An Intellyx Brain Candy Update
When we last spoke with APIContext, they explained how they checked your APIs (and the third party APIs you use) for performance and conformance, using synthetic data.
They are now also monitoring the distributed, machine-machine systems behind those APIs to detect and triage failures that other monitoring tools may not detect, or may detect inconsistently.
APIContext identifies the source of issues causing failure, for example gray failures such as creeping performance degradations, network packet loss, flaky I/O, memory thrashing, capacity pressure, data mismatches, and non-fatal exceptions.
Gray failures are difficult to spot because different monitoring and observability tools often see things differently — one tool may pick up an issue on one machine but a tool on another machine may not.
APIContext complements traditional monitoring and observability tools by detecting potential issues in API interactions. For example, an API call may issue a successful HTTP status but the payload may not contain the right data, leading to an error downstream or an application failure.
APIContext deploys agents close to network points of presence to help monitor geo fencing and API latency issues.
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