Observability articles

Connect LLM production traces to evaluation runs

Keep release, dataset, evaluator, and sampling context connected as you investigate LLM failures and compare fixes with Polyaxon.

What are your ML jobs connecting to?

Trace image pulls, Git clones, S3 and GCS access, Hugging Face downloads, and artifact uploads across the lifecycle of Kubernetes jobs and sandboxes.

Beyond agent traces: State, artifacts, and recovery

Connect agent traces to checkpoint state, versioned artifacts, action receipts, and evaluations so failures lead to controlled recovery and reproducible improvements.

Design measurable AI agents before you build

Define agent outcomes, prohibited actions, events, evaluators, budgets, segments, and feedback loops before implementation begins.

Secure AIOps automation for ML platforms

Use AIOps to investigate ML incidents and automate low-risk remediation without giving an AI system unrestricted production access.

Designing a control plane for AI agents

Separate live agent requests from versioning, evaluation, policy, rollout, identity, evidence, and recovery across the application lifecycle.

SLOs for AI applications and agents

Define service-level objectives for AI quality, task success, safety, latency, availability, and cost using measurable user-centered indicators.

Measure AI agent workflow bottlenecks

Measure Polyaxon agent workflow bottlenecks across planning, queueing, execution, transfers, and evaluation using critical-path timing and quality-aware comparisons.

MCP observability: Monitor tools, resources, and context

Trace MCP discovery, sessions, resources, prompts, tool calls, permissions, errors, and agent outcomes across clients and servers.

How to evaluate LLM routers for cost, quality, and latency

Test LLM routing policies with task-level quality, cost, latency, fallbacks, route stability, and model-aware production evidence.

What is an AI gateway?

An AI gateway centralizes model access, routing, resilience, policy, cost controls, and telemetry across production LLM applications and agents.

Turn production traces into regression tests

Convert representative production failures into sanitized, reproducible evaluation cases that protect future LLM and agent releases.

AI agent tracing: How to debug tools, loops, and handoffs

AI agent tracing connects model calls, retrieval, tools, state, and handoffs so teams can explain failures, latency, cost, and outcomes.

Observe coding agents from prompt to sandbox execution

Use Polyaxon tracking, sandbox execution receipts, artifacts, and run comparison to investigate coding-agent failures and qualify changes.

Investigate runtime threats in Polyaxon agent workloads

Correlate Polyaxon run records with external runtime-security events to investigate agent workloads, preserve evidence, and coordinate containment.

OpenTelemetry Collector for ML platforms

Design OpenTelemetry Collector pipelines for ML services with clear receivers, processors, exporters, deployment patterns, and failure controls.

Monitoring vs. observability for ML systems

Separate monitoring from observability, then combine metrics, logs, traces, run context, and model evaluation into an effective ML operating model.

BTF for portable eBPF observability

Understand how BPF Type Format supports eBPF introspection, CO-RE portability, safer deployment, and kernel-level observability for ML infrastructure.

OpenTelemetry zero-code instrumentation

Use OpenTelemetry automatic instrumentation as a safe baseline, then add domain spans, stable attributes, sampling, and rollout controls for ML services.

Distributed tracing for ML platforms

Trace ML requests and operations across APIs, queues, schedulers, storage, model services, and external tools without losing context or causality.

Istio vs. Linkerd vs. Consul for ML platforms

Compare Istio, Linkerd, and Consul service mesh architectures by workload scope, traffic policy, identity, observability, operations, and ML fit.

OpenTelemetry for Java ML services

Instrument Java and Spring ML services with the OpenTelemetry agent, SDK, OTLP, domain spans, stable resource identity, and safe rollout controls.

What is OpenTelemetry?

Understand OpenTelemetry signals, APIs, SDKs, semantic conventions, OTLP, the Collector, and how they fit into an ML observability architecture.

Kubernetes service mesh for ML platforms

Decide whether a service mesh fits your ML platform, then design traffic policy, identity, observability, rollout, and failure behavior deliberately.