Engineering details

From raw spans to explainable system behavior.

FlowLens turns distributed traces into readable service flow graphs, evidence-backed decision logs, AI-assisted narratives, and cross-trace incident analysis without asking teams to re-instrument their stack.

This page is for architects, engineering leaders, and platform teams who will ask the hard questions: where the data comes from, how the product stays grounded in telemetry, how decisions are evidenced, and how the multi-agent layer fits into the stack.

Code changes required to start reading existing Jaeger traces.

Primary analysis layers: single-trace view, decision evidence, cross-trace incident patterns.

Config-driven model for service labels, roles, decision semantics, and business overlays.

Example trace window shown in incident analysis, demonstrating repeated pattern aggregation.

Main workspace: trace history, case narrative, next action, graph, decision log, and span timeline. Trace-centric UX

Product preview

See the buyer-friendly version of the same product story.

A higher-level product view for buyers, partners, and investors. This version emphasizes product value, positioning, and visible outcomes rather than engineering implementation detail.

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Jaeger · OpenTelemetry · CloudEvents · Config-driven semantics

Why this matters

Most observability tools stop at signal visibility. Engineers still have to do the translation layer by hand.

Raw service names, tags, trace IDs, and reason codes are useful for experts, but weak for cross-functional triage. FlowLens closes that gap by layering deterministic context and controlled AI summaries over the trace model.

Signal → structure

Traces are converted into a service flow graph with lane semantics, call path clarity, and async versus sync separation. That reduces graph ambiguity fast.

Events → evidence

Decision outcomes are surfaced as named log entries with timestamps, evidence handles, and status markers instead of forcing users to inspect raw tags manually.

Patterns → action

Cross-trace analysis moves teams beyond single-case debugging. The product can highlight repeated failure reasons and likely bottlenecks across a window.

Core capability stack

What gives this product engineering credibility.

The strength is not one screenshot. It is the combination of trace fidelity, clear semantics, evidence surfaces, and configurable overlays that remain grounded in the underlying telemetry.

Trace-native foundationReads from Jaeger rather than inventing a parallel source of truth. The trace remains the system record.

Config-driven semanticsDisplay names, business roles, decision labels, and SLA thresholds are controlled through configuration, not hard-coded per domain.

Evidence-backed decision logDecision states are surfaced with timestamps, reason codes, and evidence pointers so a reviewer can follow the chain.

Cross-trace pattern analysisSingle trace debugging is necessary but insufficient. Pattern aggregation exposes recurring failure modes and likely bottlenecks.

Grounded AI usageLLMs narrate and summarize. They do not replace the service decisioning layer. That keeps the AI role auditable and bounded.

Explainable UI surfacesEvery major panel answers a real engineering or operations question instead of acting as decoration.

Evidence surfaces

Three views that make the system legible.

Decision log — What evidence supports this decision?Evidence details

Span timeline — Where is time being spent?Span duration distribution

Business process view — Which business step passed or failed?Process mapping

Incident pattern analysis — What repeated issue is emerging?Cross-trace summary

Architecture

Multi-agent architecture integrated into the product story.

The architecture below shows the stack end to end: banking event source, FlowLens observability platform, enrichment layer, specialist agents, and business-facing output. This is not bolted on after the fact; it is the operating model for how telemetry becomes explainable business insight.