, SLOs are not contractual; they enable teams to manage reliability proactively. Well-defined SLOs per API endpoint allow teams to triage incidents by business impact.
Synthetic API Monitoring
The practice of sending scripted, simulated API requests to production endpoints on a continuous schedule from external locations, without relying on real user traffic. Provides consistent baseline measurements of API availability, latency, and correctness regardless of traffic volume. Particularly valuable for catching regressions before they affect customers, verifying geographic consistency, and producing SLA-grade compliance evidence. The term "synthetic" refers to the fact that traffic is artificially generated rather than observed from actual users.
