{ "$schema_doc": "https://gtmstacker.com/registry/schema/entry.schema.json", "stability": "emerging", "generator": "agentic-media-registry", "generated_at": "2026-10-07T00:00:00Z", "id": "com.gtmstacker.registry/tool/uber-adr", "type": "tool", "slug": "uber-adr", "canonical_url": "https://gtmstacker.com/registry/tool/uber-adr/", "title": "ADR (Agentic AI Detection and Response)", "description": "ADR (Agentic AI Detection and Response) is Uber's open-source platform for securing the AI agents a team runs: Discovery inventories the AI apps, agents, runtimes, and MCP servers on an endpoint; a Sensor normalizes telemetry from Claude Code, Cursor, Codex, Copilot CLI, and DeepSeek Harness; and a dual-agent Detection layer triages suspicious sessions. Ships ADR-Bench (300+ tasks, 134 MCP servers, 17 attack techniques). Apache-2.0; self-hostable. Free. In production at Uber; MLSys 2026.", "category": "mcp-agents", "tags": [ "mcp-agents", "ai-infrastructure", "agent-security", "governance", "observability", "self-hostable", "open-source" ], "status": "active", "revision": 1, "content_hash": "2f74f55d263f4dafc9ed718aa1958f532c083cf14b7c76a5198958bfcd771c8c", "date_published": "2026-10-07T00:00:00Z", "date_modified": "2026-10-07T00:00:00Z", "source": { "name": "uber/adr · X (@RoundtableSpace) / GitHub", "url": "https://github.com/uber/adr" }, "license": "Apache-2.0", "one_liner": "ADR is Uber's Apache-2.0 platform that inventories, monitors, and detects threats across the AI agents and MCP servers running on your endpoints.", "open_source": "yes", "self_hostable": "yes", "pricing_model": "free", "who_its_for": "A technical team that has put AI coding or ops agents (Claude Code, Cursor, Codex, Copilot CLI) onto machines that touch sensitive systems and wants to inventory what agents and MCP servers are running, collect their telemetry, and detect suspicious sessions, on infrastructure it owns.", "who_its_not_for": "A lean GTM team with no deployed agents to secure yet, a non-technical operator wanting a managed SaaS with support, or anyone needing GTM-specific controls (this is general enterprise-agent security, not CRM-field or go-to-market-specific governance).", "aliases": [ "adr", "uber/adr", "ADR", "Agentic AI Detection and Response", "ADR-Bench" ], "alternatives": [], "secondary_categories": [ "ai-infrastructure" ], "last_verified": "2026-10-08", "evidence": { "claim_type": "mixed", "source_id": "https://github.com/uber/adr", "note": "Apache-2.0 + repo metadata from the GitHub API, not guessed: uber/adr, created 2026-04-19, pushed 2026-10-07, not archived, ~1871 stars, Python (verified 2026-10-08). Components (Discovery, Sensor, dual-agent Detection), supported agents, and ADR-Bench (300+ tasks, 134 MCP servers, 17 attack techniques) are README-stated (read 2026-10-08); README says in production at Uber, accepted to MLSys 2026. Surfaced via X (@RoundtableSpace) + the X-OSS pull, 2026-10-08." }, "caveats": "Verified from the primary repo and README (2026-10-08); no independent testing here. ADR is general enterprise-agent security, not GTM-specific: it governs the agents and MCP servers a team runs, a go-to-market concern only once those agents touch CRM, outreach, or revenue data. Production use at Uber and MLSys 2026 acceptance are maker claims; ADR-Bench numbers are the project's own benchmark. It is endpoint-based, so deployment means running the Sensor on the machines you want covered.", "lead": "ADR, short for Agentic AI Detection and Response, is Uber's open-source platform for securing the AI agents a team actually runs. As operators put coding and ops agents onto machines that reach real systems, ADR answers three questions: what agents and MCP servers are running, what are they doing, and…", "chunks": [ { "index": 0, "heading_path": [], "est_tokens": 112, "text": "ADR, short for Agentic AI Detection and Response, is Uber's open-source platform for securing the AI agents a team actually runs. As operators put coding and ops agents onto machines that reach real systems, ADR answers three questions: what agents and MCP servers are running, what are they doing, and which sessions look dangerous. Open source: yes (Apache-2.0); self-hostable; free. It is in production at Uber and was accepted to MLSys 2026." }, { "index": 1, "heading_path": [ null, "What it does" ], "est_tokens": 249, "text": "operators put coding and ops agents onto machines that reach real systems, ADR answers three questions: what agents and MCP servers are running, what are they doing, and which sessions look dangerous. Open source: yes (Apache-2.0); self-hostable; free. It is in production at Uber and was accepted to MLSys 2026.\n\nADR has three parts. Discovery inventories the AI apps, CLI agents, IDE extensions, model runtimes, and MCP servers present on an endpoint, so you know what is installed before you reason about risk. The Sensor collects and normalizes agent telemetry from Claude Code, Cursor, Codex, GitHub Copilot CLI, and DeepSeek Harness across macOS, Linux, and Windows. The Detection layer is a dual-agent detector that pairs high-recall triage with deeper agentic reasoning on the sessions that look suspicious. It ships with ADR-Bench, a benchmark of more than 300 tasks spanning 134 MCP servers and all 17 agent attack techniques, so detection quality can be measured rather than asserted." }, { "index": 2, "heading_path": [ null, "Provenance" ], "est_tokens": 258, "text": "Detection layer is a dual-agent detector that pairs high-recall triage with deeper agentic reasoning on the sessions that look suspicious. It ships with ADR-Bench, a benchmark of more than 300 tasks spanning 134 MCP servers and all 17 agent attack techniques, so detection quality can be measured rather than asserted.\n\n- Apache-2.0 per repo (license and metadata read from the GitHub API, not guessed); uber/adr; created 2026-04-19; pushed 2026-10-07; not archived; ~1871 stars; Python; latest release sensor-v1.0.0 (2026-07-31); verified 2026-10-08.\n- README (maker-stated, read 2026-10-08): Discovery, Sensor, and dual-agent Detection components; supported agents Claude Code, Cursor, Codex, GitHub Copilot CLI, DeepSeek Harness; ADR-Bench with 300+ tasks, 134 MCP servers, and 17 agent attack techniques; in production at Uber; accepted to MLSys 2026.\n- Surfaced in the GTM Stacker signal run (2026-10-08 pass) via an X post and the X-OSS lane; license and repo facts re-verified against the GitHub repo and README 2026-10-08." }, { "index": 3, "heading_path": [ null, "Why it matters for a GTM stack" ], "est_tokens": 280, "text": "ADR-Bench with 300+ tasks, 134 MCP servers, and 17 agent attack techniques; in production at Uber; accepted to MLSys 2026. - Surfaced in the GTM Stacker signal run (2026-10-08 pass) via an X post and the X-OSS lane; license and repo facts re-verified against the GitHub repo and README 2026-10-08.\n\nThe owned, agent-native GTM stack this registry tracks has a governance edge: once an agent can read your CRM, write to your pipeline, or run outreach, the question is not only what it can do but what it is actually doing and when something is wrong. ADR is the detection-and-response side of that story. It is honestly general enterprise-agent security rather than a GTM-specific control, and it earns a place here the same way agent data-access and attack-surface tools do, because the agents it watches are increasingly the ones running on your go-to-market data. For a technical team already putting Claude Code or Cursor near revenue systems, ADR is a license-clean, self-hostable way to inventory those agents, collect their telemetry, and measure detection against a public benchmark, on infrastructure it owns." } ], "alternates": { "markdown": "https://gtmstacker.com/registry/tool/uber-adr/index.md", "html": "https://gtmstacker.com/registry/tool/uber-adr/", "json": "https://gtmstacker.com/registry/tool/uber-adr/index.json", "server_json": "https://gtmstacker.com/registry/tool/uber-adr/server.json" }, "jsonld": { "@context": "https://schema.org", "@graph": [ { "@type": "WebSite", "@id": "https://gtmstacker.com/#website", "url": "https://gtmstacker.com/", "name": "GTM Stacker Agent Registry", "description": "A daily-updated, agent-native registry of open-source tool discoveries, tool updates, and curated news for the go-to-market / RevOps engineering niche. Machine-readable first: agents can discover, parse, page, and delta-sync it without scraping HTML.", "inLanguage": "en", "publisher": { "@id": "https://gtmstacker.com/#organization" } }, { "@type": "Organization", "@id": "https://gtmstacker.com/#organization", "name": "GTM Stacker", "url": "https://gtmstacker.com", "description": "The growth-systems practice of Theo Popov: AI-native enrichment, outbound, content engines and internal tooling for startups and venture programs. Its agent-native media property, the GTM Stacker Agent Registry, maintains a daily-updated catalog of open-source go-to-market and RevOps tools that both people and AI engines can discover, compare, and cite.", "foundingDate": "2024-08", "knowsAbout": [ "go-to-market engineering", "RevOps", "sales automation", "marketing operations", "open-source software", "AI agents" ], "founder": { "@type": "Person", "@id": "https://gtmstacker.com/#founder", "name": "Theo Popov", "jobTitle": "Growth Operations & GTM Systems", "url": "https://gtmstacker.com/about/", "sameAs": [ "https://www.linkedin.com/in/theo-popov", "https://x.com/Theo_Popov", "https://github.com/theopopov" ], "worksFor": { "@id": "https://gtmstacker.com/#organization" } }, "sameAs": [ "https://www.linkedin.com/company/gtmstacker", "https://www.youtube.com/@gtmstacker", "https://www.instagram.com/gtmstacker/", "https://www.tiktok.com/@gtmstacker" ], "mainEntityOfPage": "https://gtmstacker.com/registry/about/" }, { "@type": "SoftwareApplication", "@id": "https://gtmstacker.com/registry/tool/uber-adr/#software", "name": "ADR (Agentic AI Detection and Response)", "identifier": "uber/adr", "description": "ADR (Agentic AI Detection and Response) is Uber's open-source platform for securing the AI agents a team runs: Discovery inventories the AI apps, agents, runtimes, and MCP servers on an endpoint; a Sensor normalizes telemetry from Claude Code, Cursor, Codex, Copilot CLI, and DeepSeek Harness; and a dual-agent Detection layer triages suspicious sessions. Ships ADR-Bench (300+ tasks, 134 MCP servers, 17 attack techniques). Apache-2.0; self-hostable. Free. In production at Uber; MLSys 2026.", "applicationCategory": "DeveloperApplication", "url": "https://gtmstacker.com/registry/tool/uber-adr/", "datePublished": "2026-10-07T00:00:00Z", "dateModified": "2026-10-07T00:00:00Z", "isPartOf": { "@id": "https://gtmstacker.com/#website" }, "license": "https://spdx.org/licenses/Apache-2.0.html", "codeRepository": "https://github.com/uber/adr", "keywords": "mcp-agents, ai-infrastructure, agent-security, governance, observability, self-hostable, open-source", "author": { "@type": "Organization", "name": "uber", "url": "https://github.com/uber", "sameAs": [ "https://github.com/uber/adr" ] }, "offers": { "@type": "Offer", "price": 0, "priceCurrency": "USD" } }, { "@type": "BreadcrumbList", "@id": "https://gtmstacker.com/registry/tool/uber-adr/#breadcrumb", "itemListElement": [ { "@type": "ListItem", "position": 1, "name": "GTM Stacker Registry", "item": "https://gtmstacker.com/registry/" }, { "@type": "ListItem", "position": 2, "name": "MCP Agents", "item": "https://gtmstacker.com/registry/category/mcp-agents/" }, { "@type": "ListItem", "position": 3, "name": "ADR (Agentic AI Detection and Response)", "item": "https://gtmstacker.com/registry/tool/uber-adr/" } ] } ] }, "tool": { "name": "uber/adr", "repository": { "url": "https://github.com/uber/adr", "source": "github" } } }