Honeycomb.io introduced a series of agentic intelligence and agent observability features purpose-built for AI agents in production.
These new and enhanced capabilities include Agent Timeline, Canvas Agent, and Canvas Skills. Engineering teams will now have real-time visibility into what their agents are actually doing without proprietary software development kits (SDKs) or framework lock-in.
"AI has upended how software works: introducing real nondeterminism into production, and fundamentally changing how teams of humans and agents build and validate code,” said Christine Yen, co-founder and CEO of Honeycomb. “Engineers are drowning in uncertainty as most observability tools weren’t built for this sort of “unknown-unknown.” Honeycomb was built for the hardest parts of building software—and autonomous agents have taken those hardest parts mainstream."
Honeycomb's new AI-native observability capabilities allow engineering teams to track and analyze the performance, behavior, and interactions of AI agents. With Agent Timeline, users can render multi-agent, multi-trace workflows as a single coherent view, connecting every LLM call, tool invocation, agent handoff, and downstream system impact in real time. Engineering teams can now trace what an agent did, reconstruct the full decision path, and understand failures without switching tools or piecing together logs by hand.
New and enhanced features from Honeycomb supercharge human and agent teams by surfacing intelligence at machine speed. These include:
- Canvas, newly rebuilt to serve as a collaborative workspace, a chat interface, and autonomous agent all in one, allows engineering teams to query issues in plain English, work with human and agent team members on investigations, and produce sharable visualization snapshots.
- Auto-investigations set your Canvas agent to work automatically when an alert fires, an SLO burns, or an anomaly surfaces, gathering data, creating and testing hypotheses, and proposing remediation, all before an engineer even opens their laptop.
- Canvas Skills encode your best engineers' debugging knowledge and best practices for frameworks or services like Kafka into reusable playbooks that run autonomously.
As a supporter and contributor to the OpenTelemetry project, Honeycomb has integrated the OpenTelemetry (OTel) GenAI semantic conventions (v1.40.0) into its platform. By making gen_ai.* attributes first-class citizens, Honeycomb ensures that model evaluations, tool executions, MCP calls, LLMs, and agents are all properly observed. This alignment with OpenTelemetry GenAI standards allows for the automatic generation of structured GenAI insights without requiring re-instrumentation as specifications change, custom setups, or the use of proprietary SDKs.
Honeycomb's proprietary unified data store was built for exactly the kind of unpredictable, high-dimensionality telemetry that AI agents generate, from the ground up.
Canvas, Canvas Agent, and Skills are available starting next week for all Honeycomb customers.
Agent Timeline is available in Early Access, and anticipated to be generally available next month.
The Latest
Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...
For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...
Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...
Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...
For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...
New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...
Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...
In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ...
In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...
When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...