When your AI agents start failing in production, you’ll wish you’d instrumented them yesterday.
IBM® Instana® now traces every prompt, token, and agent decision the same way it traces every microservice request. Clear Technologies gets it running in your environment in weeks, not quarters.
If you’ve been on call long enough, you’ve watched this movie before.
Between 2018 and 2022, every serious engineering org spent a brutal stretch trying to figure out why their freshly-decomposed microservices kept lighting up the on-call rotation. The applications worked in dev. They worked in staging. They fell apart in production because nobody could see across them. The fix was a new generation of observability — distributed tracing, dependency mapping, real-time metric collection — that closed the gap between “the code runs” and “we know what the code is doing.”
That gap is open again. This time the layer is agentic AI.
Most enterprise teams have already shipped at least one agentic workflow into production — a LangChain chain, a LangGraph loop, a CrewAI multi-agent system, a custom orchestrator built on Amazon Bedrock or IBM watsonx.ai. The early demos went well. Then the chains started looping silently. Then a single LLM provider price change doubled token spend in a week and nobody could attribute the cost to a specific workflow. Then an agent started hallucinating tool calls in a way that looked fine in logs but tanked a downstream API.
The honest answer most teams give right now is: we patched some custom OpenTelemetry instrumentation around the LLM calls and we mostly know what’s happening.
Which is exactly what the 2019 answer was for microservices.
The teams that figured out microservices observability fastest weren’t the ones who built more custom dashboards. They were the ones who treated the new layer as part of the application stack — one that deserves the same fidelity of tracing, dependency mapping, and cost attribution as every other layer. The platforms that figured it out are now extending that discipline upward to AI agents and LLM workflows. The teams that haven’t started are about to spend a year rebuilding what the observability vendors already shipped.
At Think 2026, IBM put AI Agent and LLM Observability into public preview — a native extension of Instana’s automated observability into the agentic layer. The key word is native. This isn’t a sidecar product or a connector to bolt on. Instana auto-discovers GenAI workflows and agents the same way it auto-discovers JVMs, containers, and Kubernetes pods. (Read IBM’s announcement →)
Specifically, the platform now:
That’s the new layer. Underneath it, the proven foundation hasn’t changed:
This is the same observability discipline, extended to the layer that broke it. Not a new tool to learn. Not a parallel dashboard to maintain. The team that already runs Instana for microservices gets AI observability without standing up a second toolchain.
Two pieces of broader context worth knowing. First, Instana is now also integrated as Concert Observe inside IBM Concert — IBM’s operational fabric tying together Instana, Turbonomic, and CloudPak for AIOps under one operating model. If your roadmap includes automated remediation alongside observability, the pieces are already designed to talk to each other. Second, the platform took home 2026 Best Software IT Infrastructure Product from G2 and 2026 Buyer’s Choice from TrustRadius. Awards are validation, not the argument. The argument is the gap.
You can buy IBM Instana from any IBM partner. The platform is the platform. What changes is who shows up the morning of your first production incident.
Clear Technologies has been deploying IBM technology for more than 30 years. That’s not partnership theater — it’s the reason IBM’s Instana team picks up the phone when one of our enterprise customers hits a complex hybrid environment that needs a real implementation plan rather than a generic deployment guide. Three decades of relationships inside IBM is a moat other partners spend years trying to fake on a website.
The operational difference is bigger than the credential. The volume partners run hundreds of accounts on a tiered support model — you get a junior account manager, a quarterly check-in, and a deck. The transactional partners take your PO and disappear behind a ticket queue. We do not run that model. Every Clear Technologies client gets an engineer who knows their environment by name, who was in the kickoff call, and who will be in the war room the first time Instana fires an alert that matters.
What that looks like in practice:
IBM sells you the platform. We sit in the war room when the first incident hits.
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We’ve stood Instana up in enough environments to know what the first quarter looks like when it goes well.
Weeks 1–3 — Discovery. Map the existing observability stack. Identify what stays, what gets retired, and where Instana extends coverage rather than replaces it. For most enterprises this means cataloging the OpenTelemetry collectors already in place, the legacy APM tools that need a sunset plan, and the AI agent workflows that have already shipped without instrumentation. A Clear Technologies engineer leads the workshop. We document everything.
Weeks 3–8 — Deploy. Roll out the Instana agent across the agreed scope. The single-agent, auto-discovery architecture means most of the deployment time goes to access provisioning, change-management approvals, and team enablement — not configuration. Application Perspectives go live per team. AI agent instrumentation layers onto the existing LangChain or LangGraph workflows. OpenTelemetry collectors come under fleet management.
Weeks 8–12 — Operate. The first month of running data tells the truth about what was actually happening in your environment. Alert volume drops because Smart Alerts cuts through the noise the previous tooling generated. MTTR drops because the dependency map is finally real. Per-workflow LLM cost becomes visible — usually for the first time — with one or two agentic workflows typically responsible for the majority of spend.
Reference points from IBM Instana’s enterprise customer base: SIXT uses Instana to automate end-to-end infrastructure monitoring across rental operations; Mizuho deploys it to detect and resolve issues before they impact customers; Scuderia Ferrari HP runs it on the team’s mobile app, where the traffic profile spikes during race events.
No. Instana enhances OpenTelemetry rather than replacing it. The agent ingests OTel data through standard collectors, and as of February 2026, fleet management for OpenTelemetry Collectors is generally available inside Instana — centralized configuration, real-time collector health, controlled rollouts and rollbacks. Teams already standardized on OTel get enterprise-grade fleet control without rebuilding their instrumentation strategy.
Through OpenLLMetry-based instrumentation. Instana auto-discovers agents, chains, and tasks within a running LangChain or LangGraph application and maps them to the full-stack request trace. Each step shows prompts, outputs, tokens, latency, and cost — connected to the upstream user request and downstream API calls. The same model works for CrewAI, watsonx.ai, Bedrock, OpenAI, Groq, DeepSeek, and vLLM runtimes.
For a focused production rollout, four to eight weeks is typical. Enterprise-scale deployments with hybrid environments, mainframe integration, or complex compliance requirements run longer — usually 8 to 16 weeks to full operational handoff. The single-agent architecture means most of that time isn’t installation. It’s planning, enablement, and getting Application Perspectives configured for the right teams.
All three vendors have shipped LLM observability features in the last 18 months. The differentiator for Instana is native auto-discovery of agentic workflows — chains, multi-agent systems, and tool calls get mapped automatically into the same dependency graph as the microservices they sit inside. Combined with the OpenTelemetry fleet management released this year and the integration into IBM’s broader automation portfolio through Concert, the platform is positioned for enterprise AI operations rather than added on as a point feature. The honest comparison is portfolio versus point capability.
We implement, configure, and stand alongside your team — not just sell the license. IBM sells thousands of accounts. We have direct engineering relationships with a smaller book of clients, which means our engineers know your environment by name, sit in your war room during the first incidents, and have the IBM product-team escalation paths to resolve edge cases when they appear. The product is the same. The implementation experience isn’t.
Every observability shift in the last decade has happened the same way: the leading teams instrument the new layer before production incidents force them to, and everyone else spends a year catching up. Microservices in 2019. Kubernetes in 2021. AI agents in 2026.
The platform is IBM Instana. The partner who makes the deployment real is Clear Technologies.
