Honeycomb.io announced new features designed to help DevOps and engineering teams find and fix issues in production before their customers do. The release introduces Honeycomb’s new AI Ecosystem, which provides a fleet-wide view of all of your AI agents in production, their performance, costs and individual conversations, while new Canvas MCP connectors bring context to your telemetry data.
“Teams are shipping agents faster than they can see what those agents are doing,” said Graham Siener, SVP of Product at Honeycomb. “You can’t govern what you can’t see. AI Ecosystem gives you one view of failure rates, costs, and behaviors of every agent you run, including the ones that haven’t caught fire yet. Even better, it also allows you to go straight down to the conversation that caused an issue. When something needs a closer look, Canvas connectors put the code, tickets and team discussion right next to the telemetry, instead of scattered across five other tools.”
Managing a Multi-agent Fleet with Honeycomb’s AI Ecosystem
Organizations are releasing and deploying agents at a blistering pace. According to a recent survey conducted by Honeycomb, 71% of respondents said they’re deploying AI agents faster than their ability to manage or govern them.
Engineering teams need to be able to answer critical questions quickly such as:
- Which agent is degrading, and is it one agent, or fleet-wide?
- Is this a real regression or normal variance?
- Which customers are directly affected?
Honeycomb’s AI Ecosystem gives teams a diagnosis layer for an entire fleet of agents in production, providing a continuous, aggregate assessment on failure rates, token usage, latency, and cost across every agent. Because it’s built on Honeycomb’s high-cardinality data model, teams can drill down into individual agent conversations seamlessly from a high-level fleet view.
At launch, AI Ecosystem contains three core features, including Agent Fleet Performance, LLM Cost Tracking, and Agent Conversations (previously known as Agent Timeline). These collective capabilities provide full context and dynamic granularity on fleet-level health, per-agent health and volume, cost and efficiency, latency and performance, and blast radius.
Bring More Context into Canvas Investigations
AI is accelerating code generation, tripling the shipment of code and production issues, and adding more toil on engineers on call.* To help teams respond faster, Honeycomb is expanding Canvas, its multiplayer workspace, with new MCP connectors.
These connectors provide context beyond production telemetry. They add details like source code, IT and support tickets, design rationale, live team conversations and more. Connectors available today include GitHub, Linear, Pylon, incident.io, Atlassian, Braintrust, Sentry, Amplitude, and Checkly.
Unlike single-user, chat-based agents, Canvas allows teams to work collaboratively to view the same telemetry artifacts, edit queries, verify agent prompts, check tool invocations and more. It’s designed for the realities of investigating complex production environments, where teams of humans and agents need to operate off of shared context.
“Canvas gives you that unified look of this is the problem I’m trying to solve, these are the queries I threw at it, the questions I gave it,” George Luong, Senior Engineering Manager, Observability at Slack. “You can see the thought process. You see the charts that it’s plotting out.”
