Operations | Monitoring | ITSM | DevOps | Cloud

Why do AI agents that look brilliant in a sandbox fail the moment they hit production? For platform leaders, the answer is a lack of environmental parity: the ability to interact with the exact data state and service topology where the actual bugs live. When an agent attempts to modify a schema, optimize a query, or reproduce a bug without access to the real-world data state, it hits the Data Context Gap.

No one wants to stare at a plant and guess if it needs water. It’s much easier if the plant can say, “I’m thirsty.” A few years ago, we built Plant Buddy using InfluxDB Cloud 2.0. The linked article is still a great guide for cloud-first IoT prototyping as it shows how quickly you can connect devices, store time series data, and build dashboards in the cloud with the previous version of InfluxDB. But this time, the goal was different.

Corporate investment in Generative AI is outpacing value realization. While Large Language Models (LLMs) possess vast general reasoning capabilities, they suffer from a critical blind spot: they are pre-trained on the public internet, yet completely blind to your enterprise reality. This context gap renders even the most advanced models ineffective, forcing them to guess (hallucinate) rather than reason based on your specific business rules.

Since the rise of Large Language Models (LLMs) like GPT-3 and GPT-4, organizations have been rapidly adopting Agentic AI to automate and enhance their workflows. Agentic AI refers to AI systems that act autonomously, perceiving their environment, making decisions, and taking actions based on that information rather than just reacting to direct human input.

Dozens of startups are sprinting to build the next “agentic SIEM” that can autonomously detect, investigate, and respond to threats. They’re well-funded, well-marketed, but structurally hollow. Here’s what it usually looks like: an LLM layer on top of a thin orchestration engine on top of fragmented or customer-hosted data lakes. While it looks impressive in a demo, it quickly falls apart in production. Why? It’s not built on a strong foundation.

Your software is sending data to Honeycomb. Now where is the dashboard you want? The best dashboard is one created just for your application, or your service, or your team. You can get that in minutes with the Honeycomb MCP. Open your coding agent in your IDE, or on the command line in your code repository. Configure the Honeycomb MCP and authenticate with Read and Write permissions. Now tell it what you want. You can be high-level: Make me a service health board for the frontend service.

Since the Datadog Model Context Protocol (MCP) Server first launched in Preview, Datadog has experienced an overwhelming amount of interest and feedback from customers. We appreciate those who requested access to test our product, provided feedback, and shared their stories of how the MCP Server helped them overcome engineering challenges.

Here's something nobody talks about at the AI strategy meetings. Your organization just spent six figures on Cursor licenses, Claude seats, and Copilot subscriptions. Ninety percent of your engineers have access. By most internal measures, the rollout was a success. But somewhere on your team, one developer is running circles around everyone else.