{ "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" } }, { "schema_version": "1.1", "id": "auto-fec4301fea", "slug": "cloud-agents-start-3x-faster-with-builds-fec4301fea", "url": "https://feed7.dev/p/cloud-agents-start-3x-faster-with-builds-fec4301fea", "title": "Cloud agents start 3x faster with builds", "why_included": "Prebuild deterministic dependencies, start session-fresh services separately, and trace each run to its build and commit SHA.", "summary": "Cursor Cloud Agents can start from continuously prepared environment snapshots instead of reinstalling each session. Internal time to first token improved 3x, with failed builds falling back to the last good state.", "practical_implication": "Builders should move deterministic setup into the install command, keep session-fresh services in the start command, and use team or environment secrets for private registries. Agent runs can be traced to exact builds and commit SHAs.", "agent_context": "Cursor’s new **builds** continuously prepare cloud-agent environments with repositories, dependencies, and install scripts already completed. Internally, environments booted **10x faster** and time to first token improved **3x**.\n\nBuilders should move deterministic setup into the install command, keep session-fresh services in the start command, and use team or environment secrets for private registries. Agent runs can be traced to exact builds and commit SHAs.\n\nThe gains are Cursor’s internal measurements, and user results will depend on repository setup. Builds run hourly by default; failed builds are rejected, leaving agents on the last working snapshot, which may be older than the default branch.", "source": { "name": "Cursor", "url": "https://cursor.com/blog/builds", "published_at": "2026-08-13T00:00:00.000Z" }, "source_class": "blog_post", "content_type": "Engineering Post", "layer": "infra", "domains": [ "coding" ], "topics": [ "cloud-agents", "sandboxing", "observability" ], "verification": { "status": "official_source", "label": "Official Source", "method": "source_feed", "verified_at": null }, "uncertainty": [], "connected_context": null, "lifecycle": "New", "published_at": "2026-08-13T00:00:00.000Z", "modified_at": "2026-08-13T00:00:00.000Z", "supersedes": [], "expires_at": null, "formats": { "html": "https://feed7.dev/p/cloud-agents-start-3x-faster-with-builds-fec4301fea", "json": "https://feed7.dev/p/cloud-agents-start-3x-faster-with-builds-fec4301fea.json", "markdown": "https://feed7.dev/p/cloud-agents-start-3x-faster-with-builds-fec4301fea.md" } }, { "schema_version": "1.1", "id": "auto-084c45658e", "slug": "vercel-cli-expands-support-for-dns-domains-and-project-c-084c45658e", "url": "https://feed7.dev/p/vercel-cli-expands-support-for-dns-domains-and-project-c-084c45658e", "title": "Vercel CLI expands support for DNS, domains, and project commands", "why_included": "Use structured JSON for DNS, domains, projects, observability, and members while retaining confirmation gates for risky actions.", "summary": "Vercel agents can now manage DNS, domains, project state, observability, and members through structured CLI commands, while purchases and destructive actions retain confirmation gates.", "practical_implication": "Agent workflows can consume structured JSON output, reducing dashboard handoffs and brittle terminal parsing. Update the CLI before adding these operations to deployment or maintenance scripts.", "agent_context": "The Vercel CLI adds dedicated commands for **DNS records**, **domain renewal**, project pause and resume, observability settings, and project membership. DNS updates cover common fields plus SRV-specific settings.\n\nAgent workflows can consume **structured JSON output**, reducing dashboard handoffs and brittle terminal parsing. Update the CLI before adding these operations to deployment or maintenance scripts.\n\nBillable and destructive operations still require **explicit confirmation**. That safeguard limits unattended automation, and domain renewal applies only to domains purchased through Vercel.", "source": { "name": "Vercel", "url": "https://vercel.com/changelog/vercel-cli-expands-support-for-dns-domains-and-project-commands", "published_at": "2026-08-21T00:00:00.000Z" }, "source_class": "blog_post", "content_type": "Engineering Post", "layer": "infra", "domains": [ "coding" ], "topics": [ "dev-ux", "tool-use", "observability" ], "verification": { "status": "official_source", "label": "Official Source", "method": "source_feed", "verified_at": null }, "uncertainty": [], "connected_context": null, "lifecycle": "New", "published_at": "2026-08-21T00:00:00.000Z", "modified_at": "2026-08-21T00:00:00.000Z", "supersedes": [], "expires_at": null, "formats": { "html": "https://feed7.dev/p/vercel-cli-expands-support-for-dns-domains-and-project-c-084c45658e", "json": "https://feed7.dev/p/vercel-cli-expands-support-for-dns-domains-and-project-c-084c45658e.json", "markdown": "https://feed7.dev/p/vercel-cli-expands-support-for-dns-domains-and-project-c-084c45658e.md" } }, { "schema_version": "1.1", "id": "auto-790b02624c", "slug": "vercel-cli-expands-commands-for-dns-domains-and-projects-790b02624c", "url": "https://feed7.dev/p/vercel-cli-expands-commands-for-dns-domains-and-projects-790b02624c", "title": "Vercel CLI expands commands for DNS, domains, and projects", "why_included": "CLI 59.6.2 adds structured JSON for new admin commands while retaining confirmation gates for billable or destructive actions.", "summary": "Vercel CLI 59.6.2 adds agent-friendly control over DNS, domains, projects, observability, and membership, with JSON output and confirmation gates for risky actions.", "practical_implication": "Coding agents can now handle more Vercel administration without dashboard automation. Use the JSON surfaces for parsing, while retaining the built-in confirmation boundary around billable or destructive actions.", "agent_context": "**Vercel CLI 59.6.2 or later** adds commands for DNS record updates, domain renewal, project pause and resume, observability settings, and project membership. Every new command supports **structured JSON output**.\n\nCoding agents can now handle more Vercel administration without dashboard automation. Use the JSON surfaces for parsing, while retaining the built-in confirmation boundary around **billable or destructive actions**.\n\nTerminal access increases the impact of an over-broad agent instruction. Scope credentials and permissions carefully, and do not treat confirmation prompts as a substitute for reviewing the intended target and change.", "source": { "name": "Vercel", "url": "https://vercel.com/changelog/vercel-cli-expands-commands-for-dns-domains-and-projects", "published_at": "2026-08-28T00:00:00.000Z" }, "source_class": "blog_post", "content_type": "Engineering Post", "layer": "infra", "domains": [ "coding" ], "topics": [ "tool-use", "dev-ux", "observability" ], "verification": { "status": "official_source", "label": "Official Source", "method": "source_feed", "verified_at": null }, "uncertainty": [], "connected_context": null, "lifecycle": "New", "published_at": "2026-08-28T00:00:00.000Z", "modified_at": "2026-08-28T00:00:00.000Z", "supersedes": [], "expires_at": null, "formats": { "html": "https://feed7.dev/p/vercel-cli-expands-commands-for-dns-domains-and-projects-790b02624c", "json": "https://feed7.dev/p/vercel-cli-expands-commands-for-dns-domains-and-projects-790b02624c.json", "markdown": "https://feed7.dev/p/vercel-cli-expands-commands-for-dns-domains-and-projects-790b02624c.md" } } ], "conflicting_sources": [], "superseded_claims": [], "corpus_evidence": [ { "title": "When AI Agents Pay and Sellers Monetize: Building x402 Apps on AWS — Anil Nadiminti, AWS", "url": "https://www.youtube.com/watch?v=qTZirYu9pr0", "source_name": "AI Engineer", "published_at": "2026-09-01T18:00:14+00:00", "summary": "AWS is separating agent payment policy from model execution: AgentCore handles wallets and limits, while WAF meters bot access. The useful pattern is deterministic spend control at the edge." }, { "title": "KV Cache-Aware Routing and P/D Disaggregation on Kubernetes — Yuchen Fama & Ashish Kamra, Red Hat", "url": "https://www.youtube.com/watch?v=YXowceUKYJI", "source_name": "AI Engineer", "published_at": "2026-08-27T14:00:06+00:00", "summary": "Agentic inference makes KV-cache locality and prefill pressure first-class scheduling concerns. Red Hat's case study improved latency and capacity, but P/D separation needs suitable traffic and fast networking." }, { "title": "GTM Engineering: The Technical Bits — Everett Berry, Clay", "url": "https://www.youtube.com/watch?v=UhCY231d0FQ", "source_name": "AI Engineer", "published_at": "2026-08-26T13:30:31+00:00", "summary": "Clay frames GTM agents as a data and orchestration problem: resolve entities, evaluate providers, isolate agent-written state, and account for delayed syncs before automating decisions." }, { "title": "Agent Frameworks Considered Harmful — Rémi Louf, .txt", "url": "https://www.youtube.com/watch?v=KHudyx5wW3U", "source_name": "AI Engineer", "published_at": "2026-08-22T16:30:39+00:00", "summary": "Reliable background agents need runtime primitives more than elaborate framework abstractions: durable events, queues, versioned inputs, causal traces, diffs, and replay." }, { "title": "FinOps for AI Agents: Who Spent All the Tokens? — Tisha Chawla & Susheem Koul, Microsoft", "url": "https://www.youtube.com/watch?v=GJX19pNhmSw", "source_name": "AI Engineer", "published_at": "2026-08-22T14:30:07+00:00", "summary": "Run-level policies cut benchmarked agent spend while completing more runs, suggesting builders should steer loops, context, and tool output before enforcing hard caps." }, { "title": "Why Your Enterprise Tech Stack Isn’t Ready for AI Agents — Christopher Lovejoy & Saul Howard", "url": "https://www.youtube.com/watch?v=mav15aW9lLM", "source_name": "AI Engineer", "published_at": "2026-08-19T18:30:15+00:00", "summary": "Enterprise agent requirements should shape the foundation, not be bolted onto a working POC. Design audit, sensitive-data access, human escalation, and evals into the architecture." }, { "title": "Infra behind Krea 2: How to train and serve at scale — Gabriel Jorge Menezes, Krea.ai", "url": "https://www.youtube.com/watch?v=byn9PURoBNY", "source_name": "AI Engineer", "published_at": "2026-08-18T17:00:05+00:00", "summary": "Krea’s K2 infrastructure shows why GPU utilization alone misleads: tensor-core, temperature, interconnect, and checkpoint metrics are what keep large training runs productive." }, { "title": "How Web Data Infrastructure Powers the Next Generation of AI — Patricija Žemaitytė, Oxylabs", "url": "https://www.youtube.com/watch?v=1UmZHb_E_SM", "source_name": "AI Engineer", "published_at": "2026-08-14T17:00:37+00:00", "summary": "Fresh web retrieval becomes product infrastructure once agents need sub-second responses and sustained scale. The talk’s failed client demo shows why dev latency alone says little about production resilience." }, { "title": "Firetiger joins Cursor", "url": "https://cursor.com/blog/firetiger", "source_name": "Cursor", "published_at": "2026-08-13T19:00:00+00:00", "summary": "Cursor is bringing Firetiger’s production-monitoring agents in-house, aiming to connect code generation with rollout checks and incident investigation. Change Monitors are the first named upcoming feature." }, { "title": "Improving Agents is a Data Mining Problem — Vivek Trivedy, LangChain", "url": "https://www.youtube.com/watch?v=CvRngaQZQ3Y", "source_name": "AI Engineer", "published_at": "2026-08-12T19:00:01+00:00", "summary": "Agent quality problems often live in traces, not source code. Mine production runs for recurring failures, compaction drift, and counterfactual model choices, then validate changes with replayable evals." }, { "title": "Set up coding agents in one command with AI Gateway", "url": "https://vercel.com/changelog/set-up-coding-agents-in-one-command-with-ai-gateway", "source_name": "Vercel", "published_at": "2026-08-12T00:00:00+00:00", "summary": "Vercel’s setup command can route nine coding-agent clients through one gateway for shared models, budgets, policy, and traces. Review the in-place config edits before adopting it." }, { "title": "Always-on agents run production without the on-call tax — Justin Smith, Resolve AI", "url": "https://www.youtube.com/watch?v=vSx5IULvBns", "source_name": "AI Engineer", "published_at": "2026-08-09T14:45:06+00:00", "summary": "Always-on production agents can turn releases and operational events into tailored monitoring tasks. The useful pattern is persistent context plus scoped triggers, checks, and escalation." }, { "title": "Taxonomy-Driven Analysis of Open-Source AI Risk Mitigation Tools", "url": "https://arxiv.org/abs/2608.07446v1", "source_name": "arXiv", "published_at": "2026-08-07T17:33:09+00:00", "summary": "A taxonomy-based audit maps open-source LLM safety tools to enterprise risks, finding strong technical coverage but major governance, legal, regulatory, and financial gaps." }, { "title": "TRAJDEBUG: Tracing Error Lifecycle to Identify Critical Failures in Long-Horizon Agent Trajectories", "url": "https://arxiv.org/abs/2608.06346v1", "source_name": "arXiv", "published_at": "2026-08-06T17:51:20+00:00", "summary": "TrajDebug tracks whether errors persist, resolve, or cause terminal failure, offering a sharper way to debug long coding-agent runs than flagging every local mistake." }, { "title": "Export AI Gateway traces with Vercel Drains", "url": "https://vercel.com/changelog/export-ai-gateway-traces-with-vercel-drains", "source_name": "Vercel", "published_at": "2026-08-05T05:00:00+00:00", "summary": "Vercel AI Gateway can export per-request OpenTelemetry traces, exposing routing, retries, latency, tokens, cost, and attribution without sending prompt or completion content." }, { "title": "Measure time between steps in Vercel Workflows", "url": "https://vercel.com/changelog/measure-time-between-steps-in-vercel-workflows", "source_name": "Vercel", "published_at": "2026-08-05T00:00:00+00:00", "summary": "Vercel’s workflow trace viewer can measure gaps between steps, helping distinguish slow task execution from delayed starts and queue waits in agent workflows." }, { "title": "GEM Training: How Meta Doubled the Efficiency of Its LLM-Scale Ads Foundation Model", "url": "https://engineering.fb.com/2026/08/03/ml-applications/training-gem-at-llm-scale-meta-ads-recommendation-foundation-model/", "source_name": "Meta AI", "published_at": "2026-08-03T18:00:17+00:00", "summary": "Meta doubled GEM’s training efficiency by co-designing recommendation-specific kernels, precision, parallelism, networking, and memory. The takeaway is to profile workload shape before reusing an LLM training stack." }, { "title": "How Factory scaled its cloud backend to tens of millions of daily requests on Vercel", "url": "https://vercel.com/blog/how-factory-scaled-its-cloud-backend-to-tens-of-millions-of-daily-requests", "source_name": "Vercel", "published_at": "2026-08-03T04:00:00+00:00", "summary": "Factory runs its full cloud backend in one Next.js app while agents deploy internal tools through Vercel APIs. The case study is useful evidence for keeping agent-operated infrastructure programmable and guarded." }, { "title": "How Factory scaled its cloud backend to one billion monthly requests on Vercel", "url": "https://vercel.com/blog/how-factory-scaled-its-cloud-backend-to-one-billion-monthly-requests-on-vercel", "source_name": "Vercel", "published_at": "2026-08-03T04:00:00+00:00", "summary": "Factory runs API routes, middleware, and webhooks in one Next.js backend at one billion daily requests and 350ms p95. Its agents also deploy internal tools through Vercel APIs under review guardrails." }, { "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": "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." }, { "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": "PostHog/posthog", "url": "https://github.com/PostHog/posthog", "source_name": "GitHub", "published_at": null, "summary": "PostHog combines product telemetry, LLM tracing, and agent-driven diagnosis in one platform, with MCP access for Claude Code, Cursor, and other compatible agents." }, { "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": "Osmantic/ODS", "url": "https://github.com/Osmantic/ODS", "source_name": "GitHub", "published_at": null, "summary": "ODS packages local inference, agents, RAG, workflows, image generation, and operations into one installable stack, reducing homelab integration work at the cost of a large dependency surface." } ] }