In today's fast-paced digital landscape, every second of downtime counts. For businesses relying on complex, interconnected systems, an outage isn't just an inconvenience – it's a direct hit to revenue, reputation, and customer trust. Mean Time To Recovery (MTTR) is a critical metric that defines how quickly your team can resolve an incident and restore services. A low MTTR directly correlates with resilient operations and satisfied users.
So, how can you drastically improve your MTTR, especially when dealing with the increasingly intricate world of Agentic Workflows and Business-as-Code? The answer lies in deep, real-time observability and tracing.
Traditional monitoring tools often provide a high-level view of system health. They might tell you if something is broken, but they rarely tell you why or where the breakdown occurred, particularly within multi-step, intelligent workflows. This lack of granular visibility creates significant blind spots, making incident diagnosis a slow, painstaking process.
Imagine an Agentic Workflow designed to process customer orders. A single order might flow through a payment gateway, inventory management, fulfillment services, and a notification system. If an order fails, where did the failure happen? Was it a payment processing error, an inventory discrepancy, or a communication hiccup with the fulfillment center? Without precise tracing, your team is left guessing, sifting through logs, and trying to reconstruct events – all while your customers wait.
This is where trace.do comes in. trace.do is built specifically to provide deep visibility and observability into your Agentic Workflows, transforming opaque "business-as-code" processes into transparent, actionable insights. By tracing every single transaction and event, it eliminates the guesswork, allowing your team to pinpoint the root cause of issues with unprecedented speed.
Think of it this way: instead of just knowing "an order failed," trace.do shows you the exact step in the workflow where the failure occurred, what the error was, and all associated metadata.
Traditionally, an incident response often looks like this:
With trace.do, steps 3 and 4 are dramatically accelerated:
Instant Context: When an alert fires, trace.do immediately provides the context of the affected Agentic Workflow. You see the full journey of an event, even across distributed services.
Pinpoint Failure: Instead of sifting through thousands of log lines, you can directly see the failed step, its status, duration, and any associated error messages.
Rich Metadata: Each trace includes valuable metadata, giving you crucial details like user IDs, amounts, order IDs, and specific items involved, as shown in our example:
[
{
"timestamp": "2023-10-27T10:00:00Z",
"eventId": "txn_abc123",
"service": "payment-gateway",
"operation": "processPayment",
"status": "success",
"durationMs": 150,
"metadata": {
"userId": "user_xyz789",
"amount": 50.00,
"currency": "USD"
}
},
{
"timestamp": "2023-10-27T10:00:05Z",
"eventId": "order_def456",
"service": "order-fulfillment",
"operation": "createOrder",
"status": "failed",
"durationMs": 220,
"error": "Inventory not available",
"metadata": {
"orderId": "order_def456",
"items": ["itemA", "itemB"]
}
}
]
In this simplified example, if order_def456 fails, trace.do immediately identifies the order-fulfillment service as the culprit and the precise error Inventory not available. No more extensive debugging sessions trying to recreate the scenario or combing through disparate logs.
Proactive Identification: By understanding the normal flow and performance of your workflows, trace.do can help identify anomalies even before they escalate into full-blown incidents, enabling proactive intervention.
By reducing the time spent on identifying, diagnosing, and resolving issues, trace.do directly contributes to a lower MTTR. This translates into:
Gain deep visibility into every step of your Agentic Workflows with trace.do. Monitor, trace, and analyze every transaction and event to ensure your business-as-code processes are robust and resilient.
Observe Business-as-Code with trace.do.
trace.do captures detailed event data and allows you to visualize and analyze the flow and performance of your Agentic Workflows.
trace.do generates structured trace data, often in formats like OpenTelemetry or custom JSON, depending on your integration.
Yes, trace.do is designed to be integrated with other observability tools and platforms through standard APIs and data formats.