# tracing

Published articles for tracing.

This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles.

## Ensuring transaction-safety in Google App Engine

DevFeed: [Ensuring transaction-safety in Google App Engine](<https://devfeed.tech/articles/ensuring-transaction-safety-in-google-app-engine-27414.md>)

Original publisher: [Read original article](<http://engineering.khanacademy.org/posts/transaction-safety.htm>)

Author: Khan Academy

Published: 2016-06-27T22:00:00Z

Content type: article

Language: en

Sources: [Khan Academy](<https://devfeed.tech/sources/khan-academy.md>)

Topics: [Transactions](<https://devfeed.tech/topics/transactions.md>), [Databases](<https://devfeed.tech/topics/databases.md>), [Code](<https://devfeed.tech/topics/code.md>), [Google](<https://devfeed.tech/topics/google.md>)

Tags: [cache](<https://devfeed.tech/tags/cache.md>), [code](<https://devfeed.tech/tags/code.md>), [database](<https://devfeed.tech/tags/database.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [google](<https://devfeed.tech/tags/google.md>), [hooks](<https://devfeed.tech/tags/hooks.md>), [infrastructure](<https://devfeed.tech/tags/infrastructure.md>), [news](<https://devfeed.tech/tags/news.md>), [tracing](<https://devfeed.tech/tags/tracing.md>), [transactions](<https://devfeed.tech/tags/transactions.md>)

### AI overview

The article explains how Khan Academy uses transactions and user-write locks with Google App Engine to prevent database corruption caused by concurrent requests overwriting one another. It describes hooks that detect transaction-safety violations and highlights caching and code-path mistakes that can cause stale data to be used inside transactions.

### Source excerpt

By Craig Silverstein In last week's exciting post, I described an alternative to transactions that we use at ... Read more

## Atlassian Automates Root Cause Analysis by Correlating Metrics, Logs and Traces

DevFeed: [Atlassian Automates Root Cause Analysis by Correlating Metrics, Logs and Traces](<https://devfeed.tech/articles/atlassian-automates-root-cause-analysis-by-correlating-metrics-logs-and-traces-26599.md>)

Original publisher: [Read original article](<https://www.infoq.com/news/2026/09/atlassian-automated-rca/>)

Author: Craig Risi

Published: 2026-09-15T12:00:00Z

Content type: news

Language: en

Sources: [InfoQ](<https://devfeed.tech/sources/infoq.md>)

Topics: [incident](<https://devfeed.tech/topics/incident.md>), [atlassian](<https://devfeed.tech/topics/atlassian.md>), [telemetry](<https://devfeed.tech/topics/telemetry.md>), [SIEM, Security, Observability](<https://devfeed.tech/topics/siem-security-observability.md>), [tracing](<https://devfeed.tech/topics/tracing.md>), [Cloud Native Ecosystem](<https://devfeed.tech/topics/cloud-native-ecosystem.md>), [OpenTelemetry](<https://devfeed.tech/topics/opentelemetry.md>)

Tags: [atlassian](<https://devfeed.tech/tags/atlassian.md>), [atlassian-automated-rca](<https://devfeed.tech/tags/atlassian-automated-rca.md>), [cloud-native](<https://devfeed.tech/tags/cloud-native.md>), [defects](<https://devfeed.tech/tags/defects.md>), [devops](<https://devfeed.tech/tags/devops.md>), [distributed](<https://devfeed.tech/tags/distributed.md>), [incident](<https://devfeed.tech/tags/incident.md>), [incident-response](<https://devfeed.tech/tags/incident-response.md>), [logging](<https://devfeed.tech/tags/logging.md>), [logs](<https://devfeed.tech/tags/logs.md>), [metrics](<https://devfeed.tech/tags/metrics.md>), [news](<https://devfeed.tech/tags/news.md>), [opentelemetry](<https://devfeed.tech/tags/opentelemetry.md>), [root-cause-analysis](<https://devfeed.tech/tags/root-cause-analysis.md>), [services](<https://devfeed.tech/tags/services.md>), [telemetry](<https://devfeed.tech/tags/telemetry.md>), [traces](<https://devfeed.tech/tags/traces.md>), [tracing](<https://devfeed.tech/tags/tracing.md>)

### AI overview

Atlassian has outlined an approach to automating root cause analysis for large-scale cloud-native incidents. It correlates metrics, logs, distributed traces, and service topology to detect anomalies, align them in time, trace dependencies, and produce ranked hypotheses about likely fault origins and propagation paths.

### Source excerpt

Atlassian has outlined a new approach to automating root cause analysis for large-scale cloud-native incidents, using correlation across metrics, logs, distributed traces, and service topology to generate ranked hypotheses about where failures originate and how they propagate. By Craig Risi

## What It Takes to Build a Production Agent Harness

DevFeed: [What It Takes to Build a Production Agent Harness](<https://devfeed.tech/articles/what-it-takes-to-build-a-production-agent-harness-18246.md>)

Original publisher: [Read original article](<https://blog.dailydoseofds.com/p/what-it-takes-to-build-a-production>)

Author: Avi Chawla

Published: 2026-09-14T19:50:37Z

Content type: article

Language: en

Sources: [Daily Dose of Data Science](<https://devfeed.tech/sources/daily-dose-of-data-science.md>)

Topics: [Agent Harness](<https://devfeed.tech/topics/agent-harness.md>), [LangChain](<https://devfeed.tech/topics/langchain.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Persistence](<https://devfeed.tech/topics/persistence.md>), [Graphs](<https://devfeed.tech/topics/graphs.md>)

Tags: [agent](<https://devfeed.tech/tags/agent.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-engineer](<https://devfeed.tech/tags/ai-engineer.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [langchain](<https://devfeed.tech/tags/langchain.md>), [langgraph](<https://devfeed.tech/tags/langgraph.md>), [memory](<https://devfeed.tech/tags/memory.md>), [persistence](<https://devfeed.tech/tags/persistence.md>), [tools](<https://devfeed.tech/tags/tools.md>), [tracing](<https://devfeed.tech/tags/tracing.md>)

### AI overview

A hands-on series chapter explains how to build a production agent harness with LangChain and LangGraph. It covers model, message, prompt, and tool interactions; tool-call execution; state transitions; persistence; failure handling; tracing; evaluation; human approval; and resumable execution.

### Source excerpt

A hands-on nanodegree for production agent engineering.

## Monitoring production agent lifecycle with AWS DevOps Agent and AgentCore Evaluations

DevFeed: [Monitoring production agent lifecycle with AWS DevOps Agent and AgentCore Evaluations](<https://devfeed.tech/articles/monitoring-production-agent-lifecycle-with-aws-devops-agent-and-agentcore-evaluations-4737.md>)

Original publisher: [Read original article](<https://aws.amazon.com/blogs/machine-learning/monitoring-production-agent-lifecycle-with-aws-devops-agent-and-agentcore-evaluations/>)

Author: Meghana Ashok

Published: 2026-09-11T18:26:38Z

Content type: article

Language: en

Sources: [Artificial Intelligence](<https://devfeed.tech/sources/artificial-intelligence.md>)

Topics: [ai observability](<https://devfeed.tech/topics/ai-observability.md>), [AWS IAM](<https://devfeed.tech/topics/aws-iam.md>), [incident](<https://devfeed.tech/topics/incident.md>), [Orchestration](<https://devfeed.tech/topics/orchestration.md>), [telemetry](<https://devfeed.tech/topics/telemetry.md>)

Tags: [advanced-300](<https://devfeed.tech/tags/advanced-300.md>), [agent](<https://devfeed.tech/tags/agent.md>), [agents](<https://devfeed.tech/tags/agents.md>), [amazon-bedrock](<https://devfeed.tech/tags/amazon-bedrock.md>), [amazon-bedrock-agentcore](<https://devfeed.tech/tags/amazon-bedrock-agentcore.md>), [aws](<https://devfeed.tech/tags/aws.md>), [aws-identity-and-access-management-iam](<https://devfeed.tech/tags/aws-identity-and-access-management-iam.md>), [best-practices](<https://devfeed.tech/tags/best-practices.md>), [devops](<https://devfeed.tech/tags/devops.md>), [incident](<https://devfeed.tech/tags/incident.md>), [infrastructure-monitoring](<https://devfeed.tech/tags/infrastructure-monitoring.md>), [logs](<https://devfeed.tech/tags/logs.md>), [metrics](<https://devfeed.tech/tags/metrics.md>), [monitoring](<https://devfeed.tech/tags/monitoring.md>), [orchestration](<https://devfeed.tech/tags/orchestration.md>), [production](<https://devfeed.tech/tags/production.md>), [tracing](<https://devfeed.tech/tags/tracing.md>)

### AI overview

The article describes monitoring production multi-agent systems with Amazon Bedrock AgentCore Evaluations for continuous quality assessment and AWS DevOps Agent for autonomous infrastructure incident investigation.

### Source excerpt

Multi-agent systems fail in ways traditional monitoring misses. This post presents a dual-layer approach to monitoring production agents: Amazon Bedrock AgentCore Evaluations for continuous quality scoring and AWS DevOps Agent for autonomous infrastructure investigation, shown on a four-agent airline reservation system.

## Wrapture: a Python package for monkey patching, testing, and observability

DevFeed: [Wrapture: a Python package for monkey patching, testing, and observability](<https://devfeed.tech/articles/don-t-sleep-on-wrapture-31170.md>)

Original publisher: [Read original article](<https://simonwillison.net/2026/Sep/11/wrapture/>)

Author: Simon Willison

Published: 2026-09-11T13:51:32Z

Content type: opinion

Language: en

Sources: [Simon Willison's Weblog](<https://devfeed.tech/sources/simon-willison-s-weblog.md>)

Topics: [Python](<https://devfeed.tech/topics/python.md>), [Testing](<https://devfeed.tech/topics/testing.md>), [observability](<https://devfeed.tech/topics/observability.md>), [Instrumentation](<https://devfeed.tech/topics/instrumentation.md>), [tracing](<https://devfeed.tech/topics/tracing.md>), [OpenTelemetry](<https://devfeed.tech/topics/opentelemetry.md>), [Flask](<https://devfeed.tech/topics/flask.md>), [jupyterlab](<https://devfeed.tech/topics/jupyterlab.md>)

Tags: [code](<https://devfeed.tech/tags/code.md>), [graham-dumpleton](<https://devfeed.tech/tags/graham-dumpleton.md>), [graham-dumpleton-6](<https://devfeed.tech/tags/graham-dumpleton-6.md>), [how-to](<https://devfeed.tech/tags/how-to.md>), [instrumentation](<https://devfeed.tech/tags/instrumentation.md>), [monkey-patching](<https://devfeed.tech/tags/monkey-patching.md>), [monkey-patching-10](<https://devfeed.tech/tags/monkey-patching-10.md>), [observability](<https://devfeed.tech/tags/observability.md>), [observability-10](<https://devfeed.tech/tags/observability-10.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [open-source-320](<https://devfeed.tech/tags/open-source-320.md>), [opentelemetry](<https://devfeed.tech/tags/opentelemetry.md>), [python](<https://devfeed.tech/tags/python.md>), [python-1-283](<https://devfeed.tech/tags/python-1-283.md>), [testing](<https://devfeed.tech/tags/testing.md>), [testing-95](<https://devfeed.tech/tags/testing-95.md>), [tracing](<https://devfeed.tech/tags/tracing.md>)

### AI overview

This article reviews wrapture, an alpha Python package by Graham Dumpleton for monkey patching, unit testing, call recording, live and zero-code tracing, timing analysis, and OpenTelemetry export. It highlights tutorials and JupyterLab workshops covering the package and related instrumentation.

### Source excerpt

Graham Dumpleton's new monkey patching package wrapture is shaping up to be an indispensable tool for Python developers. I'm not sure why I've seen so little buzz about it! Graham has been posting new tutorials for it almost daily since the initial release on August 31st. Here's everything he's published so far: Introducing wrapture - a new monkey patching library that serves both testing and observability (think New Relic style tracing) at the same time. Unit testing with wrapture - how to use it for the same kinds of thing as unittest.mock. Recording calls with wrapture - recording method calls as timelines and processing and displaying them as trees. Phased behaviour in wrapture - arranging patched methods to change behavior across multiple calls. Beyond callables in wrapture - monkey patching attributes, dictionaries, generators. Live tracing with wrapture - tracing a live application to see exactly how it works. Zero-code tracing with wrapture - configuring tracing in a separate TOML file without modifying Python code at all. Tracing Flask with wrapture - using the separate wrapture-instrumenation package to instrument a Flask application. That package also provides instrumentation for aiohttp.client, aiohttp.web, django, fastapi, flask, grpc, http.client, httpx, jinja2, requests, sqlalchemy, sqlite3, starlette, urllib.request, urllib3, uvicorn, werkzeug.serving, wsgiref.simple_server, xmlrpc.client, xmlrpc.server. Finding slow code with wrapture - wrapture's tools for recording timing information, both individually and aggregated across multiple calls. OpenTelemetry export in wrapture - exporting traces to OpenTelemetry. Graham also has a set of interactive workshops for wrapture, implemented as JupyterLab notebooks. Wrapture is still alpha software but it's already very usable - especially given you can configure and try it out with a TOML file without modifying any Python code at all. This feels like one of those Swiss Army Knife packages that, once mastered

## OpenTelemetry proposes environment variables for context propagation across processes

DevFeed: [OpenTelemetry proposes environment variables for context propagation across processes](<https://devfeed.tech/articles/help-us-stabilize-environment-variable-context-propagation-32571.md>)

Original publisher: [Read original article](<https://opentelemetry.io/blog/2026/environment-variable-context-propagation/>)

Author: OpenTelemetry Authors; Docs CC BY

Published: 2026-09-11T11:01:22Z

Content type: article

Language: en

Sources: [Blog on OpenTelemetry](<https://devfeed.tech/sources/blog-on-opentelemetry.md>)

Topics: [context](<https://devfeed.tech/topics/context.md>), [tracing](<https://devfeed.tech/topics/tracing.md>), [OpenTelemetry](<https://devfeed.tech/topics/opentelemetry.md>), [Processes](<https://devfeed.tech/topics/processes.md>), [CI/CD](<https://devfeed.tech/topics/cicd.md>), [data-processing](<https://devfeed.tech/topics/data-processing.md>)

Tags: [ci-cd](<https://devfeed.tech/tags/ci-cd.md>), [data-processing](<https://devfeed.tech/tags/data-processing.md>), [environment-variables](<https://devfeed.tech/tags/environment-variables.md>), [opentelemetry](<https://devfeed.tech/tags/opentelemetry.md>), [processes](<https://devfeed.tech/tags/processes.md>), [spans](<https://devfeed.tech/tags/spans.md>), [tracing](<https://devfeed.tech/tags/tracing.md>), [w3c](<https://devfeed.tech/tags/w3c.md>)

### AI overview

The OpenTelemetry Specification has a release candidate for using environment variables to carry trace context and baggage between processes. The article explains how this can connect spans across workflow runners, shells, build tools, test processes, and similar workloads when protocol headers or message metadata are unavailable, and requests feedback before the specification becomes Stable.

### Source excerpt

A trace does not always cross a network boundary. A workflow runner starts a shell, the shell launches a build tool, and the build tool starts test processes. Batch and data-processing systems create similar chains of child processes. Without a shared way to pass trace information across these boundaries, spans from each process can end up in separate traces. If context propagation is new to you, it is the mechanism that carries information from one service or process to the next. For tracing, this includes the trace and span identifiers that let new spans join the same trace. It can also carry baggage: application-defined key-value pairs that are passed to downstream work.

## Debugging our AI search assistant with agent tracing

DevFeed: [Debugging our AI search assistant with agent tracing](<https://devfeed.tech/articles/debugging-our-ai-search-assistant-with-agent-tracing-24095.md>)

Original publisher: [Read original article](<https://blog.sentry.io/debugging-our-ai-search-assistant-with-agent-tracing/>)

Author: Dominik Buszowiecki; Shaun Kaasten

Published: 2026-09-11T09:00:00Z

Content type: article

Language: en

Sources: [Sentry Blog](<https://devfeed.tech/sources/sentry-blog.md>)

Topics: [AI search](<https://devfeed.tech/topics/ai-search.md>), [agent observability](<https://devfeed.tech/topics/agent-observability.md>), [debugging](<https://devfeed.tech/topics/debugging.md>), [tracing](<https://devfeed.tech/topics/tracing.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>)

Tags: [agent](<https://devfeed.tech/tags/agent.md>), [ai](<https://devfeed.tech/tags/ai.md>), [api](<https://devfeed.tech/tags/api.md>), [debugging](<https://devfeed.tech/tags/debugging.md>), [errors](<https://devfeed.tech/tags/errors.md>), [eval](<https://devfeed.tech/tags/eval.md>), [llm](<https://devfeed.tech/tags/llm.md>), [search](<https://devfeed.tech/tags/search.md>), [sentry](<https://devfeed.tech/tags/sentry.md>), [tracing](<https://devfeed.tech/tags/tracing.md>)

### AI overview

Sentry engineers describe how they debugged the Search Query Assistant, which converts natural-language prompts into Sentry Syntax queries. They used evals for performance measurement and AI Conversation tracing to investigate failures, including a bug involving custom numerical attributes that caused queries to return no results.

### Source excerpt

See how Sentry engineers used AI Conversations to debug a natural language search assistant and fix a tricky query generation bug.

## How Heurist Finance built an AI-native investment workbench on Amazon Bedrock AgentCore

DevFeed: [How Heurist Finance built an AI-native investment workbench on Amazon Bedrock AgentCore](<https://devfeed.tech/articles/how-heurist-finance-built-an-ai-native-investment-workbench-on-amazon-bedrock-agentcore-4734.md>)

Original publisher: [Read original article](<https://aws.amazon.com/blogs/machine-learning/how-heurist-finance-built-an-ai-native-investment-workbench-on-amazon-bedrock-agentcore/>)

Author: JW Wang

Published: 2026-09-09T18:11:12Z

Content type: article

Language: en

Sources: [Artificial Intelligence](<https://devfeed.tech/sources/artificial-intelligence.md>)

Topics: [Amazon Bedrock](<https://devfeed.tech/topics/amazon-bedrock.md>), [Claude](<https://devfeed.tech/topics/claude.md>), [Orchestration](<https://devfeed.tech/topics/orchestration.md>), [tracing](<https://devfeed.tech/topics/tracing.md>)

Tags: [advanced-300](<https://devfeed.tech/tags/advanced-300.md>), [agent](<https://devfeed.tech/tags/agent.md>), [ai](<https://devfeed.tech/tags/ai.md>), [amazon-bedrock-agentcore](<https://devfeed.tech/tags/amazon-bedrock-agentcore.md>), [anthropic-claude](<https://devfeed.tech/tags/anthropic-claude.md>), [customer-solutions](<https://devfeed.tech/tags/customer-solutions.md>), [identity](<https://devfeed.tech/tags/identity.md>), [memory](<https://devfeed.tech/tags/memory.md>), [observability](<https://devfeed.tech/tags/observability.md>), [sandbox](<https://devfeed.tech/tags/sandbox.md>), [tracing](<https://devfeed.tech/tags/tracing.md>)

### AI overview

Heurist Finance uses Amazon Bedrock AgentCore to run a conversational investment workbench that accesses premium data per query and produces auditable, personalized research responses.

### Source excerpt

Learn how Heurist built Heurist Finance, a conversational AI investment workbench, on Amazon Bedrock AgentCore. This customer story shows how AgentCore payments, Identity, Memory, Code Interpreter, and Observability let a small team buy premium market data per query, isolate analysis in a sandbox, and keep every action auditable.

## September 09, 2026

DevFeed: [September 09, 2026](<https://devfeed.tech/articles/september-09-2026-22736.md>)

Original publisher: [Read original article](<https://developer.android.com/jetpack/androidx/versions/all-channel>)

Published: 2026-09-09T17:00:00Z

Content type: release

Language: en

Sources: [AndroidX Release Notes](<https://devfeed.tech/sources/androidx-release-notes.md>)

Topics: [Jetpack](<https://devfeed.tech/topics/jetpack.md>), [releases](<https://devfeed.tech/topics/releases.md>), [Release notes](<https://devfeed.tech/topics/release-notes.md>), [Compose](<https://devfeed.tech/topics/compose.md>), [Library](<https://devfeed.tech/topics/library.md>)

Tags: [benchmark](<https://devfeed.tech/tags/benchmark.md>), [compose](<https://devfeed.tech/tags/compose.md>), [jetpack](<https://devfeed.tech/tags/jetpack.md>), [latest-release](<https://devfeed.tech/tags/latest-release.md>), [libraries](<https://devfeed.tech/tags/libraries.md>), [lifecycle](<https://devfeed.tech/tags/lifecycle.md>), [navigation](<https://devfeed.tech/tags/navigation.md>), [release-notes](<https://devfeed.tech/tags/release-notes.md>), [room](<https://devfeed.tech/tags/room.md>), [security](<https://devfeed.tech/tags/security.md>), [sqlite](<https://devfeed.tech/tags/sqlite.md>), [tracing](<https://devfeed.tech/tags/tracing.md>), [ui](<https://devfeed.tech/tags/ui.md>)

### AI overview

This Android developer page lists AndroidX library components across four release channels and provides recent release dates and version information. It also links to component release histories, archived release notes, and the current versions page.

### Source excerpt

Activity Version 1.14.0-alpha02 Annotation Version 1.11.0-rc01 Benchmark Version 1.5.0 Car App Version 1.9.0-alpha02 Collection Version 1.7.0-alpha01 Compose Animation Version 1.12.1 Compose Animation Version 1.13.0-alpha03 Compose Foundation Version 1.12.1 Compose Foundation Version 1.13.0-alpha03 Compose Material Version 1.12.1 Compose Material Version 1.13.0-alpha03 Compose Material3 Version 1.5.0-alpha28 Compose Material3 Adaptive Version 1.4.0-alpha02 Compose Remote Version 1.0.0-alpha19 Compose Remote Foundation Version 1.0.0-alpha02 Compose Runtime Version 1.12.1 Compose Runtime Version 1.13.0-alpha03 Compose Ui Version 1.12.1 Compose Ui Version 1.13.0-alpha03 Core Uwb Version 1.1.0-alpha02 Credentials Providerevents Version 1.0.0-beta01 Datastore Version 1.3.0-alpha11 Emoji2 Version 1.7.0-rc01 Glance Adaptive Version 1.0.0-alpha02 Glance Wear Version 1.0.0-alpha18 Heifwriter Version 1.2.0-alpha02 Ink Version 1.1.0-alpha08 Lifecycle Version 2.12.0-alpha03 Mediarouter Version 1.9.0-alpha02 Navigation Version 2.10.1 Navigation3 Version 1.2.0-rc01 Room Version 2.8.5 Room3 Version 3.0.3 Room3 Version 3.1.0-alpha01 Savedstate Version 1.6.0-alpha03 Security Version 1.0.0 Security Version 1.1.0 Sqlite Version 2.7.1 Sqlite Version 2.8.0-alpha01 Tracing Version 2.0.2 Wear Compose Version 1.7.0-rc01 Wear Compose Remote Version 1.0.0-alpha11 Webkit Version 1.18.0-alpha01 Xr Arcore Version 1.0.0-rc01 Xr Glimmer Version 1.0.0-alpha19 Xr Projected Version 1.0.0-alpha12 Xr Runtime Version 1.0.0-rc01 Xr Runtime Version 1.1.0-alpha01 Xr Scenecore Version 1.0.0-rc01

## Distributed tracing for CI pipelines without touching a single workflow file

DevFeed: [Distributed tracing for CI pipelines without touching a single workflow file](<https://devfeed.tech/articles/distributed-tracing-for-ci-pipelines-without-touching-a-single-workflow-file-4598.md>)

Original publisher: [Read original article](<https://www.cncf.io/blog/2026/09/08/distributed-tracing-for-ci-pipelines-without-touching-a-single-workflow-file/>)

Author: George Sims, downtherabbithole.dev

Published: 2026-09-08T11:00:00Z

Content type: article

Language: en

Sources: [Cloud Native Computing Foundation](<https://devfeed.tech/sources/cloud-native-computing-foundation.md>)

Topics: [ci](<https://devfeed.tech/topics/ci.md>)

Tags: [blog](<https://devfeed.tech/tags/blog.md>), [ci](<https://devfeed.tech/tags/ci.md>), [github](<https://devfeed.tech/tags/github.md>), [github-actions](<https://devfeed.tech/tags/github-actions.md>), [instrumentation](<https://devfeed.tech/tags/instrumentation.md>), [jobs](<https://devfeed.tech/tags/jobs.md>), [opentelemetry](<https://devfeed.tech/tags/opentelemetry.md>), [tracing](<https://devfeed.tech/tags/tracing.md>), [workflow](<https://devfeed.tech/tags/workflow.md>), [workflows](<https://devfeed.tech/tags/workflows.md>)

### AI overview

The article describes collecting GitHub Actions events at the organization level and converting them into OpenTelemetry spans to trace CI workflows without editing individual workflow files.

### Source excerpt

You've probably felt this one: GitHub Actions usage creeps up across your org, and your actual visibility into it doesn't keep pace. Which workflows are slow? Which are flaky? How long are jobs sitting queued for...

## Measuring real-time performance per dollar under continuous load: CostBench's first end-to-end results

DevFeed: [Measuring real-time performance per dollar under continuous load: CostBench's first end-to-end results](<https://devfeed.tech/articles/measuring-real-time-performance-per-dollar-under-continuous-load-costbench-s-first-end-to-end-results-5218.md>)

Original publisher: [Read original article](<https://clickhouse.com/blog/costbench-real-time-performance-per-dollar>)

Author: Tom Schreiber; Lionel Palacin

Published: 2026-09-08T00:00:00Z

Content type: article

Language: en

Sources: [ClickHouse Blog](<https://devfeed.tech/sources/clickhouse-blog.md>)

Topics: [AI, ML & Data Engineering](<https://devfeed.tech/topics/ai-ml-data-engineering.md>), [Amazon Redshift](<https://devfeed.tech/topics/amazon-redshift.md>)

Tags: [analytics](<https://devfeed.tech/tags/analytics.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [bigquery](<https://devfeed.tech/tags/bigquery.md>), [clickhouse](<https://devfeed.tech/tags/clickhouse.md>), [cloud](<https://devfeed.tech/tags/cloud.md>), [cost](<https://devfeed.tech/tags/cost.md>), [data](<https://devfeed.tech/tags/data.md>), [performance](<https://devfeed.tech/tags/performance.md>), [real-time](<https://devfeed.tech/tags/real-time.md>), [serverless](<https://devfeed.tech/tags/serverless.md>), [streaming](<https://devfeed.tech/tags/streaming.md>), [systems](<https://devfeed.tech/tags/systems.md>), [tracing](<https://devfeed.tech/tags/tracing.md>), [work](<https://devfeed.tech/tags/work.md>)

### AI overview

CostBench benchmarks the cost and performance of real-time cloud data warehouses under continuous ingestion and query load. It compares ClickHouse Cloud with Snowflake, BigQuery, and Redshift Serverless, reporting better end-to-end performance per dollar for ClickHouse Cloud in the tested workload.

### Source excerpt

CostBench puts cloud data warehouses under continuous load. Across the complete path from fresh data to fast answers, ClickHouse Cloud delivers 412-1,996x better performance per dollar.

## Making Rust observability reliable at scale with OpenTelemetry

DevFeed: [Making Rust observability reliable at scale with OpenTelemetry](<https://devfeed.tech/articles/making-rust-observability-reliable-at-scale-with-opentelemetry-2273.md>)

Original publisher: [Read original article](<https://www.datadoghq.com/blog/engineering/rust-tracing-opentelemetry/>)

Author: Björn Antonsson; Paul Le Grand des Cloizeaux; Scott Gerring

Published: 2026-09-01T00:00:00Z

Content type: article

Language: en

Sources: [Datadog | The Monitor blog](<https://devfeed.tech/sources/datadog-the-monitor-blog.md>)

Topics: [tracing](<https://devfeed.tech/topics/tracing.md>), [telemetry](<https://devfeed.tech/topics/telemetry.md>), [Traces](<https://devfeed.tech/topics/traces.md>), [Instrumentation](<https://devfeed.tech/topics/instrumentation.md>)

Tags: [apm](<https://devfeed.tech/tags/apm.md>), [instrumentation](<https://devfeed.tech/tags/instrumentation.md>), [latency](<https://devfeed.tech/tags/latency.md>), [observability](<https://devfeed.tech/tags/observability.md>), [opentelemetry](<https://devfeed.tech/tags/opentelemetry.md>), [production](<https://devfeed.tech/tags/production.md>), [rust](<https://devfeed.tech/tags/rust.md>), [scale](<https://devfeed.tech/tags/scale.md>), [traces](<https://devfeed.tech/tags/traces.md>), [tracing](<https://devfeed.tech/tags/tracing.md>)

### AI overview

Datadog describes building an opinionated Rust tracer on OpenTelemetry to improve trace propagation, sampling consistency, and trace quality in production services.

### Source excerpt

Learn how Datadog improved Rust tracing by building an opinionated OpenTelemetry-based library to help ensure consistent sampling, propagation, and trace quality at scale.

## What we learned about AI agent security by monitoring our agents

DevFeed: [What we learned about AI agent security by monitoring our agents](<https://devfeed.tech/articles/what-we-learned-about-ai-agent-security-by-monitoring-our-agents-2225.md>)

Original publisher: [Read original article](<https://www.datadoghq.com/blog/ai-agent-security-lessons/>)

Author: Alexa Levine; Emmanuelle Lejeail; Mallory Mooney

Published: 2026-08-27T00:00:00Z

Content type: article

Language: en

Sources: [Datadog | The Monitor blog](<https://devfeed.tech/sources/datadog-the-monitor-blog.md>)

Topics: [AI Bots](<https://devfeed.tech/topics/ai-bots.md>)

Tags: [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [ai-security](<https://devfeed.tech/tags/ai-security.md>), [identity](<https://devfeed.tech/tags/identity.md>), [logs](<https://devfeed.tech/tags/logs.md>), [monitoring](<https://devfeed.tech/tags/monitoring.md>), [security](<https://devfeed.tech/tags/security.md>), [telemetry](<https://devfeed.tech/tags/telemetry.md>), [tools](<https://devfeed.tech/tags/tools.md>), [tracing](<https://devfeed.tech/tags/tracing.md>)

### AI overview

Datadog describes security monitoring practices for AI agents, focusing on inventorying components and correlating session-wide telemetry to investigate actions, sensitive-data access, identities, and tool calls.

### Source excerpt

Learn what we discovered about AI agent security by monitoring our agents, from creating an inventory of agent components to tracing sensitive data and tool calls.

## Debug live production code without redeploying with Datadog Live Debugger

DevFeed: [Debug live production code without redeploying with Datadog Live Debugger](<https://devfeed.tech/articles/debug-live-production-code-without-redeploying-with-datadog-live-debugger-2289.md>)

Original publisher: [Read original article](<https://www.datadoghq.com/blog/live-debugger/>)

Author: Eric Metaj; Sarah Stonehill

Published: 2026-08-27T00:00:00Z

Content type: article

Language: en

Sources: [Datadog | The Monitor blog](<https://devfeed.tech/sources/datadog-the-monitor-blog.md>)

Topics: [debugging](<https://devfeed.tech/topics/debugging.md>), [Instrumentation](<https://devfeed.tech/topics/instrumentation.md>), [AI-assisted coding](<https://devfeed.tech/topics/ai-assisted-coding.md>), [Code](<https://devfeed.tech/topics/code.md>)

Tags: [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [apm](<https://devfeed.tech/tags/apm.md>), [bits-ai](<https://devfeed.tech/tags/bits-ai.md>), [code](<https://devfeed.tech/tags/code.md>), [debug](<https://devfeed.tech/tags/debug.md>), [debugger](<https://devfeed.tech/tags/debugger.md>), [debugging](<https://devfeed.tech/tags/debugging.md>), [developer](<https://devfeed.tech/tags/developer.md>), [ide](<https://devfeed.tech/tags/ide.md>), [instrumentation](<https://devfeed.tech/tags/instrumentation.md>), [production](<https://devfeed.tech/tags/production.md>), [root-cause-analysis](<https://devfeed.tech/tags/root-cause-analysis.md>), [tracing](<https://devfeed.tech/tags/tracing.md>)

### AI overview

Datadog Live Debugger helps developers investigate production bugs without changing, restarting, or redeploying application code. It captures runtime details through logpoints, including variable values, method arguments, execution context, and request paths. Bits AI can analyze linked source code, place non-breaking logpoints, interpret collected data, and suggest fixes grounded in production behavior.

### Source excerpt

Learn how Live Debugger helps you investigate production code and debug faster using Bits AI.

## Closing the AI agent trust gap with graduated autonomy

DevFeed: [Closing the AI agent trust gap with graduated autonomy](<https://devfeed.tech/articles/closing-the-ai-agent-trust-gap-with-graduated-autonomy-4638.md>)

Original publisher: [Read original article](<https://aws.amazon.com/blogs/architecture/closing-the-ai-agent-trust-gap-with-graduated-autonomy/>)

Author: Dev Arora

Published: 2026-08-26T17:33:03Z

Content type: article

Language: en

Sources: [AWS Architecture Blog](<https://devfeed.tech/sources/aws-architecture-blog.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [AI Agent](<https://devfeed.tech/topics/ai-agent.md>), [Authorization](<https://devfeed.tech/topics/authorization.md>), [Amazon Bedrock](<https://devfeed.tech/topics/amazon-bedrock.md>), [Amazon Web Services](<https://devfeed.tech/topics/aws.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [tracing](<https://devfeed.tech/topics/tracing.md>), [configuration](<https://devfeed.tech/topics/configuration.md>), [Provisioning](<https://devfeed.tech/topics/provisioning.md>)

Tags: [agent](<https://devfeed.tech/tags/agent.md>), [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [ai-agents](<https://devfeed.tech/tags/ai-agents.md>), [amazon](<https://devfeed.tech/tags/amazon.md>), [amazon-bedrock](<https://devfeed.tech/tags/amazon-bedrock.md>), [amazon-bedrock-agentcore](<https://devfeed.tech/tags/amazon-bedrock-agentcore.md>), [api](<https://devfeed.tech/tags/api.md>), [aws](<https://devfeed.tech/tags/aws.md>), [compliance](<https://devfeed.tech/tags/compliance.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [infrastructure](<https://devfeed.tech/tags/infrastructure.md>), [large-language-model](<https://devfeed.tech/tags/large-language-model.md>), [logs](<https://devfeed.tech/tags/logs.md>), [model](<https://devfeed.tech/tags/model.md>), [policy](<https://devfeed.tech/tags/policy.md>), [production](<https://devfeed.tech/tags/production.md>), [thought-leadership](<https://devfeed.tech/tags/thought-leadership.md>), [tracing](<https://devfeed.tech/tags/tracing.md>)

### AI overview

This article presents graduated autonomy as an architectural pattern for closing the trust gap in AI agents. Agents gain permissions through sustained reliability and lose them when performance degrades, using trust scoring, autonomy tiers, pre- and post-execution controls, policy enforcement, provenance, reversibility, and delivery gates. The design uses Amazon Bedrock AgentCore, Amazon DynamoDB, and AWS CodePipeline.

### Source excerpt

Most teams give AI agents either full access or read-only, leaving value unused or risk unmanaged. This post describes graduated autonomy, an architectural pattern in which agents earn expanded permissions through sustained reliability and lose them when performance degrades, built on Amazon Bedrock AgentCore, Amazon DynamoDB, and AWS CodePipeline.

## Python 3.15 Preview: Sampling Profiler

DevFeed: [Python 3.15 Preview: Sampling Profiler](<https://devfeed.tech/articles/python-3-15-preview-sampling-profiler-4400.md>)

Original publisher: [Read original article](<https://realpython.com/python315-sampling-profiler/>)

Author: Bartosz Zaczyński

Published: 2026-08-26T14:00:00Z

Content type: tutorial

Language: en

Sources: [Real Python](<https://devfeed.tech/sources/real-python.md>)

Topics: [cpu](<https://devfeed.tech/topics/cpu.md>), [Processes](<https://devfeed.tech/topics/processes.md>), [dashboards](<https://devfeed.tech/topics/dashboards.md>)

Tags: [concurrency](<https://devfeed.tech/tags/concurrency.md>), [cpu](<https://devfeed.tech/tags/cpu.md>), [graphs](<https://devfeed.tech/tags/graphs.md>), [processes](<https://devfeed.tech/tags/processes.md>), [profile](<https://devfeed.tech/tags/profile.md>), [python](<https://devfeed.tech/tags/python.md>), [tracing](<https://devfeed.tech/tags/tracing.md>), [tutorial](<https://devfeed.tech/tags/tutorial.md>)

### AI overview

A tutorial previewing Python 3.15's new low-overhead sampling profiler. It covers profiling scripts, modules, threads, async tasks, and live processes, with CPU/I/O/GIL modes and outputs such as flame graphs, heatmaps, and a terminal dashboard.

### Source excerpt

Explore Python 3.15's new sampling profiler and learn low-overhead profiling of scripts, threads, and live production processes.

## Monitor Azure Functions across every hosting plan with Datadog

DevFeed: [Monitor Azure Functions across every hosting plan with Datadog](<https://devfeed.tech/articles/monitor-azure-functions-across-every-hosting-plan-with-datadog-2293.md>)

Original publisher: [Read original article](<https://www.datadoghq.com/blog/monitor-azure-functions-hosting-plans/>)

Author: Duncan Harvey; Kathie Huang; Piyali Banerjee

Published: 2026-08-24T00:00:00Z

Content type: article

Language: en

Sources: [Datadog | The Monitor blog](<https://devfeed.tech/sources/datadog-the-monitor-blog.md>)

Topics: [Azure](<https://devfeed.tech/topics/azure.md>), [serverless monitoring](<https://devfeed.tech/topics/serverless-monitoring.md>), [Serverless](<https://devfeed.tech/topics/serverless.md>), [telemetry](<https://devfeed.tech/topics/telemetry.md>), [tracing](<https://devfeed.tech/topics/tracing.md>), [Traces](<https://devfeed.tech/topics/traces.md>), [Application Performance Management (APM)](<https://devfeed.tech/topics/apm.md>), [cpu](<https://devfeed.tech/topics/cpu.md>), [Node.js](<https://devfeed.tech/topics/node-js.md>), [Python](<https://devfeed.tech/topics/python.md>), [.NET](<https://devfeed.tech/topics/net.md>)

Tags: [apm](<https://devfeed.tech/tags/apm.md>), [azure](<https://devfeed.tech/tags/azure.md>), [azure-functions](<https://devfeed.tech/tags/azure-functions.md>), [cpu](<https://devfeed.tech/tags/cpu.md>), [digital-experience-monitoring](<https://devfeed.tech/tags/digital-experience-monitoring.md>), [metrics](<https://devfeed.tech/tags/metrics.md>), [monitor](<https://devfeed.tech/tags/monitor.md>), [monitoring](<https://devfeed.tech/tags/monitoring.md>), [net](<https://devfeed.tech/tags/net.md>), [node-js](<https://devfeed.tech/tags/node-js.md>), [observability](<https://devfeed.tech/tags/observability.md>), [performance](<https://devfeed.tech/tags/performance.md>), [python](<https://devfeed.tech/tags/python.md>), [serverless](<https://devfeed.tech/tags/serverless.md>), [serverless-monitoring](<https://devfeed.tech/tags/serverless-monitoring.md>), [telemetry](<https://devfeed.tech/tags/telemetry.md>), [traces](<https://devfeed.tech/tags/traces.md>), [tracing](<https://devfeed.tech/tags/tracing.md>)

### AI overview

Datadog Serverless Monitoring expands observability for Azure Functions across Linux and Windows hosting plans, with telemetry collection, distributed tracing, enhanced CPU metrics, and continuous profiling.

### Source excerpt

Trace requests, get enhanced CPU metrics, and profile code across Azure Functions hosting plans with Datadog Serverless Monitoring.

## When Microservice Decomposition Is the Wrong Default

DevFeed: [When Microservice Decomposition Is the Wrong Default](<https://devfeed.tech/articles/when-microservice-decomposition-is-the-wrong-default-34109.md>)

Original publisher: [Read original article](<https://philipptheserver.com/posts/microservice-decomposition-heuristic/>)

Author: Philipp Lehmann (philipp.lehmann@gruppe.ai)

Published: 2026-08-21T07:00:00Z

Content type: opinion

Language: en

Sources: [Philipp Lehmann](<https://devfeed.tech/sources/philipp-lehmann.md>)

Topics: [Microservice](<https://devfeed.tech/topics/microservice.md>), [Architecture & Design](<https://devfeed.tech/topics/architecture-design.md>), [Microservices](<https://devfeed.tech/topics/microservices.md>), [FastAPI](<https://devfeed.tech/topics/fastapi.md>)

Tags: [api](<https://devfeed.tech/tags/api.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [circuit](<https://devfeed.tech/tags/circuit.md>), [docker](<https://devfeed.tech/tags/docker.md>), [microservices](<https://devfeed.tech/tags/microservices.md>), [migration](<https://devfeed.tech/tags/migration.md>), [modular-monolith](<https://devfeed.tech/tags/modular-monolith.md>), [monolith](<https://devfeed.tech/tags/monolith.md>), [openapi](<https://devfeed.tech/tags/openapi.md>), [tracing](<https://devfeed.tech/tags/tracing.md>)

### AI overview

The article argues that splitting a small system into microservices should not be the default. It recommends keeping responsibilities in a modular monolith unless differences in scaling needs, organizational boundaries, or failure isolation justify a network boundary, whose costs include contracts, compatibility management, retries, timeouts, circuit breaking, and distributed tracing.

### Source excerpt

import-linter forbidden contract as a module boundary: a FastAPI modular monolith instead of early microservices, and when a network split pays off.

## Always-on tracing for production and preview traffic

DevFeed: [Always-on tracing for production and preview traffic](<https://devfeed.tech/articles/always-on-tracing-for-production-and-preview-traffic-807.md>)

Original publisher: [Read original article](<https://vercel.com/changelog/always-on-tracing-for-production-and-preview-traffic>)

Author: wits

Published: 2026-08-21T00:00:00Z

Content type: release

Language: en

Sources: [Vercel News](<https://devfeed.tech/sources/vercel-news.md>)

Topics: [tracing](<https://devfeed.tech/topics/tracing.md>), [Traces](<https://devfeed.tech/topics/traces.md>), [Vercel](<https://devfeed.tech/topics/vercel.md>), [Command-line interface](<https://devfeed.tech/topics/cli.md>), [telemetry](<https://devfeed.tech/topics/telemetry.md>)

Tags: [cli](<https://devfeed.tech/tags/cli.md>), [debug](<https://devfeed.tech/tags/debug.md>), [infrastructure](<https://devfeed.tech/tags/infrastructure.md>), [logs](<https://devfeed.tech/tags/logs.md>), [traces](<https://devfeed.tech/tags/traces.md>), [tracing](<https://devfeed.tech/tags/tracing.md>), [vercel](<https://devfeed.tech/tags/vercel.md>)

### AI overview

Vercel introduces always-on tracing for production and preview traffic, allowing teams to debug real user requests through continuously collected, sampled traces. Sampling rules control collection by environment and path, with CLI management, automatic infrastructure and outbound fetch spans, optional framework and custom instrumentation through @vercel/otel, and trace viewing in Logs or via the CLI.

### Source excerpt

With always-on tracing, you can now debug your real user requests without reproducing them. Always-on tracing continuously collects traces from your production and preview traffic. Unlike session tracing, which only captures requests from your own browser, it samples your live traffic. You control what's collected with sampling rules. Each rule sets a trace rate for an environment (All, Production, or Preview), optionally scoped to a path prefix like /checkout. Nothing is collected until you add a rule, so you pay only for what you choose to trace. You can also manage sampling rules from the CLI. vercel traces config set adds a rule, or changes the rate of an existing one when you run it again with the same environment and path prefix: Infrastructure and outbound fetch spans are captured automatically. To add framework and custom spans, instrument your app with @vercel/otel. To view a trace, open the Logs or run vercel traces get <request-id>. Always-on tracing is is now in beta and available to teams on all plans. Tracing is priced at $0.50 per 1M span units. Traces follow your runtime log retention: 1 hour on Hobby, 1 day on Pro and 3 days on Enterprise. Get started by adding a rule in Settings -> Tracing or from the settings icon in Logs. Learn more in the documentation. Read more

## Connect client traces to your logs

DevFeed: [Connect client traces to your logs](<https://devfeed.tech/articles/connect-client-traces-to-your-logs-346.md>)

Original publisher: [Read original article](<https://supabase.com/blog/connect-client-traces-to-your-logs>)

Author: Katerina Skroumpelou; Steven Eubank

Published: 2026-08-18T07:00:00Z

Content type: tutorial

Language: en

Sources: [Supabase Blog](<https://devfeed.tech/sources/supabase-blog.md>)

Topics: [Supabase](<https://devfeed.tech/topics/supabase.md>), [tracing](<https://devfeed.tech/topics/tracing.md>), [OpenTelemetry](<https://devfeed.tech/topics/opentelemetry.md>), [client](<https://devfeed.tech/topics/client.md>), [Amazon API Gateway](<https://devfeed.tech/topics/amazon-api-gateway.md>), [browser](<https://devfeed.tech/topics/browser.md>), [SIEM, Security, Observability](<https://devfeed.tech/topics/siem-security-observability.md>)

Tags: [api](<https://devfeed.tech/tags/api.md>), [backend](<https://devfeed.tech/tags/backend.md>), [browser](<https://devfeed.tech/tags/browser.md>), [how-to](<https://devfeed.tech/tags/how-to.md>), [integration](<https://devfeed.tech/tags/integration.md>), [opentelemetry](<https://devfeed.tech/tags/opentelemetry.md>), [traces](<https://devfeed.tech/tags/traces.md>), [tracing](<https://devfeed.tech/tags/tracing.md>)

### AI overview

A walkthrough of connecting client-side traces to Supabase logs by propagating W3C Trace Context. It explains how to configure a tracer and client propagation so requests can be followed from the browser through Supabase's API Gateway and Edge Function logs, including with external log backends.

### Source excerpt

supabase-js now propagates W3C Trace Context to Supabase, so a client trace and the matching Supabase log share one trace_id.

## Trace AWS Lambda durable functions with Datadog

DevFeed: [Trace AWS Lambda durable functions with Datadog](<https://devfeed.tech/articles/trace-aws-lambda-durable-functions-with-datadog-2315.md>)

Original publisher: [Read original article](<https://www.datadoghq.com/blog/trace-aws-lambda-durable-functions/>)

Author: Joey Zhao; Yiming Luo; Pablo Martinez Bernardo; Ethan Gracer

Published: 2026-08-18T00:00:00Z

Content type: article

Language: en

Sources: [Datadog | The Monitor blog](<https://devfeed.tech/sources/datadog-the-monitor-blog.md>)

Topics: [telemetry](<https://devfeed.tech/topics/telemetry.md>), [Node.js](<https://devfeed.tech/topics/node-js.md>), [SDKs](<https://devfeed.tech/topics/sdks.md>)

Tags: [apm](<https://devfeed.tech/tags/apm.md>), [aws](<https://devfeed.tech/tags/aws.md>), [aws-lambda](<https://devfeed.tech/tags/aws-lambda.md>), [instrumentation](<https://devfeed.tech/tags/instrumentation.md>), [node-js](<https://devfeed.tech/tags/node-js.md>), [observability](<https://devfeed.tech/tags/observability.md>), [python](<https://devfeed.tech/tags/python.md>), [serverless-monitoring](<https://devfeed.tech/tags/serverless-monitoring.md>), [telemetry](<https://devfeed.tech/tags/telemetry.md>), [traces](<https://devfeed.tech/tags/traces.md>), [tracing](<https://devfeed.tech/tags/tracing.md>), [workflow](<https://devfeed.tech/tags/workflow.md>)

### AI overview

Datadog instruments AWS Lambda durable executions to connect telemetry from multiple Lambda invocations into one trace, helping developers investigate workflow operations, retries, waits, and failures.

### Source excerpt

Trace AWS Lambda durable executions across invocations to investigate operations, retries, failures, waits, and function status in Datadog.

## Observability best practices for Lambda durable functions

DevFeed: [Observability best practices for Lambda durable functions](<https://devfeed.tech/articles/observability-best-practices-for-lambda-durable-functions-4670.md>)

Original publisher: [Read original article](<https://aws.amazon.com/blogs/compute/observability-best-practices-for-lambda-durable-functions-2/>)

Author: D Surya Sai

Published: 2026-08-14T12:49:49Z

Content type: tutorial

Language: en

Sources: [AWS Compute Blog](<https://devfeed.tech/sources/aws-compute-blog.md>)

Topics: [AWS Lambda](<https://devfeed.tech/topics/aws-lambda.md>), [observability](<https://devfeed.tech/topics/observability.md>), [Monitoring](<https://devfeed.tech/topics/monitoring.md>), [log management](<https://devfeed.tech/topics/log-management.md>), [tracing](<https://devfeed.tech/topics/tracing.md>), [Instrumentation](<https://devfeed.tech/topics/instrumentation.md>), [debugging](<https://devfeed.tech/topics/debugging.md>), [Amazon API Gateway](<https://devfeed.tech/topics/amazon-api-gateway.md>), [stripe](<https://devfeed.tech/topics/stripe.md>), [Serverless](<https://devfeed.tech/topics/serverless.md>)

Tags: [advanced-300](<https://devfeed.tech/tags/advanced-300.md>), [amazon-api-gateway](<https://devfeed.tech/tags/amazon-api-gateway.md>), [aws](<https://devfeed.tech/tags/aws.md>), [aws-lambda](<https://devfeed.tech/tags/aws-lambda.md>), [debugging](<https://devfeed.tech/tags/debugging.md>), [instrumentation](<https://devfeed.tech/tags/instrumentation.md>), [logging](<https://devfeed.tech/tags/logging.md>), [metrics](<https://devfeed.tech/tags/metrics.md>), [monitoring](<https://devfeed.tech/tags/monitoring.md>), [observability](<https://devfeed.tech/tags/observability.md>), [payment](<https://devfeed.tech/tags/payment.md>), [payment-processing](<https://devfeed.tech/tags/payment-processing.md>), [payments](<https://devfeed.tech/tags/payments.md>), [serverless](<https://devfeed.tech/tags/serverless.md>), [technical-how-to](<https://devfeed.tech/tags/technical-how-to.md>), [tracing](<https://devfeed.tech/tags/tracing.md>)

### AI overview

This tutorial presents an observability pattern for AWS Lambda durable functions using a Stripe payment-processing workflow. It covers durable-function metrics, custom business metrics, alarms, structured logging, AWS X-Ray tracing, instrumentation across suspension boundaries, and end-to-end debugging of callback timeouts.

### Source excerpt

Learn observability best practices for AWS Lambda durable functions, including CloudWatch metrics, custom alarms, structured logging, and X-Ray tracing for debugging callback timeouts end-to-end.

## Automated agent triage with Agent Tracing and Claude Routines

DevFeed: [Automated agent triage with Agent Tracing and Claude Routines](<https://devfeed.tech/articles/automated-agent-triage-with-agent-tracing-and-claude-routines-24091.md>)

Original publisher: [Read original article](<https://blog.sentry.io/claude-routines-agent-triage/>)

Author: Trevor Elkins

Published: 2026-08-13T09:00:00Z

Content type: article

Language: en

Sources: [Sentry Blog](<https://devfeed.tech/sources/sentry-blog.md>)

Topics: [Claude](<https://devfeed.tech/topics/claude.md>), [AI Agent](<https://devfeed.tech/topics/ai-agent.md>), [tracing](<https://devfeed.tech/topics/tracing.md>), [Model Context Protocol](<https://devfeed.tech/topics/model-context-protocol.md>), [agent observability](<https://devfeed.tech/topics/agent-observability.md>)

Tags: [agent](<https://devfeed.tech/tags/agent.md>), [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [claude](<https://devfeed.tech/tags/claude.md>), [mcp](<https://devfeed.tech/tags/mcp.md>), [model-context-protocol-server](<https://devfeed.tech/tags/model-context-protocol-server.md>), [report](<https://devfeed.tech/tags/report.md>), [tool](<https://devfeed.tech/tags/tool.md>), [tracing](<https://devfeed.tech/tags/tracing.md>)

### AI overview

Sentry describes a Claude Routine that uses the Sentry MCP to query Agent Tracing data from roughly 800 overnight AI agent conversations. It aggregates error statistics, samples traces and conversations, reviews the Seer codebase, and files new Linear tickets for findings that are not already tracked.

### Source excerpt

How Sentry uses a Claude Routine and the Sentry MCP to automatically triage 800 AI agent conversations overnight and file bugs.

## Elastic community newsletter -- August 2026

DevFeed: [Elastic community newsletter -- August 2026](<https://devfeed.tech/articles/elastic-community-newsletter-august-2026-4798.md>)

Original publisher: [Read original article](<https://www.elastic.co/blog/devrel-newsletter-august-2026>)

Author: Elastic DevRel team

Published: 2026-08-13T00:00:00Z

Content type: news

Language: en

Sources: [Elastic Blog - Elasticsearch, Kibana, and ELK Stack](<https://devfeed.tech/sources/elastic-blog-elasticsearch-kibana-and-elk-stack.md>)

Topics: [elasticsearch](<https://devfeed.tech/topics/elasticsearch.md>), [devrel](<https://devfeed.tech/topics/devrel.md>), [dashboards](<https://devfeed.tech/topics/dashboards.md>), [Embeddings](<https://devfeed.tech/topics/embeddings.md>), [multimodal](<https://devfeed.tech/topics/multimodal.md>), [quantization](<https://devfeed.tech/topics/quantization.md>), [AI search](<https://devfeed.tech/topics/ai-search.md>), [tracing](<https://devfeed.tech/topics/tracing.md>)

Tags: [audio](<https://devfeed.tech/tags/audio.md>), [dashboards](<https://devfeed.tech/tags/dashboards.md>), [devrel](<https://devfeed.tech/tags/devrel.md>), [elasticsearch](<https://devfeed.tech/tags/elasticsearch.md>), [embeddings](<https://devfeed.tech/tags/embeddings.md>), [images](<https://devfeed.tech/tags/images.md>), [multimodal](<https://devfeed.tech/tags/multimodal.md>), [pdf](<https://devfeed.tech/tags/pdf.md>), [quantization](<https://devfeed.tech/tags/quantization.md>), [tracing](<https://devfeed.tech/tags/tracing.md>), [video](<https://devfeed.tech/tags/video.md>)

### AI overview

The Elastic community newsletter covers Elasticsearch 9.5, including native PromQL, the Dashboards API, Columnar Mode, vector search improvements, multimodal semantic search, and Elastic Agent Builder tracing in technical preview.

### Source excerpt

In this newsletter, we cover version 9.5 of Elasticsearch, the latest blogs and videos, and upcoming events.

[Next page](<https://devfeed.tech/tags/tracing.md?cursor=WyIyMDI2LTA4LTEzVDAwOjAwOjAwKzAwOjAwIiwgIjQ4ZTJlZDk5LTMwOGMtNGJhNy1iNThkLTYwMTBhYjEyZDJlOCJd>)