Automated Remediation

Example: Code Suggestion Demo

Logsmith identifies production anomalies and suggests code-level fixes by correlating metrics, logs, and distributed traces.

descriptionapi/services/checkout_handler.go

Issue Detected

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func ProcessCheckout(ctx context.Context, items []Item) error {

for _, item := range items {

-err := db.UpdateInventory(ctx, item.ID, item.Quantity)

+err := db.BatchUpdateInventory(ctx, items)

auto_awesome Logsmith: Refactored to avoid N+1 query pattern

if err != nil {

return fmt.Errorf("failed to update: %w", err)

}

return nil

}

Ln 127, Col 24UTF-8 Go

wifi_tetheringLogsmith Agent: Active

analytics

Metrics

Analyze in Grafana

reorder

Distributed Traces

View in SigNoz

psychology

RCA Logic

Reasoning Chain v2.4

Detected Anomaly

Spike in latency (p99 > 2.5s) detected in /checkout endpoint at 14:02 UTC.

Correlation Analysis

Trace ID sz-7712-ax shows 150+ sequential DB calls for a single request. Identified as N+1 query pattern.

Suggested Remediation

Refactor loop-based updates to a batch operation. Expected latency reduction: ~85%.

Confidence Score

94%

Deploy with Confidence

Integrate Logsmith into your CI/CD pipeline to catch performance regressions before they hit production.

rocket_launch

Deep Integrations with your Stack

Logsmith doesn't just guess. It validates suggestions using your existing observability data.

Metrics Correlation

Auto-identifies metric deviations linked to code changes and deployments.

Trace-to-Code

Maps spans directly to specific functions and lines of code in your IDE.

Auto-Patching

Creates PRs with suggested fixes, complete with benchmarked performance gains.