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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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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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honeycomb

AI Working for You: MCP, Canvas, and Agentic Workflows - Part 2

Apr 6, 2026 By Ken Rimple In Honeycomb
In our previous post in our series on observability for the agent era, we looked at how Honeycomb provides unique visibility into LLMs operating in your production environment. Now, let’s flip it around and explore how Honeycomb provides observability insights uniquely suited to helping your AI agents rapidly diagnose and fix production issues, and build production feedback into the next round of development.
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honeycomb

The Fundamentals: Fast, Deep, and Ready for What Comes Next - Part 3

Apr 6, 2026 By Ken Rimple In Honeycomb
The previous two posts in this series have looked at some of the use cases Honeycomb customers are implementing to observe LLMs in production and power agentic observability workflows. In this third and final post, we’ll take it back to basics and look at how the fundamental capabilities and infrastructure of Honeycomb provide the comprehensive data and fast performance that makes these use cases work at production scale. AI capabilities built on a weak observability foundation fall apart fast.
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