Synthetic Transaction

Unlike reactive monitoring that waits for real user traffic, synthetic transactions are proactively executed on a scheduled or triggered basis. They run from external vantage points (e.g., cloud regions, edge locations) to simulate the experience of an end-user or autonomous agent connecting from outside the corporate network. This provides an early warning system for issues before they impact real customers.

• Example: A script that runs every 5 minutes from three global AWS regions, simulating an agent logging in and calling a critical CRM API.

Each synthetic transaction is a predefined script that follows a precise sequence of steps. This determinism allows for exact benchmarking of performance and functional correctness against known expected outcomes. The script typically includes:

• Authentication flows (if required).
• Sequential API/tool calls mimicking a business process.
• Assertions to validate response status codes, data schema, payload content, and business logic results.
• Timing measurements for each step and the overall transaction.

These transactions validate both functional correctness and non-functional requirements. Key validated metrics include:

• End-to-End Latency: Total time for the script to complete.
• Step-by-Step Latency: Time for individual tool/API calls (P50, P95, P99).
• Success/Error Rates: Percentage of script executions that pass all assertions.
• Payload Integrity: Verification that returned data matches expected format and values.
• Infrastructure Dependencies: Confirms health of APIs, databases, authentication services, and third-party integrations.

Synthetic transactions are a source of high-fidelity telemetry data. They integrate deeply with observability stacks:

• Trace Generation: Each script execution generates a full distributed trace, with spans for each simulated action.
• Metric Emission: Results are emitted as time-series metrics (e.g., synthetic.duration, synthetic.success).
• Alerting: Failures or SLO breaches (e.g., latency > 2s) trigger alerts for engineering teams.
• Dependency Mapping: Results help build and validate service dependency maps by confirming connectivity and performance between nodes.

For autonomous agents, synthetic transactions are critical for validating the tool-calling layer. Specific use cases include:

• Pre-Deployment Validation: Testing new agent logic or tool integrations in a staging environment that mirrors production.
• Continuous Availability Monitoring: Ensuring all tools an agent depends on (e.g., Stripe API, Slack API, internal microservices) are reachable and responding correctly.
• Performance Regression Detection: Establishing baseline latency for agent tool chains and detecting degradations after deployments.
• Geographic Performance Testing: Simulating agent interactions from different global regions to ensure consistent experience for distributed users.

Synthetic monitoring complements but differs fundamentally from Real User Monitoring (RUM).

Together, they provide a complete picture: synthetics ensure core pathways are always working, while RUM reveals how the system performs under real, variable load.

A method of observing requests as they propagate through a distributed system. For agents, this captures the end-to-end journey of a task, correlating timing and metadata from each internal step and external tool call. Synthetic transactions generate these traces to establish performance baselines and identify bottlenecks.

• Core Component: A Trace is the full record; a Span represents each logical operation (e.g., 'call weather API').
• Synthetic Use: Scripted tests produce canonical traces that serve as a 'golden path' for comparing against real user or agent traffic.

Quantitative measures and targets for service reliability from a user's perspective. For tool calls, key SLIs include:

• Latency (P95, P99)
• Success Rate
• Error Rate

Synthetic transactions are the primary mechanism for measuring these SLIs from outside the production environment, providing an external, unbiased view of availability and performance that aligns with the SLO.

A resilience design pattern that fails fast when calls to a dependent service are likely to fail. It monitors failure rates and, upon breaching a threshold, opens the circuit to stop all requests temporarily.

Synthetic transactions test the circuit breaker's logic by:

• Simulating failure conditions to trigger the open state.
• Validating the half-open state's probe logic.
• Confirming automatic recovery when the synthetic test shows the dependency is healthy again.

A release strategy where a new version of an agent or its tool-calling logic is deployed to a small subset of traffic. Synthetic transactions are run against both the stable (baseline) and canary versions.

Key comparisons include:

• Performance Regression: Detecting latency increases in the canary.
• Functional Correctness: Ensuring new code doesn't break existing tool integrations.
• Error Rate Delta: Identifying new failure modes introduced by the change.

The observability practice of automatically discovering and mapping the external services, APIs, and tools an agent relies on. This is often visualized in a service map or dependency graph.

Synthetic transactions validate this map by:

• Proving each documented dependency is reachable and functional.
• Identifying 'shadow' or undocumented dependencies that are invoked during execution.
• Monitoring for changes in dependency response patterns that could indicate version drift or degradation.

The allowable amount of unreliability, derived from an SLO, that a service can consume over a period (e.g., a month). It quantifies risk.

Synthetic transaction data directly feeds the error budget calculation:

• Each failed synthetic check consumes the budget.
• Persistent high latency measured by synthetics consumes the budget.
• Teams use this burn rate, informed by synthetic monitoring, to decide when to halt feature releases and focus on stability.