{
  "schema_version": "1.1",
  "id": "atlas-observability",
  "slug": "observability",
  "title": "Observability",
  "url": "https://feed7.dev/atlas/observability",
  "current_answer": null,
  "implementation_consequence": null,
  "agent_context": null,
  "confidence": "auto_collected",
  "last_verified": null,
  "last_updated": null,
  "evidence": [
    {
      "schema_version": "1.1",
      "id": "auto-179f4fe13f",
      "slug": "ai-gateway-logs-now-have-a-dedicated-page-179f4fe13f",
      "url": "https://feed7.dev/p/ai-gateway-logs-now-have-a-dedicated-page-179f4fe13f",
      "title": "AI Gateway logs now have a dedicated page",
      "why_included": "Trace cost, tokens, latency, routing, cache use, and provider fallbacks by request ID, then export filtered runs for analysis.",
      "summary": "AI Gateway’s dedicated logs expose per-request cost, tokens, latency, routing, and provider fallbacks, making agent failures and spend anomalies easier to trace.",
      "practical_implication": "Use filters and request-ID search to debug agent failures or compare routing behavior. Filtered views are shareable by URL and exportable as CSV or JSON, which makes targeted cost and reliability analysis easier.",
      "agent_context": "The new **Logs page** lists gateway requests with cost, token counts, duration, model, provider, and region. Request details include time to first token, cache and reasoning tokens, policy flags, and the ordered fallback path.\n\nUse filters and request-ID search to debug agent failures or compare routing behavior. Filtered views are shareable by URL and exportable as **CSV or JSON**, which makes targeted cost and reliability analysis easier.\n\nThe page records what happened but does not define alerts, retention, or automated remediation. Diagnosis still requires interpreting provider failures, routing-budget exhaustion, and latency in the context of the agent run.",
      "source": {
        "name": "Vercel",
        "url": "https://vercel.com/changelog/ai-gateway-logs",
        "published_at": "2026-07-31T00:00:00.000Z"
      },
      "source_class": "blog_post",
      "content_type": "Engineering Post",
      "layer": "infra",
      "domains": [
        "coding",
        "data"
      ],
      "topics": [
        "observability",
        "gateways",
        "agent-reliability"
      ],
      "verification": {
        "status": "official_source",
        "label": "Official Source",
        "method": "source_feed",
        "verified_at": null
      },
      "uncertainty": [],
      "connected_context": null,
      "lifecycle": "New",
      "published_at": "2026-07-31T00:00:00.000Z",
      "modified_at": "2026-07-31T00:00:00.000Z",
      "supersedes": [],
      "expires_at": null,
      "formats": {
        "html": "https://feed7.dev/p/ai-gateway-logs-now-have-a-dedicated-page-179f4fe13f",
        "json": "https://feed7.dev/p/ai-gateway-logs-now-have-a-dedicated-page-179f4fe13f.json",
        "markdown": "https://feed7.dev/p/ai-gateway-logs-now-have-a-dedicated-page-179f4fe13f.md"
      }
    }
  ],
  "conflicting_sources": [],
  "superseded_claims": [],
  "corpus_evidence": [
    {
      "title": "TokTier: Exact Stateful Tokenization for Agentic LLM Serving",
      "url": "https://arxiv.org/abs/2607.29678v1",
      "source_name": "arXiv",
      "published_at": "2026-07-31T17:56:30+00:00",
      "summary": "TokTier targets a hidden agent-serving bottleneck: repeatedly tokenizing long transcripts after small tool results. Its stateful repair cut median vLLM time to first token by 16–34% in reported tests."
    },
    {
      "title": "AI Gateway now supports team and project spend budgets",
      "url": "https://vercel.com/changelog/ai-gateway-spend-budgets-and-alerts",
      "source_name": "Vercel",
      "published_at": "2026-07-31T17:00:00+00:00",
      "summary": "AI Gateway can now enforce spend caps across a team, project, or API key, giving agent workloads layered cost controls instead of relying on per-key limits alone."
    },
    {
      "title": "fighting slop with slop — Vaibhav Gupta, Boundary",
      "url": "https://www.youtube.com/watch?v=AMiyLItEtLA",
      "source_name": "AI Engineer",
      "published_at": "2026-07-31T01:11:30+00:00",
      "summary": "Boundary replaces blanket code review with stable architecture rules, agent-generated tests, transcript inspection, execution traces, and human triage—a concrete harness pattern, not a license to skip verification."
    },
    {
      "title": "How Forward Deployed Engineering is done at Factory — Eno Reyes",
      "url": "https://www.youtube.com/watch?v=wpOA-UXynoM",
      "source_name": "AI Engineer",
      "published_at": "2026-07-28T22:00:06+00:00",
      "summary": "Factory’s deployed-engineering model treats agent adoption as workflow design: instrument the path from signal to deploy, build validators, and tie autonomy to measurable business outcomes."
    },
    {
      "title": "MemLens: A Value-Aware Memory Management System with Interactive Analytics for LLM-based Agents",
      "url": "https://arxiv.org/abs/2607.25992v1",
      "source_name": "arXiv",
      "published_at": "2026-07-28T17:08:56+00:00",
      "summary": "MemLens scores individual agent memories, stores them by value, and exposes quality, latency, and token tradeoffs—an inspectable alternative to retaining every interaction equally."
    },
    {
      "title": "From Signal to PR: Anatomy of a Self-Improving Agent — Jason Lopatecki, Arize",
      "url": "https://www.youtube.com/watch?v=9HbzAWnKbo4",
      "source_name": "AI Engineer",
      "published_at": "2026-07-24T20:15:32+00:00",
      "summary": "Agents can turn production traces, evals, repository context, and observability skills into investigated issues or draft fixes. Human review remains more important as changes grow."
    },
    {
      "title": "Core dump epidemiology: fixing an 18-year-old bug",
      "url": "https://openai.com/index/core-dump-epidemiology-data-infrastructure-bug",
      "source_name": null,
      "published_at": null,
      "summary": "OpenAI debugged rare infrastructure crashes by analyzing core dumps at fleet scale, tracing them to a hardware fault plus an 18-year-old software bug. A useful pattern for hunting non-reproducible failures."
    },
    {
      "title": "Agent Runs now available in the Vercel MCP and CLI",
      "url": "https://vercel.com/changelog/agent-runs-vercel-mcp-cli",
      "source_name": null,
      "published_at": null,
      "summary": "Your coding agent can now pull its own Agent Runs traces—reasoning, tool calls, token usage—from Vercel via MCP or CLI, so it can debug its runs and refine skills from real production behavior."
    },
    {
      "title": "Online Safety Monitoring for LLMs",
      "url": "https://arxiv.org/abs/2607.02510v1",
      "source_name": null,
      "published_at": null,
      "summary": "A deliberately simple online safety monitor — threshold an external verifier's signal, calibrate via risk control — matches sequential-hypothesis-testing monitors on math-reasoning and red-teaming datasets."
    }
  ]
}