One Kernel, Zero Sidecars: Tracing AI Workloads Without an Agent on Every Host
The article discusses the challenges of using traditional observability agents for AI workloads, particularly in large fleets. It highlights the overhead costs associated with running an agent on every host and introduces kernel-level tracing as a more efficient alternative. The use of eBPF for tracing GPU workloads is presented as a solution that minimizes resource consumption while providing necessary observability data.
- ▪OpenAI uses Datadog for tracing inside its Codex agent, which requires an agent on every host.
- ▪Kernel-level tracing with eBPF can provide necessary data without the overhead of multiple agents.
- ▪At scale, the resource costs of running observability agents can become significant, impacting overall performance.
DEV.to (Top) files mainly under programming. We currently carry 4,924 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
inspect →
Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | DEV.to (Top) |
| Canonical URL | https://dev.to/ingero/one-kernel-zero-sidecars-tracing-ai-workloads-without-an-agent-on-every-host-50bl |
| Publication time | Mon, 18 May 2026 13:00:00 +0000 |
| Retrieval time | 2026-05-18T13:04:56.494Z |
| Last seen | 2026-05-18T13:04:56.494Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | E_D24A26rQaw |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3853036) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Ingero Team Posted on May 18 • Originally published at ingero.io One Kernel, Zero Sidecars: Tracing AI Workloads Without an Agent on Every Host #linux #devops #observability #monitoring Per-host overhead multiplied across N hosts, vs. one kernel-level instrumentation per host. The math at fleet scale is harder to argue with than the marketing one. TL;DR Wolfe Research disclosed this week that OpenAI uses Datadog for tracing inside its Codex agent.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at DEV.to (Top).