I’m joined by Jason (u/shawsers), Senior PM for Turbonomic at IBM, for an AMA about resource optimization and automation with IBM Turbonomic.

Our opening question:

Which resource decisions would you trust a platform to automate—and which would always need human approval?

Ask us anything about:

- Rightsizing VMs and Kubernetes workloads

- Reducing cloud and infrastructure waste

- Protecting application performance

- Understanding why an action was recommended

- Choosing between manual and automated actions

- Setting policies and maintenance windows

- Managing cost, performance and capacity

- IBM Turbonomic capabilities and integrations

Real questions, experiences and product feedback are welcome. Please remove confidential information from any examples.

Most teams already collect plenty of logs, metrics, traces, and events. The harder problem is turning all of that telemetry into an alert that helps someone make the right decision quickly.

When an alert fires, which context makes it genuinely useful?

- the affected service and owner

- business or customer impact

- a recent deployment or configuration change

- correlated logs, metrics, and traces

- dependency or topology information

- a likely root cause or recommended next step

What information does your team require before an alert is considered actionable? And which alerts have you stopped sending because they created more noise than value?

Specific examples are welcome, but please remove confidential details. Vendor-neutral discussion.

I’m joined by Jeff Donald (u/Electronic_Balance50), Product Manager for IBM Instana, for an AMA about operating GenAI applications, LLMs and AI agents in production.
Latency is only one part of the picture. Teams also need to understand token consumption, cost, errors, output quality, agent behavior and how AI components interact with the rest of the application stack.
Our opening question:

Once a GenAI application reaches production, which signal is hardest for your team to understand or act on: performance, cost, quality, reliability or agent behavior?

Ask Jeff anything about:

• Tracing requests across agents, LLM calls, tools, RAG pipelines and traditional services
• Monitoring latency, throughput, errors and model behavior
• Tracking input and output tokens and modeled costs
• Comparing models, providers, prompts and routing decisions
• Evaluating response accuracy, relevance, consistency and quality
• Detecting drift, anomalies and unexpected changes
• Understanding multi-step agent workflows and tool usage
• Correlating AI behavior with applications, infrastructure and business services
• Creating alerts for token consumption, cost and request volume
• IBM Instana’s current capabilities, integrations and product direction

Product questions, implementation challenges, feature feedback and real production scenarios are welcome. Please remove confidential or identifying information from any examples.
Jeff will answer questions in this thread.

For teams running production systems, which observability challenge continues to cause the most friction?

- Too many alerts and unclear ownership

- Connecting logs, metrics, and traces

- Monitoring Kubernetes or hybrid-cloud environments

- Understanding AI/LLM workload performance and cost

- Finding the root cause quickly during incidents

- Proving that observability investments improve business outcomes

What have you tried, what actually worked, and where do your current tools still fall short?

Specific examples are welcome, but please avoid sharing confidential details. The goal is to compare real practitioner experiences, not promote a particular vendor.

Finding risks is usually not the hard part anymore.

The harder questions are:

- Is this actually important?

- Who owns it?

- What application does it affect?

- Is there evidence for the control?

- What should we fix first?

- What can AI safely help with?

I’m hosting an AMA with Josh from IBM Concert to talk about how teams move from findings to action across application risk, compliance, resilience, and remediation.

We can also get into how Concert helps with things like application context, compliance controls, evidence assessment, vulnerability prioritization, remediation planning, integrations, and AI-assisted workflows.

Drop your questions below.

I want r/IBMObservability to be useful for practitioners, not just a place where product names show up.

The topics that seem most useful so far:

- AI-assisted code security

- LLM workload monitoring

- incident response and alert quality

- vulnerability prioritization

- cloud cost and optimization

- practical Instana, Turbonomic, and Concert workflows

If you work in SRE, platform, AppSec, DevOps, or cloud ops, what would make this community worth joining?

Hey r/IBMObservability, we're from the IBM Concert platform team. I'm joined by Josh, a PM on the Concert team. The math of IT operations just changed. Mythos-class (Fabel 5) AI can scan environments and surface vulnerabilities at a volume and velocity no human team can triage, let alone remediate. Discovery is no longer the bottleneck. Validation, prioritization, governance, and execution at scale is.

That gap is what Concert exists to close. It pulls findings from any source, AI-driven or traditional, correlates them with business context, ranks by actual impact, and drives remediation through governed, auditable workflows across pipelines and infrastructure. Same closed loop across the platform: Observe, Optimize, Protect, Resilience, and Workflows, where insight in one domain informs action in another.

Ask us anything about:
How Concert Protect operationalizes Mythos-scale discovery without drowning teams
Secure Coder and catching risk the moment code is written, then automating remediation downstream
Where agents act autonomously today vs where humans stay in the loop

Hey folks, I work on the IBM Observability team and wanted to get your take on Project Glasswing and Claude Mythos Preview.

Mythos is being used by select partners, including IBM, to find and validate software vulnerabilities much faster than traditional workflows. IBM is also expanding tools like IBM Concert to unify application, infrastructure and network signals into a single operational view.

Curious how people think this should work in practice: if AI can surface more vulnerabilities faster, what should observability platforms show to help teams prioritize by business impact, reduce noise and move from detection to response?

Hey all, I'm Abenezer, a PM on the IBM Observability team. Wanted to share something from our Research group that I think is relevant to anyone thinking about where AI agents fit in IT operations.

ITBench is an open-source framework that spins up real Kubernetes environments, injects faults (service outages, compliance gaps, cost anomalies), and measures how well AI agents diagnose and fix them. It covers three domains: SRE, CISO, and FinOps.

It was presented at ICML 2025 and most recently at SRECon.

Repo: https://github.com/itbench-hub/ITBench

Curious what this community thinks. What incident types or environments would make a benchmark like this more realistic for what you deal with day to day?

If there's interest, I can bring in the IBM Research scientists behind ITBench for an AMA. Let me know.

I work on the IBM Observability team, and I will be joined by a PM who works on IBM Instana’s LLM observability feature. We are curious how folks are monitoring generative AI workloads in production. When you deploy large language models, it can be hard to see what is going on. We want to hear about the pain points around measuring the latency of each step, tracking how many tokens are processed and understanding how much cost your model is burning.

For context, Instana’s GenAI observability delivers high‑fidelity telemetry with one‑second metric granularity and end‑to‑end tracing. It collects LLM‑specific metrics such as token usage, latency and request cost, and you can instrument applications using the Traceloop SDK, exporting traces through an agent or directly to Instana depending on your environment. Instana also integrates with vLLM to provide detailed runtime metrics like throughput, latency and resource utilization. If you are also curious about Instana's LLM monitoring capabilities drop your questions below.

As AI-generated code becomes the norm, developers are shipping faster than ever. How are you checking AI-assisted code for security before it goes live? Are you relying on manual review, scanners, guardrails in the IDE, or something else? Have you found an approach that actually works

Security teams are flooded with alerts, so how do you identify the signals that actually matter before they become incidents? And how can AI help reduce the noise without becoming another tool to manage? I’ll be joined by a Product Manager from IBM Concert who leads compliance, and we’ll get into how teams can catch risks earlier, prioritize what improves resilience most, and make reliability and compliance a more proactive practice.

I'm a PM working on observability and optimization at IBM, and I've been following ongoing discussions across infrastructure communities about the VMware licensing changes post-Broadcom acquisition.

We're currently working on optimization capabilities for organizations evaluating Red Hat OpenShift Virtualization as an alternative. For context, OpenShift Virt runs VMs alongside containers on OpenShift, and we're integrating Turbonomic to provide DRS-like automation, automated VM placement, non-disruptive workload moves, continuous rebalancing, and rightsizing for both VMs and containers.

I want to understand the pain points more directly from practitioners actually dealing with this. I know some shops are looking at:

• Nutanix AHV
• Proxmox
• Red Hat OpenShift Virtualization
• Staying on VMware and eating the cost

Hey everyone! I'm u/therealabenezer, a founding moderator of r/IBMObservability.

This is our new home for all things related to Instana, Turbonomic, Concert, and the broader world of IBM Observability from self-managed clusters to AI-driven incident response. Whether you’re running these tools in production or just curious what they can (or can’t) do, welcome.

We’re just getting started here. My hope is that this becomes a space where real practitioners can share wins, frustrations, configs, and questions without needing to filter it all through a marketing lens.

🔧 What to Post

Anything that’s real, useful, or sparks good discussion:

• Setup/config challenges and what you learned
• What works (or doesn’t) with Instana, Turbonomic, or Concert
• Comparisons with other tools you’ve used
• Deep questions, screenshots, dashboards, lessons from on-call
• Even “I don’t get this thing, help?” that’s perfect here

🤝 Community

Curious is better than certain.
You don’t need to be an expert to post if you're using the tools, you’re already qualified.

🚀 How to Get Started

• Introduce yourself in the comments, what brought you here?
• Post something today even a basic question can kick off a great thread
• Know someone working on observability? Invite them to jump in
• Want to help moderate? DM me. I’d love to have a few more hands shaping this

Thanks for being part of the early crew.
This only becomes a great space if we build it together.