'observability' 검색 결과

상시 실행되는 AI 에이전트의 시작·중지, 로그 모니터링, 권한 관리, 단계적 업데이트와 롤백 절차를 정리합니다. 장애 대응과 성공률·비용 평가를 지원합니다.

• 상시 실행 에이전트의 운영 설계하기
• 실패가 급증할 때 장애 대응하기
• 업데이트와 롤백 절차 마련하기

browser-use 참고 자료를 바탕으로 Python 브라우저 자동화 구현을 돕습니다. 설치, 에이전트와 브라우저 설정, 도구 추가, 실행 모니터링과 비용 추적을 다룹니다.

• Python으로 브라우저 작업을 자동화합니다
• 에이전트와 브라우저 인증을 설정합니다
• 사용자 정의 도구와 실행 훅을 추가합니다

앱의 LLM 호출을 Caveman Cloud로 연결해 전송 내용을 유지하면서 사용량과 비용을 측정합니다. 연결 설정부터 실제 요청을 통한 검증까지 수행합니다.

• LLM 앱의 사용량과 비용 측정 시작
• 기존 LLM 호출의 연결 주소 변경
• 실제 요청으로 연결 검증

운영 환경의 동작과 장애 원인을 추적하도록 로그, 지표, 서비스 간 처리 경로와 알림을 설계합니다. 실제 출력과 알림까지 검증합니다.

• 운영 기능의 모니터링 정보 구성
• 서비스 간 로그 연결
• 알림과 대응 절차 검토

Monitor a swarm, a group of agents, and receive notifications when its status or agent count changes. A watcher script checks status periodically and streams changes through Monitor; separate tools provide one-time status and health checks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Receive notifications when swarm status changes
• Track changes in the number of agents
• Adjust the watcher update interval

Inspect how a task ran across agents by collecting recorded execution spans, or timed processing steps. Build a trace tree showing agent activity, duration, errors, and bottlenecks, with a summary of the execution flow.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating a slow task
• Reviewing coordination between agents
• Locating errors in recorded execution

Summarizes recorded system metrics for a chosen period, including completed tasks, errors, memory use, and token consumption. It compares values with historical baselines and flags unusual changes to help assess agent team health.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Review task completions and errors from the past hour
• Check active agents, memory use, and token consumption
• Spot unusual changes in task duration

Export recorded AI usage costs and budgets as Prometheus metrics or JSON. Feed external dashboards with totals, model tier breakdowns, session costs, and budget utilization, or send the same data to a webhook endpoint.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Refreshing cost metrics for Grafana or Prometheus
• Sending a one-time cost report to a webhook
• Providing budget utilization data for monitoring

Set up or review production error monitoring to capture unhandled errors, failed requests, and performance signals. Check event context, release labels, and alert routing so teams receive useful information when problems occur.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Setting up error monitoring for a new production app
• Investigating errors that monitoring fails to capture
• Reviewing critical alerts and on-call routing

Reconstruct how an AI coding agent produced a feature, pull request, or report using local ax records. Connect sessions, commits, skills, and tool activity into an evidence-based sequence with key decisions, checks, and a repeatable recipe.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Tracing how a shipped feature was built
• Reconstructing development from a commit
• Finding the workflow behind work done on a known date

Deploy and operate AI agents with AWS Bedrock AgentCore. Choose services for hosting agents, connecting external APIs, managing credentials and conversation state, monitoring activity, and assessing agent quality through automated evaluations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Plan an agent deployment on AgentCore
• Expose a REST API as an agent tool through MCP
• Configure credentials and access controls

Coordinate specialist agents to investigate and improve application performance across databases, backend services, frontend code, and delivery infrastructure. Establish baseline measurements, address observed bottlenecks, validate changes with load tests, and set up monitoring to detect future slowdowns.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding bottlenecks across an entire application
• Coordinating database, backend, and frontend improvements
• Validating performance changes with load tests

Design mock APIs, simulated services that reproduce expected responses, authentication, errors, and delays. Define scenarios and repeatable test data to support frontend development, integration testing, and realistic demos before the real backend is ready.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Developing a frontend before the backend is ready
• Simulating a partner or third-party API
• Preparing a demo with realistic API responses

Inspect local AI coding session logs with agenttrace to identify cost spikes, delays, failed tools, and retry loops. Produce readable reports, compare attempts, and configure automated checks for unusual or unhealthy runs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigate slow or expensive AI coding sessions
• Review failed runs before retrying
• Compare tool usage and verification across attempts

Add monitoring to AI agents with response times, token usage, estimated costs, and error metrics. Trace records follow model and tool calls step by step, helping teams investigate failures and understand operating costs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigate slow agent responses
• Track token usage and estimated costs
• Trace failures in model and tool calls

Set up distributed tracing with Jaeger and Tempo to follow requests across services. Examine delays, service dependencies, and error propagation, with guidance on sampling, linking logs to traces, and troubleshooting missing trace data.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating slow requests across services
• Understanding service dependencies
• Tracing how errors spread

Build Grafana dashboards that show application and infrastructure health through request rates, errors, response times, and resource usage. Organize metrics into useful panels, add filters and alerts, and manage dashboard definitions as code.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build an API health dashboard
• Visualize CPU and memory usage
• Track service level objectives and business KPIs

Set up Phoenix tracing for LLM applications in Python or TypeScript. The skill guides automatic and custom recording of individual operations, conversation grouping, metadata, feedback, and production settings such as batching and personal data masking.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Set up tracing for an LLM application
• Record document retrieval and tool calls
• Group conversation traces by session

Add Arize AX tracing to an LLM application, recording how model calls and tool executions connect. Inspect the existing code, select suitable integrations, add instrumentation, and verify that the application runs and traces reach Arize.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Setting up tracing for an LLM application
• Recording tool inputs and outputs in an agent
• Adding Arize export to an existing OpenTelemetry setup

Apply consistent names to SGLang speculative decoding code, which verifies draft tokens to generate text. Distinguish tokens with or without the bonus token, counts, counters, and arrays when adding identifiers, renaming code, or reviewing changes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Naming variables in speculative decoding code
• Renaming identifiers to match their meaning
• Reviewing metric and counter names

Design LLM applications using patterns for answers grounded in documents, agents that use tools, and prompt testing. Implementation examples also cover monitoring response quality, latency, costs, and failures during operation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building an assistant that answers from internal documents
• Choosing an architecture for an agent that uses tools
• Comparing and versioning prompts

Investigate production issues by searching Datadog logs, querying metrics, and following request traces across services. Compare error counts across time periods, inspect surrounding logs, and manage dashboards through a command-line tool.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating errors in production
• Comparing error counts across time periods
• Following request traces across services

Connect cognee to additional databases and data sources through community packages. The skill covers installing and registering adapters, importing data through connectors, configuring monitoring, and developing extension packages with examples and tests.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Connect cognee to Qdrant or another database
• Import data from Slack or Gmail
• Configure monitoring with Keywords AI

Audit a codebase before production deployment across eight areas, including security, databases, dependencies, and AI usage. Receive findings with file locations, manual checklists, and a release verdict that blocks shipping when critical issues remain.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check an application before its first production release
• Find security and database issues before deployment
• Review backup restoration and rollback readiness