Honeycomb Introduces AI SRE Agents That Go Beyond Incident Chasing, with Goal-Oriented Continuous Improvement
Personal and Team Agents start from high-cardinality traces, learn from every investigation, and act only within the limits teams set
SAN FRANCISCO, Nov. 3, 2026 /PRNewswire/ -- Honeycomb.io, the observability platform to improve software in production, today announced AI site reliability engineering (SRE) agents designed to enhance individual and team engineering efforts by watching deploys, catching regressions before customers do, and paying down reliability debt: work that rarely makes the sprint, but pays dividends in more stable future releases.
Many AI SRE tools either sit on top of someone else's telemetry or only respond to alerts when something is already a problem. Honeycomb's agents work toward goals you define for tasks like resolving incidents, fine-tuning instrumentation, or watching production deploys. They draw context from Honeycomb's rich, high-cardinality telemetry data and Model Context Protocol (MCP) connectors. This allows them to link every finding to the query, trace, or deploy behind it. They also remember what they learn and carry it into the next investigation.
"Code is getting written faster than any of us can review it, so production is ultimately where we find out whether it works," said Christine Yen, CEO and co-founder at Honeycomb. "The agents doing that checking need real data to work from, and deliver incredible results when driven by the rich production metadata we've always welcomed. Our agents investigate, show their work, and earn your trust. They also remember what they learn, so what one engineer figures out on Tuesday, the whole team (human or agent!) knows on Wednesday."
Code is shipping faster than teams can review it
According to Honeycomb's forthcoming survey, 76% of respondents said that AI-generated code has grown faster than their team's ability to review and monitor it. And 65% say they had a production issue in the past six months caused by AI rate limits, runaway agent loops, or token spend spikes in code developed by and calling AI. Reviewing all the code is no longer practical, so production data has to carry the load. Agents are only as good as the data they query.
"With agent-based software development, human review of all code is no longer viable or even desirable in high performing engineering teams. We're all testing in production now," said James Governor, co-founder at RedMonk. "Honeycomb's AI SRE agents are designed to provide the quality tracing and shared context critical for successful troubleshooting by individuals and teams."
Personal Agent: Honeycomb's expertise, plus yours
The Personal Agent is a collaborator and an assistant for your human teammates. It combines Honeycomb's observability expertise with your team's runbooks, skills, and systems. Ask a question or run a full investigation in Honeycomb. The agent keeps your chats private, shows you how it reached its conclusion, and acts on recommendations only when you tell it to.
Team Agents: Goal-oriented workers that lift the team
These agents work with entire teams to support investigations, performance checks, quality improvements and more. Give the agent a goal, such as monitoring deploys, and it works in the background on a schedule, trigger, or service level objective (SLO). Goals, whether pre-defined by Honeycomb's observability experts or created by users, define the outcomes for SRE tasks, such as:
- Watching a release
- Improving instrumentation
- Investigating alerts
- Improving agent quality
- Maintaining knowledge docs
Team Agents build a shared memory from every investigation, whether the end result came from a person, a Personal Agent, or an automated run. Every Team Agent finds links to the query, trace, deploy, or ticket it came from, and each says whether it's confirmed or a hypothesis. Within the permissions a team grants, the agent files the ticket, pages the responder, or drafts the fix. Autonomy grows as the agent earns it, and the team decides what it can do on its own.
"Many AI SRE tools only start when an alert fires, after the problem has already reached customers. Team Agents start earlier," said Graham Siener, SVP of Product at Honeycomb. "They investigate alerts, and they keep working when no one is paged: watching deploys, improving instrumentation, and paying down reliability debt. Each one works from distributed traces and within limits the team sets, so engineers can hand it a goal and check how it reached each finding. That means regressions get caught before customers see them."
Availability
Honeycomb Personal and Team Agents are now in early access. If you're interested in seeing how these agents perform in action, be sure to visit https://www.honeycomb.io/platform/team-agents or find us at AWS re:Invent November 30-December 4.
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About Honeycomb
Honeycomb is the observability platform to improve software in production. Built on a decade of distributed tracing leadership and deep roots in the OpenTelemetry community, Honeycomb gives engineering teams—and the AI agents now operating alongside them—real-time, high-cardinality answers about any production system, with no pre-aggregation and no cardinality limits. Learn more at www.honeycomb.io and follow us on LinkedIn.
Media Contact
Ciri Haugh
Press@honeycomb.io
SOURCE Honeycomb
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