{ "schema_version": "1.1", "id": "atlas-enterprise", "slug": "enterprise", "title": "Enterprise", "url": "https://feed7.dev/atlas/enterprise", "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-bb03b50141", "slug": "introducing-cursor-router-bb03b50141", "url": "https://feed7.dev/p/introducing-cursor-router-bb03b50141", "title": "Introducing Cursor Router", "why_included": "Evaluate model routing by cost per shipped change, using repository-specific quality, latency, and code-retention signals.", "summary": "Cursor Router classifies coding requests and selects models by task and cost. Cursor reports lower spend in production tests, but the strongest evidence is limited to its own traffic and metrics.", "practical_implication": "Teams should test routing against cost per shipped change, not token price alone. Cursor’s classifier considers query, context, complexity, domain, model behavior, and cache misses; admins can set defaults and restrict models or modes.", "agent_context": "Cursor Router was trained on **600k+ live requests** and evaluated through A/B tests covering **millions of requests**. Cursor reports frontier-level satisfaction at about **60% lower cost** and offers Intelligence, Balance, and Cost modes.\n\nTeams should test routing against cost per shipped change, not token price alone. Cursor’s classifier considers query, context, complexity, domain, model behavior, and cache misses; admins can set defaults and restrict models or modes.\n\nThe reported **30–50% early-access savings** came from three high-volume accounts and used Opus 4.8 pricing as the counterfactual. Quality relies on Cursor’s satisfaction and code keep-rate signals, so results may not transfer to other harnesses or workloads.", "source": { "name": "Cursor", "url": "https://cursor.com/blog/router", "published_at": "2026-07-22T00:00:00.000Z" }, "source_class": "blog_post", "content_type": "Engineering Post", "layer": "tools", "domains": [ "coding" ], "topics": [ "model-selection", "coding-agents", "enterprise" ], "verification": { "status": "official_source", "label": "Official Source", "method": "source_feed", "verified_at": null }, "uncertainty": [], "connected_context": null, "lifecycle": "New", "published_at": "2026-07-22T00:00:00.000Z", "modified_at": "2026-07-22T00:00:00.000Z", "supersedes": [], "expires_at": null, "formats": { "html": "https://feed7.dev/p/introducing-cursor-router-bb03b50141", "json": "https://feed7.dev/p/introducing-cursor-router-bb03b50141.json", "markdown": "https://feed7.dev/p/introducing-cursor-router-bb03b50141.md" } }, { "schema_version": "1.1", "id": "auto-fb21820d80", "slug": "from-assistance-to-execution-how-enterprises-put-ai-to-w-fb21820d80", "url": "https://feed7.dev/p/from-assistance-to-execution-how-enterprises-put-ai-to-w-fb21820d80", "title": "From assistance to execution: How enterprises put AI to work", "why_included": "The adoption narrative lacks sample size, methodology, workflow examples, and implementation guidance for improving an agent session.", "summary": "OpenAI’s research frames enterprise AI adoption as a shift from assistance toward agent execution, with ChatGPT and Codex as examples rather than implementation guidance.", "practical_implication": "Builders should treat this as evidence of organizational demand for agents that execute work, then look for the underlying research before changing product or deployment plans.", "agent_context": "OpenAI reports that enterprises are adopting **agentic AI** through products including **ChatGPT and Codex**, and says a group it calls frontier firms is moving ahead in adoption.\n\nBuilders should treat this as evidence of organizational demand for agents that execute work, then look for the underlying research before changing product or deployment plans.\n\nThe supplied material provides no sample size, methodology, adoption rates, workflow examples, or definition of a frontier firm, so the size of the claimed gap is unclear.", "source": { "name": "OpenAI", "url": "https://openai.com/index/how-enterprises-put-ai-to-work", "published_at": "2026-08-12T00:00:00.000Z" }, "source_class": "blog_post", "content_type": "Official Release", "layer": "industry", "domains": [ "coding" ], "topics": [ "adoption", "enterprise" ], "verification": { "status": "official_source", "label": "Official Source", "method": "source_feed", "verified_at": null }, "uncertainty": [], "connected_context": null, "lifecycle": "New", "published_at": "2026-08-12T00:00:00.000Z", "modified_at": "2026-08-12T00:00:00.000Z", "supersedes": [], "expires_at": null, "formats": { "html": "https://feed7.dev/p/from-assistance-to-execution-how-enterprises-put-ai-to-w-fb21820d80", "json": "https://feed7.dev/p/from-assistance-to-execution-how-enterprises-put-ai-to-w-fb21820d80.json", "markdown": "https://feed7.dev/p/from-assistance-to-execution-how-enterprises-put-ai-to-w-fb21820d80.md" } }, { "schema_version": "1.1", "id": "auto-d34e82eae2", "slug": "how-loveholidays-is-making-everyone-a-builder-with-codex-d34e82eae2", "url": "https://feed7.dev/p/how-loveholidays-is-making-everyone-a-builder-with-codex-d34e82eae2", "title": "How loveholidays is making everyone a builder with Codex", "why_included": "It offers no workflow, governance details, adoption data, or measured outcomes that a solo builder could reuse next session.", "summary": "loveholidays is extending Codex beyond software teams so more employees can turn ideas into products, but the supplied material gives no implementation details or measured outcomes.", "practical_implication": "Builders evaluating broader agent access should consider which product work non-engineers can safely own and what review boundaries they need.", "agent_context": "loveholidays says it is using **Codex** to make software development accessible **across the business**, not only within engineering.\n\nBuilders evaluating broader agent access should consider which product work non-engineers can safely own and what review boundaries they need.\n\nThe material provides no workflow, governance model, adoption numbers, or measured delivery gains, so it is evidence of direction rather than a reusable playbook.", "source": { "name": "OpenAI", "url": "https://openai.com/index/loveholidays", "published_at": "2026-08-26T00:00:00.000Z" }, "source_class": "blog_post", "content_type": "Official Release", "layer": "industry", "domains": [ "coding" ], "topics": [ "adoption", "enterprise" ], "verification": { "status": "official_source", "label": "Official Source", "method": "source_feed", "verified_at": null }, "uncertainty": [], "connected_context": null, "lifecycle": "New", "published_at": "2026-08-26T00:00:00.000Z", "modified_at": "2026-08-26T00:00:00.000Z", "supersedes": [], "expires_at": null, "formats": { "html": "https://feed7.dev/p/how-loveholidays-is-making-everyone-a-builder-with-codex-d34e82eae2", "json": "https://feed7.dev/p/how-loveholidays-is-making-everyone-a-builder-with-codex-d34e82eae2.json", "markdown": "https://feed7.dev/p/how-loveholidays-is-making-everyone-a-builder-with-codex-d34e82eae2.md" } } ], "conflicting_sources": [], "superseded_claims": [], "corpus_evidence": [ { "title": "Run cloud agents on machines you manage", "url": "https://cursor.com/blog/self-hosted-machines", "source_name": "Cursor", "published_at": "2026-09-02T12:00:00+00:00", "summary": "Cursor cloud agents can now execute tools on machines and elastic pools inside your network while Cursor retains planning and inference. This unlocks internal access and custom hardware, not full self-hosting." }, { "title": "From Production Traffic to Post-Training: Building a Self-Hosted LLM That Covers the Corporate Request Mix", "url": "https://arxiv.org/abs/2609.01572v1", "source_name": "arXiv", "published_at": "2026-09-01T17:39:26+00:00", "summary": "A production-derived post-training recipe consolidated more than 200 internal apps onto one self-hosted model by training separate experts for distinct quality gaps, then merging them." }, { "title": "How law firm Gilbert + Tobin governs and scales AI with OpenAI", "url": "https://openai.com/index/gilbert-tobin", "source_name": "OpenAI", "published_at": "2026-09-01T01:00:00+00:00", "summary": "Gilbert + Tobin’s enterprise rollout pairs executive sponsorship and formal governance with human accountability, a useful reminder that agent adoption is an operating-model change." }, { "title": "Guardians of the State: An Air-Gapped AI Fortress for Consumer Data — Rachna Srivastava, DFPI", "url": "https://www.youtube.com/watch?v=2WZsT-znFTQ", "source_name": "AI Engineer", "published_at": "2026-08-29T15:00:25+00:00", "summary": "California’s financial regulator built an offline AI pipeline around replayable data, hardware-backed redaction, model routing, and one-way updates for court-defensible evidence." }, { "title": "Which AI startups actually land enterprise contracts? — Brian Lewis, Millennium", "url": "https://www.youtube.com/watch?v=7A65O-0lvKE", "source_name": "AI Engineer", "published_at": "2026-08-29T14:00:06+00:00", "summary": "Enterprise AI contracts are won on security, controls, integration, and support as much as model capability. Builders should make those operational surfaces part of the product early." }, { "title": "How do you diffuse AI into the real world? — Varun Shenoy, Long Lake", "url": "https://www.youtube.com/watch?v=B0fjR3yaZFU", "source_name": "AI Engineer", "published_at": "2026-08-28T17:30:33+00:00", "summary": "Real-world agent adoption depends on workflow redesign, operational traces, and hands-on enablement. Code-agent patterns help, but service work has messier exceptions and triggers." }, { "title": "Reverse-Engineering the AI Buyer — Aliisa Rosenthal, Acrew Capital", "url": "https://www.youtube.com/watch?v=wdTRsfw0KG0", "source_name": "AI Engineer", "published_at": "2026-08-26T13:00:06+00:00", "summary": "For AI startups scaling sales, automate intake, follow-up, security, and outbound before adding headcount. Keep the first design partners high-touch, and remove buyer work wherever possible." }, { "title": "The end of credential sprawl for agents", "url": "https://vercel.com/blog/the-end-of-credential-sprawl-for-agents", "source_name": "Vercel", "published_at": "2026-08-25T04:00:00+00:00", "summary": "Vercel Connect gives agents runtime-minted, task-scoped credentials instead of stored provider tokens, adding per-user identity, revocation, audit logs, and usage visibility." }, { "title": "Introducing the Admin plugin for ChatGPT Work and Codex", "url": "https://openai.com/index/introducing-admin-plugin", "source_name": "OpenAI", "published_at": "2026-08-25T00:00:00+00:00", "summary": "OpenAI’s Admin plugin lets workspace operators inspect usage and handle membership, permissions, limits, and admin requests from ChatGPT Work or Codex." }, { "title": "Coding Agents Don't Scale Themselves. Neither Do Your Teams. — Patrick Debois, Tessl", "url": "https://www.youtube.com/watch?v=zCJtYuqwm7E", "source_name": "AI Engineer", "published_at": "2026-08-22T16:00:06+00:00", "summary": "Agent adoption becomes a team-systems problem: improve shared context and harnesses, assign platform ownership, and measure fewer human interventions instead of individual prompt speed." }, { "title": "From Regulation to Implementation: A Critical Evaluation of LLM-Assisted Regulatory Compliance in Industry", "url": "https://arxiv.org/abs/2608.21317v1", "source_name": "arXiv", "published_at": "2026-08-21T17:27:13+00:00", "summary": "LLM compliance generation behaves differently under vague and strict schemas: vague artifacts need richer context, while rigid formats can stay consistent yet hallucinate." }, { "title": "Agentic SDLC at Uber — Uday Kiran Medisetty & Adam Huda, Uber", "url": "https://www.youtube.com/watch?v=17-YSUHo6Lk", "source_name": "AI Engineer", "published_at": "2026-08-21T13:00:06+00:00", "summary": "Uber’s agent adoption rests on shared gateways, ready-to-run environments, skills, and a context graph. The operational bottleneck is shifting from code generation to validation and capacity." }, { "title": "Vercel Agent is now available in Slack code channels", "url": "https://vercel.com/changelog/vercel-agent-is-now-available-in-slack-code-channels", "source_name": "Vercel", "published_at": "2026-08-20T00:00:00+00:00", "summary": "Vercel Agent can now investigate incidents, edit code, and review PRs inside shared Slack code channels, with approval gates and an audit trail for every action." }, { "title": "Offering Zero Data Retention for frontier models", "url": "https://openai.com/index/offering-zero-data-retention-for-frontier-models", "source_name": "OpenAI", "published_at": "2026-08-19T19:00:00+00:00", "summary": "OpenAI is retaining Zero Data Retention for eligible API customers and previewing private safety processing, relevant to teams sending sensitive context to frontier models." }, { "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": "Cursor is now a part of SpaceX", "url": "https://cursor.com/blog/joining-spacex", "source_name": "Cursor", "published_at": "2026-08-14T12:00:00+00:00", "summary": "SpaceX has acquired Cursor, tying the coding-agent vendor’s model roadmap to a larger compute provider. Cursor says the deal should produce stronger models at lower cost, but gives no pricing commitments." }, { "title": "How RingCentral builds AI-native work from engineering to ops", "url": "https://openai.com/index/ringcentral", "source_name": "OpenAI", "published_at": "2026-08-12T00:00:00+00:00", "summary": "RingCentral uses ChatGPT Work and Codex across engineering and operations, connecting AI product development with centralized operational knowledge." }, { "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": "AI Gateway is now available on AWS Marketplace", "url": "https://vercel.com/changelog/ai-gateway-is-now-available-on-aws-marketplace", "source_name": "Vercel", "published_at": "2026-08-05T04:00:00+00:00", "summary": "Vercel AI Gateway is available through AWS Marketplace, letting teams place inference on their AWS bill under annual private offers without changing per-token pricing." }, { "title": "Circles powers telco personalization with OpenAI technology", "url": "https://openai.com/index/circles", "source_name": "OpenAI", "published_at": "2026-08-03T00:00:00+00:00", "summary": "Circles reports measurable telco gains from combining the OpenAI API with Codex, but the supplied case-study material gives no baseline, methodology, or detail on the developer-efficiency claim." }, { "title": "Chat SDK now supports reactions and ephemeral messages on Teams", "url": "https://vercel.com/changelog/chat-sdk-reactions-and-ephemeral-messages-on-teams", "source_name": "Vercel", "published_at": "2026-07-31T00:00:00+00:00", "summary": "Chat SDK’s Teams adapter adds reactions, user-only ephemeral messages, custom token supply, and routing fixes useful for building safer agent approval and progress flows." }, { "title": "Advancing the price-performance frontier with GPT-5.6", "url": "https://openai.com/index/advancing-the-price-performance-frontier-with-gpt-5-6", "source_name": "OpenAI", "published_at": "2026-07-30T10:00:00+00:00", "summary": "OpenAI says GPT-5.6 Luna and Terra now cost less, which may change model-routing choices for agent workflows. The supplied material gives no prices or workload comparisons." }, { "title": "The Dirty Secret of Forward Deployed Engineering — Natalie Meurer, Sierra", "url": "https://www.youtube.com/watch?v=Byv311hdoHE", "source_name": "AI Engineer", "published_at": "2026-07-28T18:00:18+00:00", "summary": "Forward-deployed engineering is not one role but a stack of customer-accountable work. Coding agents now let those engineers carry field insight through to production changes." }, { "title": "How Forward Deployed Engineering is done at Decagon — Sunny Rekhi", "url": "https://www.youtube.com/watch?v=7wu2hsRfvV0", "source_name": "AI Engineer", "published_at": "2026-07-28T17:00:35+00:00", "summary": "Decagon splits deployment between configuring each customer’s agent and turning repeated enterprise requests into product features. The scarce skill is resisting brittle one-offs." }, { "title": "How Forward Deployed Engineering is done at Kepler — Vinoo Ganesh", "url": "https://www.youtube.com/watch?v=1OMHGsUZiqA", "source_name": "AI Engineer", "published_at": "2026-07-28T16:00:00+00:00", "summary": "Kepler frames forward deployment as product discovery: observe real work, ship the smallest useful fix, then turn repeated pain and customer vocabulary into durable product leverage." }, { "title": "Forward Deployed Engineering 101 — Kevin Bai, Anthropic, ex Palantir & Rippling Founding FDE", "url": "https://www.youtube.com/watch?v=KwhgfwOSToQ", "source_name": "AI Engineer", "published_at": "2026-07-28T15:00:06+00:00", "summary": "Forward-deployed engineering fits technical products sold to nontechnical buyers, but only when customer work composes shared platform primitives instead of creating bespoke codebases." }, { "title": "Regional inference now available on AI Gateway", "url": "https://vercel.com/changelog/regional-inference-now-available-on-ai-gateway", "source_name": "Vercel", "published_at": "2026-07-27T19:00:00+00:00", "summary": "AI Gateway can pin inference and retained provider data to the US or EU, giving agent builders one residency control with per-response verification across supported providers." }, { "title": "NTT DATA Group cuts incident analysis to 30 minutes with Codex", "url": "https://openai.com/index/ntt-data", "source_name": "OpenAI", "published_at": "2026-07-22T00:00:00+00:00", "summary": "NTT DATA reports using Codex and ChatGPT Enterprise across 9,000 employees, with incident analysis reduced to 30 minutes—a concrete enterprise adoption data point." }, { "title": "Introducing organizations for Cursor Enterprise", "url": "https://cursor.com/blog/organizations", "source_name": null, "published_at": null, "summary": "Cursor Enterprise adds organizations: a teams-within-org hierarchy with per-team budgets, model access controls, and spend and token analytics rolled up in one admin dashboard." }, { "title": "How agents are transforming work", "url": "https://openai.com/index/how-agents-are-transforming-work", "source_name": null, "published_at": null, "summary": "An OpenAI research paper argues AI agents now sustain longer, more complex tasks and lift productivity across roles. The vendor's own read on the delegation workflows agent-first builders already run daily." }, { "title": "Why B3 chose Android for secure AI-enabled productivity", "url": "https://blog.google/products-and-platforms/products/android-enterprise/b3-android-enterprise/", "source_name": null, "published_at": null, "summary": "Enterprise case study, not builder tooling: Brazil's B3 exchange rolled Android devices with built-in Gemini to about 1,000 employees in under two weeks, projecting 30% cost savings over a decade." }, { "title": "Forward Deployed Engineering at Cursor — Pauline Brunet", "url": "https://www.youtube.com/watch?v=APqXGyCoGW4", "source_name": null, "published_at": null, "summary": "Cursor’s FDE playbook treats agent deployment as co-development: choose measurable use cases, require customer ownership, avoid staff augmentation, and document the handoff." }, { "title": "HP Inc. launches Frontier strategic partnership with OpenAI", "url": "https://openai.com/index/hp-frontier-partnership", "source_name": null, "published_at": null, "summary": "HP Inc. is scaling an OpenAI Frontier partnership to deploy AI across customer experience, software development, and enterprise operations. An enterprise-adoption signal, nothing builders can use directly." }, { "title": "GPT-5.6 is now the preferred model in Microsoft 365 Copilot", "url": "https://openai.com/index/gpt-5-6-preferred-model-microsoft-365-copilot", "source_name": null, "published_at": null, "summary": "Microsoft 365 Copilot now defaults to GPT-5.6 across five work surfaces, signaling wider enterprise use without giving builders performance or rollout data." }, { "title": "Australian Payments Plus moves faster with ChatGPT and Codex", "url": "https://openai.com/index/australian-payments-plus", "source_name": null, "published_at": null, "summary": "Australian Payments Plus uses ChatGPT Enterprise and Codex in payments work while retaining human judgment. The material claims time and quality gains but provides no metrics or workflow detail." }, { "title": "CFOs and the new economics of AI", "url": "https://cursor.com/blog/cfo-council", "source_name": null, "published_at": null, "summary": "Cursor is convening a CFO Council on AI ROI, and its telemetry is the useful part: cost per agent request varies ~9x across model families, and 84% of power users run multiple models weekly." }, { "title": "Ask an AI expert: What exactly is the full stack?", "url": "https://blog.google/innovation-and-ai/technology/ai/full-stack-ai-explainer/", "source_name": null, "published_at": null, "summary": "Google explainer pitching its integrated stack — TPUs, Gemini, an enterprise agent platform, and app surfaces — as one system. Mostly positioning, but it maps where Antigravity and AI Studio sit." } ] }