Operations | Monitoring | ITSM | DevOps | Cloud

sentry

AI agent observability: The developer's guide to agent monitoring

Apr 7, 2026 By Sergiy Dybskiy In Sentry
Most "agent observability best practices" content reads like a compliance checklist from 2019 with "AI" pasted over "microservices." Implement comprehensive logging. Establish evaluation metrics. Create governance frameworks. Not a single line of code. No mention of what happens when your agent silently picks the wrong tool on turn 3 and you need to figure out why.
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datadog

Operating agentic AI with Amazon Bedrock AgentCore and Datadog LLM Observability: Lessons from NTT DATA

Apr 7, 2026 By Tohn Furutani In Datadog
This guest blog post is by Tohn Furutani, SRE Engineer at NTT DATA. Over the past year, the conversation around generative AI has shifted from single-shot use cases—such as summarization, Q&A, and chat interfaces—to agentic AI systems that can make decisions based on context, plan multistep actions, invoke tools, and adapt as conditions change.
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digitate

The Next Phase of Agentic AI

Apr 7, 2026 By Digitate In Digitate
The Enterprise AI Survey conducted by Digitate in collaboration with Sapio Research states that the journey of enterprise automation and AI adoption has evolved significantly. The initial waves focused primarily on improving accuracy, efficiency, and reducing costs. Now, the next phase, Agentic AI, is transforming this shift from mere automation to dynamic collaboration.
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Practical AI-Enabled Observability for Agents and LLMs

Apr 7, 2026 By Datadog In Datadog
You’re told to “go build agents” without clear guidance on what that actually means, how to do it well, or how to know if it is working. You are not a data scientist. You are a software engineer. In this talk, a Datadog AI product leader Shri Subramanian breaks down what changes when you move from building applications to building AI agents, and why familiar approaches like traditional testing and linear delivery fall short. We will explore how agent development shifts the focus from code alone to data, prompts, and evaluation, and why functional reliability matters just as much as operational reliability.
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How to Catch AI Code Mistakes Before They Reach Production

Apr 7, 2026 By GitKraken In GitKraken
AI can write code fast, but it makes mistakes humans often don't. In this session from Ole Lensmar, CTO of Testkube, breaks down the real quality risks of AI-generated code and how engineering teams can build guardrails before those bugs hit production. What you'll learn: Common mistakes LLMs make (and which ones are unique to AI) Whether you're a developer leaning on AI to ship faster or a QA lead trying to keep up with the pace of AI-generated code, this talk gives you a practical framework for staying ahead of quality issues.
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uptrace

LLM Cost Monitoring with OpenTelemetry

Apr 7, 2026 By Alexandr Bandurchin In Uptrace
Teams running LLM applications in production face a cost problem that traditional APM tools were never designed to solve. CPU and memory costs are relatively predictable — a web service processing 1,000 requests per second costs roughly the same week over week. LLM API costs are not. A single user session can cost $0.01 or $5 depending on prompt length, model choice, conversation history, and how many retries happen inside your chain.
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How AI Is Powering the Next Era of IT Operations

Apr 7, 2026 By ScienceLogic In ScienceLogic
AI is redefining the future of IT. In this Nexus Live 2025 keynote, ScienceLogic CEO and Founder Dave Link shares the vision behind Skylar AI, why the industry is shifting toward autonomous operations, and how organizations can move faster, smarter, and more proactively than ever before. In this session you’ll see.
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IREX Enhances FireTrack AI Module for Faster, More Accurate Fire Detection

IREX Enhances FireTrack AI Module for Faster, More Accurate Fire Detection

Apr 7, 2026 By OpsMatters In OpsMatters
WASHINGTON, DC - IREX, a global developer of ethical AI and intelligent video analytics, has announced a significant upgrade to its FireTrack fire and smoke detection module, expanding its capabilities across a wide range of environments. As outlined in an article on TNW, the updated solution is designed to work seamlessly with existing camera infrastructure, eliminating the need for additional hardware while extending its use to critical infrastructure, public institutions, residential and commercial properties, and natural environments such as parks and forests.
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From AI Idea to Real System: What Changes Along the Way

From AI Idea to Real System: What Changes Along the Way

Apr 7, 2026 By OpsMatters In OpsMatters
Most companies don't struggle with the idea of AI. They struggle with what to do with it. The potential is clear-automation, predictions, better decisions. But translating that into something useful inside a business is where things become less obvious. That's usually when ai ml consulting services start to make sense.
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