TraceRoot.AI is an AI-native observability platform that helps developers fix production bugs faster by analyzing structured logs and traces. It offers SDK integration, AI agents for root cause analysis, and a platform for comprehensive visualizations.
TraceRoot accelerates the debugging process with AI-powered insights. It integrates seamlessly into your development workflow, providing real-time trace and log analysis, code context understanding, and intelligent assistance. It offers both a cloud and self-hosted version, with SDKs available for Python and JavaScript/TypeScript.
The article discusses the emergence of 'agentic traffic' – outbound API calls made by autonomous AI agents – and the need for a new infrastructure layer, an 'AI Gateway', to govern and secure this traffic. It outlines the components of an AI Gateway and the importance of security, compliance, and observability in managing agentic AI.
The company's transition from fragmented observability tools to a unified system using OpenTelemetry and OneUptime dramatically improved incident response times, reducing MTTR from 41 to 9 minutes. By correlating logs, metrics, and traces through structured logging and intelligent sampling, they eliminated much of the noise and confusion that previously slowed root cause analysis. The shift also reduced the number of dashboards engineers needed to check per incident and significantly lowered the percentage of incidents with unknown causes.
Key practices included instrumenting once with OpenTelemetry, enforcing cardinality limits, and archiving raw data for future analysis. The move away from 100% trace capture and over-instrumentation helped manage data volume while maintaining visibility into anomalies. This transformation emphasized that effective observability isn't about collecting more data, but about designing correlated signals that support intentional diagnosis and reduce cognitive load.
Key practices included instrumenting once with OpenTelemetry, enforcing cardinality limits, and archiving raw data for future analysis. The move away from 100% trace capture and over-instrumentation helped manage data volume while maintaining visibility into anomalies. This transformation emphasized that effective observability isn't about collecting more data, but about designing correlated signals that support intentional diagnosis and reduce cognitive load.
AI is revolutionizing Infrastructure as Code (IaC), enhancing speed, intelligence, and responsiveness. However, human expertise remains crucial for understanding AI-generated outputs and ensuring proper system functionality.
Sam Newman discusses the three golden rules of distributed computing and how they necessitate robust handling of timeouts, retries, and idempotency. He provides practical, data-driven strategies for implementing these principles, including using request IDs and server-side fingerprinting to create safe, resilient distributed systems.
**Experiment Goal:** Determine if LLMs can autonomously perform root cause analysis (RCA) on live application
Five LLMs were given access to OpenTelemetry data from a demo application,:
* They were prompted with a naive instruction: "Identify the issue, root cause, and suggest solutions."
* Four distinct anomalies were used, each with a known root cause established through manual investigation.
* Performance was measured by: accuracy, guidance required, token usage, and investigation time.
* Models: Claude Sonnet 4, OpenAI GPT-o3, OpenAI GPT-4.1, Gemini 2.5 Pro
* **Autonomous RCA is not yet reliable.** The LLMs generally fell short of replacing SREs. Even GPT-5 (not explicitly tested, but implied as a benchmark) wouldn't outperform the others.
* **LLMs are useful as assistants.** They can help summarize findings, draft updates, and suggest next steps.
* **A fast, searchable observability stack (like ClickStack) is crucial.** LLMs need access to good data to be effective.
* **Models varied in performance:**
* Claude Sonnet 4 and OpenAI o3 were the most successful, often identifying the root cause with minimal guidance.
* GPT-4.1 and Gemini 2.5 Pro required more prompting and struggled to query data independently.
* **Models can get stuck in reasoning loops.** They may focus on one aspect of the problem and miss other important clues.
* **Token usage and cost varied significantly.**
**Specific Anomaly Results (briefly):**
* **Anomaly 1 (Payment Failure):** Claude Sonnet 4 and OpenAI o3 solved it on the first prompt. GPT-4.1 and Gemini 2.5 Pro needed guidance.
* **Anomaly 2 (Recommendation Cache Leak):** Claude Sonnet 4 identified the service restart issue but missed the cache problem initially. OpenAI o3 identified the memory leak. GPT-4.1 and Gemini 2.5 Pro struggled.
Five LLMs were given access to OpenTelemetry data from a demo application,:
* They were prompted with a naive instruction: "Identify the issue, root cause, and suggest solutions."
* Four distinct anomalies were used, each with a known root cause established through manual investigation.
* Performance was measured by: accuracy, guidance required, token usage, and investigation time.
* Models: Claude Sonnet 4, OpenAI GPT-o3, OpenAI GPT-4.1, Gemini 2.5 Pro
* **Autonomous RCA is not yet reliable.** The LLMs generally fell short of replacing SREs. Even GPT-5 (not explicitly tested, but implied as a benchmark) wouldn't outperform the others.
* **LLMs are useful as assistants.** They can help summarize findings, draft updates, and suggest next steps.
* **A fast, searchable observability stack (like ClickStack) is crucial.** LLMs need access to good data to be effective.
* **Models varied in performance:**
* Claude Sonnet 4 and OpenAI o3 were the most successful, often identifying the root cause with minimal guidance.
* GPT-4.1 and Gemini 2.5 Pro required more prompting and struggled to query data independently.
* **Models can get stuck in reasoning loops.** They may focus on one aspect of the problem and miss other important clues.
* **Token usage and cost varied significantly.**
**Specific Anomaly Results (briefly):**
* **Anomaly 1 (Payment Failure):** Claude Sonnet 4 and OpenAI o3 solved it on the first prompt. GPT-4.1 and Gemini 2.5 Pro needed guidance.
* **Anomaly 2 (Recommendation Cache Leak):** Claude Sonnet 4 identified the service restart issue but missed the cache problem initially. OpenAI o3 identified the memory leak. GPT-4.1 and Gemini 2.5 Pro struggled.
The Azure MCP Server implements the MCP specification to create a seamless connection between AI agents and Azure services. It allows agents to interact with various Azure services like AI Search, App Configuration, Cosmos DB, and more.
Real-time observability and analytics platform for local LLMs, with dashboard and API.
The article discusses how agentic LLMs can help users overcome the learning curve of the command line interface (CLI) by automating tasks and providing guidance. It explores tools like ShellGPT and Auto-GPT that leverage LLMs to interpret natural language instructions and execute corresponding CLI commands. The author argues that this approach can make the CLI more accessible and powerful, even for those unfamiliar with its intricacies.
