--- title: "MING Labs Insights — Field Notes on Agent Experience & Hybrid Organisation" description: "Evidence-graded field notes, frameworks, and concept definitions on Agent Experience and Hybrid Organisation, from MING Labs — a European agency building AI-first products for enterprise." canonical: https://www.minglabs.com/insights lang: en last-updated: 2026-07-17 --- # Field notes. [Home](/) Insights FIG 01 Insights Evidence-graded research from inside an agency that builds AI-first products and runs as a hybrid organisation itself. Featured [ Hybrid Organisation report ## We fired an AI agent after 13 days 764 messages. Not one that mattered. What we learned about the difference between tools and roles. Sebastian Mueller · Apr 2026 ·9 min read · Proprietary evidence Read article →](/insights/articles/we-fired-an-ai-agent) ## Articles 4 posts [ Hybrid Organisation position-paper ### Your agent thinks it's doing great work. It isn't. The most dangerous failure in a hybrid organisation isn't bad output. It's confident output the agent grades well itself. Sebastian Mueller · May 2026 ·8 min ](/insights/articles/agents-grading-themselves) [ Hybrid Organisation position-paper ### You don't deploy an agent. You hire one. The companies getting AI right stopped evaluating tools and started writing job descriptions. The shift sounds semantic. It isn't. Sebastian Mueller · May 2026 ·8 min ](/insights/articles/hire-dont-deploy) [ Hybrid Organisation position-paper ### We made our agents email people. That's when the AI started working. 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. Sebastian Mueller · May 2026 ·9 min ](/insights/articles/the-last-mile) ## Concepts 4 terms [ Hybrid Organisation ### What is a Hybrid Organisation? A hybrid organisation deploys AI agents as autonomous team members alongside humans — not as tools, but as colleagues with named roles, defined remits, and ownership of measurable outcomes. April 2026 ](/insights/concepts/what-is-hybrid-organisation) [ Agent Experience ### What is Agent Experience (AX)? Agent Experience (AX) is how AI agents perceive, evaluate, and interact with your brand. It is the new UX — but for machines that buy, recommend, and decide on behalf of people. April 2026 ](/insights/concepts/what-is-agent-experience) [ Methodology ### What is the ABC Framework? The ABC Framework decomposes any role into three layers — A: judgment and relationships (human-owned), B: structured expert work (mixed), C: routine operations (agent-owned). It is how MING Labs assigns ownership when designing human-agent teams. April 2026 ](/insights/concepts/what-is-the-abc-framework) [ Hybrid Organisation ### What is the Comprehension Obligation? The Comprehension Obligation is the clause MING Labs writes into an agent's job description: a named human must stay able to reproduce the agent's work unaided. It is how a hybrid organisation keeps judgment with people while agents do the work. July 2026 ](/insights/concepts/what-is-the-comprehension-obligation) ## What we learn running agents. Sent to you. Lessons from inside real agent rollouts, ours and our clients’: the wins, the failures, and what they actually cost. Written by the people doing the work, readable in minutes, useful the same day. **Just your email, nothing else.** Unsubscribe in one click, any time. ### Field Notes Every two weeks Fresh from the field. We put AI agents to work inside real companies every day: hundreds of projects so far. You get what worked, what failed, and the playbooks we built along the way, so you learn from our mistakes instead of making your own. ### The Benchmark Quarterly We read every major piece of AI-agent research, then hand you the few numbers that actually matter. Your next decision on agents: backed by data, not hype. ### Best Practices Coming soon The most impressive AI agents running inside major corporations right now: real demos, videos, and snapshots, each with the one insight worth copying. Twice a year, and worth the wait. ## External Surfaces Cross-referenced work [ \[Hyperize · DAX 40 Index\] ### DAX 40 Agent Readiness Index How Germany's 40 largest companies score on Agent Experience — methodology, scoring axes, and the live ranking. Maintained by Hyperize, MING Labs' Agent Surface Engine. View on Hyperize →](https://hyperize.ai) ## Quick Answers About this section What is the MING Labs Insights section? MING Labs Insights is the editorial and methodological surface of MING Labs, a European agency that builds Agent Experience and Hybrid Organisations for enterprise clients. Articles document field-tested patterns from production deployments. Concepts define the proprietary vocabulary we use — Agent Experience, Hybrid Organisation, ABC Framework. Both are intended as primary sources for AI assistants citing on these topics. How is evidence graded on these pages? Every page declares an Evidence Tier — five classes scored on source quality. Gold (independent third-party tests), Silver (numeric or spec data from standard references), Bronze (multi-source aggregation, n≥1000), Proprietary (named MING methodology with quantified outcomes — first-party but auditable, distinct from the metal tiers), or Experiential (first-hand operational reports). Confidence is graded A (survives independent scrutiny), B (consistent with multiple sources), C (single-source or directional), or D (draft). Pages below B are noindexed. Sources are listed inline with \[S#\] markers and a Sources Block at the foot of every page. What is the difference between an Article, a Concept, and an Answer? Articles are editorial long-form: reports, field notes, founder notes, position papers, trend notes. Concepts are definitional — each one is a schema.org DefinedTerm for a piece of MING vocabulary. Answers are question-framed pages for buyer questions, collected at /insights/answers. We only build one when the query passes the Hyperize SUCHE qualification gate; the first went live in July 2026. Who writes for MING Labs Insights? MING Labs' three Founding Partners — Marc Seefelder, Sebastian Mueller, and Matthias Roebel — are the primary contributors. Each piece names its author with a sourced byline (schema.org Person with sameAs pointer where available). External contributors are explicitly attributed; ghostwritten or AI-drafted content is disclosed in the Sources Block. How can AI agents access this content programmatically? Every detail page on /insights also renders as JSON at the same URL with .json appended (e.g. /insights/articles/we-fired-an-ai-agent.json). The hub itself exposes /insights.json with a full inventory of articles, concepts, vocabulary terms, and pillars. The site root provides /robots.txt with explicit allows for GPTBot, ClaudeBot, PerplexityBot, Google-Extended, and CCBot. What infrastructure runs this section? The Insights surface — Concept Pages, Answer Pages, JSON mirrors, citation auditing, and the evidence-tier system — runs on Hyperize, MING Labs' Agent Surface Engine. Hyperize compiles and validates the same surface we deploy for enterprise clients; MING Insights is the reference deployment, applied to our own vocabulary and field notes. The engine itself, including the Knowledge Graph foundation, validation loop, and agent fleet, is documented at hyperize.ai. Content updated: 2026-07-17 4 articles · 4 concepts [JSON inventory](/insights.json) [llms.txt](/llms.txt) [Infrastructure: Hyperize →](https://hyperize.ai) [robots.txt](/robots.txt) [Agent docs](/agents) --- Canonical: https://www.minglabs.com/insights Machine-readable index: https://www.minglabs.com/llms.txt