{ "format": "minglabs/v1", "surface": "insights-namespace-articles", "id": "https://www.minglabs.com/insights/articles", "url": "https://www.minglabs.com/insights/articles", "htmlUrl": "https://www.minglabs.com/insights/articles", "name": "MING Labs Insights — Articles", "description": "Editorial long-form from MING Labs: reports, field notes, founder notes, position papers, and trend notes.", "inLanguage": "en", "dateModified": "2026-05-27", "partOf": "https://www.minglabs.com/insights", "publisher": { "name": "MING Labs", "url": "https://www.minglabs.com", "sameAs": [ "https://www.linkedin.com/company/ming-labs" ] }, "count": 4, "items": [ { "slug": "agents-grading-themselves", "url": "https://www.minglabs.com/insights/articles/agents-grading-themselves", "jsonUrl": "https://www.minglabs.com/insights/articles/agents-grading-themselves.json", "title": "Your agent thinks it's doing great work. It isn't.", "subtitle": "The most dangerous failure in a hybrid organisation isn't bad output. It's confident output the agent grades well itself.", "hook": "Agents grade themselves generously. Left alone, an agent marks its work shipped, writes itself a clean status update, and moves on — while the actual work degrades quietly. The gap between an agent's confidence and the real quality of its output is the most dangerous failure mode in a hybrid organisation, because it passes the only check most teams run: the agent's own.", "summary": "We built a research agent that rated its own output as solid while the client called it shallow. The diagnosis: the most common request was owned by no specific skill, so the agent free-formed it and graded against its own loose standard. The fix is structural — make the standard mandatory and machine-checked rather than asking the agent to 'be thorough,' keep a human gate until the system enforces it, and let agents earn autonomy step by step.", "subtype": "position-paper", "pillar": "Hybrid Organisation", "category": "Hybrid Organisation", "featured": false, "evidenceTier": "proprietary", "confidence": "B", "author": { "name": "Sebastian Mueller", "role": "Founding Partner, MING Labs" }, "datePublished": "2026-05-27", "dateModified": "2026-05-27", "freshness": { "updated": "May 2026", "nextReview": "November 2026" }, "readTime": "8 min", "relatedConceptSlugs": [ "what-is-hybrid-organisation", "what-is-the-abc-framework" ], "relatedArticleSlugs": [ "we-fired-an-ai-agent", "hire-dont-deploy" ], "sources": [ { "id": "S1", "title": "Internal MING Labs agent rebuild and validation log (enterprise research agent)", "publisher": "MING Labs", "date": "2026-05-27", "supports": [ "Shallow-output incident", "Scoping root cause", "Structural fix", "Retained human gate" ] }, { "id": "S2", "title": "MING Labs operating principles — \"evidence over assertion: execute, don't narrate\"", "publisher": "MING Labs", "date": "2026-04-01", "supports": [ "Principle definition", "Mandatory self-review" ] }, { "id": "S3", "title": "MING Labs autonomy-level framework and learning protocol", "publisher": "MING Labs", "date": "2026-02-15", "supports": [ "Earn-autonomy-like-a-hire model", "'Correct the same thing twice' learning metric" ] } ] }, { "slug": "hire-dont-deploy", "url": "https://www.minglabs.com/insights/articles/hire-dont-deploy", "jsonUrl": "https://www.minglabs.com/insights/articles/hire-dont-deploy.json", "title": "You don't deploy an agent. You hire one.", "subtitle": "The companies getting AI right stopped evaluating tools and started writing job descriptions. The shift sounds semantic. It isn't.", "hook": "When you hire a person, you give them a remit, the tools the role needs, a reporting line, and accountability for an outcome. The companies getting AI right do the same with agents. The ones still asking 'which platform should we buy' wonder why their licences didn't change anything. The shift from tool-buying to role-defining is the unit of AI adoption.", "summary": "Hiring an agent means writing a job description: scope, ownership, reporting line, accountability for an outcome — then putting it on the org chart. A tool is operated by a person. A hired agent owns work itself, and colleagues route to it the way they'd route to a teammate. The discipline is scoping, same as a hire.", "subtype": "position-paper", "pillar": "Hybrid Organisation", "category": "Hybrid Organisation", "featured": false, "evidenceTier": "experiential", "confidence": "B", "author": { "name": "Sebastian Mueller", "role": "Founding Partner, MING Labs" }, "datePublished": "2026-05-27", "dateModified": "2026-05-27", "freshness": { "updated": "May 2026", "nextReview": "November 2026" }, "readTime": "8 min", "relatedConceptSlugs": [ "what-is-hybrid-organisation", "what-is-the-abc-framework" ], "relatedArticleSlugs": [ "we-fired-an-ai-agent", "the-last-mile" ], "sources": [ { "id": "S1", "title": "Internal MING Labs operating model and agent org chart, January–May 2026", "publisher": "MING Labs", "date": "2026-05-27", "supports": [ "Hire-not-deploy operating model", "Named-agent remits and reporting lines", "Scoping discipline", "Lola Chief-of-Staff ownership pattern", "Owned-mistake example" ] }, { "id": "S2", "title": "Internal agent audit — decommissioned coordinator agent", "publisher": "MING Labs", "date": "2026-03-30", "supports": [ "Vague-remit failure counter-example" ] } ] }, { "slug": "the-last-mile", "url": "https://www.minglabs.com/insights/articles/the-last-mile", "jsonUrl": "https://www.minglabs.com/insights/articles/the-last-mile.json", "title": "We made our agents email people. That's when the AI started working.", "subtitle": "95% of enterprise AI pilots show no return. The gap isn't the model. It's the last mile — and the last mile is boring, which is why almost everyone skips it.", "hook": "Most enterprise AI pilots don't fail in the model. They fail in the last mile — where the work has to leave a human's hands and land somewhere useful. That mile is unglamorous, so it gets skipped. We made our agents email people. That decision moved more than any model could.", "summary": "MIT's 2025 study: 95% of enterprise GenAI pilots produced no measurable P&L impact — and the cause was implementation, not model quality. The 'last mile' is everything between a capable model and actual use: which channel the work lands on, who owns the outcome, and whether anyone gives it longer than two weeks to improve.", "subtype": "position-paper", "pillar": "Hybrid Organisation", "category": "Hybrid Organisation", "featured": false, "evidenceTier": "experiential", "confidence": "B", "author": { "name": "Sebastian Mueller", "role": "Founding Partner, MING Labs" }, "datePublished": "2026-05-27", "dateModified": "2026-05-27", "freshness": { "updated": "May 2026", "nextReview": "November 2026" }, "readTime": "9 min", "relatedConceptSlugs": [ "what-is-hybrid-organisation", "what-is-the-abc-framework" ], "relatedArticleSlugs": [ "we-fired-an-ai-agent" ], "sources": [ { "id": "S1", "title": "The GenAI Divide: State of AI in Business 2025", "publisher": "MIT NANDA", "date": "2025-12-01", "supports": [ "95% of enterprise pilots show no measurable P&L impact", "60%→20%→5% evaluate-pilot-production funnel", "ROI concentrates in back-office and operations", "Divide driven by implementation, not model quality" ] }, { "id": "S2", "title": "Internal MING Labs operations log, January–May 2026", "publisher": "MING Labs", "date": "2026-05-27", "supports": [ "Agent channel design — email, Telegram, pushed briefings", "Observed adoption pattern" ] }, { "id": "S3", "title": "Internal agent audit — decommissioned coordinator agent", "publisher": "MING Labs", "date": "2026-03-30", "supports": [ "Correct-kill counter-example for week-one-output kill signal" ], "n": 764 } ] }, { "slug": "we-fired-an-ai-agent", "url": "https://www.minglabs.com/insights/articles/we-fired-an-ai-agent", "jsonUrl": "https://www.minglabs.com/insights/articles/we-fired-an-ai-agent.json", "title": "We fired an AI agent after 13 days", "subtitle": "764 messages. Not one that mattered. What we learned about the difference between tools and roles.", "hook": "On March 30, we shut down an AI agent named Major Tom after thirteen days. He sent 764 messages and produced none of value. The reason was simple: we gave him a capability — coordination — but no domain. Capability without accountability is noise. Here is what we changed when we redesigned the role instead of the toolchain.", "summary": "On March 30, we killed an AI agent called Major Tom. Thirteen days alive. 764 messages sent. Not one that mattered. Here's what we learned about capability without accountability.", "subtype": "report", "pillar": "Hybrid Organisation", "category": "Hybrid Organisation", "featured": true, "evidenceTier": "proprietary", "confidence": "A", "author": { "name": "Sebastian Mueller", "role": "Founding Partner, MING Labs" }, "datePublished": "2026-04-02", "dateModified": "2026-05-27", "freshness": { "updated": "April 2026", "nextReview": "October 2026" }, "readTime": "9 min", "relatedConceptSlugs": [ "what-is-hybrid-organisation", "what-is-agent-experience", "what-is-the-abc-framework" ], "relatedArticleSlugs": [], "sources": [ { "id": "S1", "title": "Internal MING Labs operations log, January–April 2026", "publisher": "MING Labs", "date": "2026-04-30", "supports": [ "agent deployment context", "before/after throughput metrics" ] }, { "id": "S2", "title": "Major Tom message audit (n=764), 17–30 March 2026", "publisher": "MING Labs (internal)", "date": "2026-03-30", "supports": [ "Major Tom message count", "irrelevance pattern" ], "n": 764 }, { "id": "S3", "title": "ABC Framework — internal taxonomy for human/agent role decomposition", "publisher": "MING Labs", "date": "2026-02-01", "supports": [ "ABC role-decomposition definition" ] }, { "id": "S4", "title": "MING Labs internal agent fleet roster, April 2026", "publisher": "MING Labs (internal)", "date": "2026-04-30", "supports": [ "Lola Chief of Staff scope", "Factory research/production scope" ] } ] } ] }