AI Workflows Get New Visibility from Honeycomb
Engineering teams running AI agents in production have a new way to see what those agents are actually doing, after observability vendor Honeycomb introduced a set of agent-focused features built on OpenTelemetry standards rather than proprietary SDKs.
The release adds three capabilities to the Honeycomb platform: Agent Timeline, Canvas Agent and Canvas Skills. The vendor frames the launch as a response to the non-deterministic, multi-hop nature of agent workflows, which it argues older observability tools were not designed to handle.
Agent Timeline renders multi-agent, multi-trace workflows as a single view, connecting language model calls, tool invocations, agent handoffs and downstream system impacts. The intent is to let engineers reconstruct a decision path when an agent contributes to an incident, rather than piecing together logs by hand.
Canvas has been rebuilt as a collaborative workspace, chat interface and autonomous agent in one. Auto-investigations let the Canvas agent gather data, form and test hypotheses and propose remediation when an alert fires or a service level objective is at risk. Canvas Skills encode debugging knowledge and framework-specific practices, such as Kafka troubleshooting, into reusable playbooks.
"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, cofounder 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.”
The product approach leans on OpenTelemetry rather than custom instrumentation. Honeycomb has integrated the OpenTelemetry GenAI semantic conventions at version 1.40.0, treating gen_ai.* attributes as first-class data so model evaluations, tool executions, Model Context Protocol calls and language model outputs surface without re-instrumentation as the specification evolves.