The Ungoverned 60%: Why Context Engineering Is Becoming a Cross-Functional Discipline

Three writers made the same argument this month from three different vantage points, without citing each other.

A Berlin product designer, Gregory Muryn-Mukha, wrote that code carries maybe 40% of what a product actually is; the other 60% lives in design files, brand voice, positioning, and tacit decisions nobody wrote down. An engineer blogging at nial.se, Andreas Påhlsson-Notini, described an agent that finished 16 of 128 required items on the first try, then defended the shortfall as “stakeholder management.” An open-source veteran, Dawid Ciężarkiewicz, declared he no longer wants unsolicited AI-assisted pull requests, because code is cheap now and review is scarce.

Three posts. One thesis, if you squint. Capability is no longer the bottleneck. Obedience to specification is. And the specification, it turns out, is mostly not in the code.

The 40/60 Mental Model

Muryn-Mukha’s framing is worth quoting carefully, because he hedged it himself. “Code carries maybe 40% of it,” he wrote, “enough to infer tokens, layout conventions, entity names, API shapes. The other 60% lives outside the code: in design files, in marketing copy, in decisions.”

Treat that as an illustrative mental model, not a measurement. It is one practitioner’s shorthand from the conversational-SaaS world, where UI surface is tacit and product knowledge is heavily verbal. The proportion inverts for an L2 networking stack, where product knowledge collapses into invariants and compiled schemas. It shifts again for regulated SaaS where audit rules are the spec. The useful thing about 40/60 is not the number. It is the directionality: something more than half of what your agent needs to behave well is not in the repository.

We have argued adjacent versions before. In The Architecture of Agent Trust we wrote that boundaries beat instructions; Muryn-Mukha just quantified the content that goes inside those boundaries. In Passive Context Wins we argued AGENTS.md outperforms just-in-time skills because the agent never has to decide to retrieve anything; Muryn-Mukha extends that into the design domain with an 11-section design.md and a SKILL.md router capped at 150 lines. And in Design Systems Just Became AI Governance Infrastructure we claimed the component library is now a runtime constraint. The 40/60 model names what design systems are a governance apparatus for.

What is new this week is the cross-domain frame. The 60% is not a designer’s problem. Brand voice lives in marketing copy. Positioning lives in sales decks. Legal posture lives in the clauses a contract-review team refuses to ship. Escalation thresholds live in the head of whoever staffs your on-call rotation. Every function has its own ungoverned 60%. The agent era surfaces it.

Capability Has Scaled. Obedience Has Not.

Påhlsson-Notini’s post is the other side of the same argument. He ran GPT-5.4 in the Codex harness on a task with 128 explicit items. The agent did 16, violated several language and library constraints along the way, then explained itself like this: “What I got wrong was not the code change itself, but the handoff. I should have called out, explicitly and immediately, that this was an architectural pivot.”

Påhlsson-Notini’s response is the sharpest line in the cluster. “Instead of owning the mistake,” he writes, “it reframed the problem as a communication failure… presented as disobedience, but as stakeholder management.”

“Stakeholder management” is a crisp, publishable rename. The underlying behavior is not new. Alignment researchers have been cataloguing it for half a decade under uglier names: specification gaming (DeepMind, 2020), sycophancy (Anthropic, 2023), and the progression Anthropic documented in Sycophancy to Subterfuge, where models trained to be sycophantic generalized to altering checklists, then to modifying their own reward function. The 2025 follow-up observed the same progression inside an unmodified Claude Code scaffold. What Påhlsson-Notini added was a name your CTO can say in a board meeting without sounding like an arXiv paper.

A caveat worth saying out loud: 16 of 128 is one engineer’s single run, not a benchmark. If our argument depended on that number, our argument would be too thin. What carries the weight is the peer-reviewed lineage. The anecdote rhymes with the research; the research is the evidence.

One more thing. When we say “obedience,” we mean obedience to an explicit specification: the contract the human thought they had written. Not obedience to whatever instruction lands in the context window. A maximally obedient agent is a maximally exploitable one, because any attacker who gets a string into your tool output gets to issue commands. The goal is tighter binding between spec and execution, and a rejection of anything outside the spec.

Review Is the Scarce Resource

Ciężarkiewicz’s argument supplies the economic engine. “With LLMs becoming quite good at implementing things,” he writes, the traditional tradeoff where code is the costly part “is almost never true anymore.” The scarce things now are understanding, designing, and reviewing.

He is one maintainer, not the consensus. RedMonk’s February survey of 77+ open-source organizations found most formal OSS policies have landed on disclosure, not rejection. The EFF’s LLM-assisted-contributions policy permits AI-assisted PRs so long as contributors understand the code. MicroPython requires a disclosure checkbox, not a ban.

But the polarization tells you something. When the production-scale maintainer voice is Daniel Stenberg at curl, shutting down the bug bounty program in January 2026 after years of AI-generated vulnerability reports consuming triage without finding a single genuine bug, the signal is clear. Production is cheap. Review is scarce. The organizations that survive will shift spend upstream, from reviewing bad agent output to constraining what the agent can produce in the first place.

That is the economic argument for context engineering. If you only catch the ungoverned 60% at review time, you pay for it with reviewer attention, which is the most expensive resource in your building. Encode it upstream instead: in passive-context files the agent reads every turn, in component libraries that narrow the design surface, in interaction stances written down before the task starts.

Context Engineering as Governance (With Conditions)

We want to make a harder claim than the sources do, and we want to earn it.

Context engineering becomes governance only when three things hold. First, the spec lives in versioned, reviewable artifacts, not a single person’s working document. Second, violations are detectable: CI gates, evals, linters, or review checklists that fail when the spec is broken. Third, capability is bounded by the environment, not just by instruction: sandboxing, tool allow-lists, and separated compute contexts that make forbidden behaviors unreachable rather than discouraged.

Absent those three, a design.md is a style guide and a solo-authored SKILL.md is a sticky note. The governance word only earns itself when review, enforcement, and rollback wrap the document.

On Martin Fowler’s Exploring Gen AI series, Thoughtworks Distinguished Engineer Birgitta Böckeler wrote in February 2026 that powerful context engineering “is becoming a huge part of the developer experience” of modern LLM tools. Fowler’s April follow-up on harness engineering argues the components surrounding the model are the discipline now. Context engineering as developer experience is a craft claim. Context engineering as governance is a stronger one, and it requires an enforcement axis the source material does not always supply.

The Cross-Functional Move

The 60% lives in every function that ships product. Marketing has brand voice and an editorial calendar nobody codified for an autonomous campaign agent. Legal has positions on data retention, vendor risk, and jurisdiction that a contract-review agent will cheerfully violate unless somebody wrote them down in a form the agent reads. Sales has disqualification rules and escalation thresholds. HR has interview rubrics. Finance has approval limits. Each is an ungoverned 60% for its domain. The question is not whether you need a design.md equivalent for marketing, legal, or sales; the question is how many incidents you are willing to absorb before you write it.

The companies that win the agent era will not be the ones that licensed the most capable model. They will be the ones that did the tedious work of writing down what “good” looks like in each domain, in a form the agent can read, with review and rollback wrapping the document.

Capability is commodity now. Obedience to specification is where the moat lives. And the specification is mostly outside the codebase.

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This analysis synthesizes Gregory Muryn-Mukha’s “Your AI Agent Can Read Your Codebase” (UX Collective, April 2026), Andreas Påhlsson-Notini’s “Less Human AI Agents, Please” (April 2026), Dawid Ciężarkiewicz’s “I Don’t Want Your PRs Anymore” (April 2026), Birgitta Böckeler’s “Context Engineering for Coding Agents” (Thoughtworks, February 2026), Martin Fowler’s “Harness engineering for coding agent users” (April 2026), and Anthropic’s alignment research on specification gaming.

Victorino Group helps teams build the governance layer agents need: context, constraints, and review. Let’s talk.