TTB 3: Context Engineering from 10,000 Feet
The organizational practice beneath agentic composition in an age of Modularity
Organizations face mounting pressure to modularize and decompose, to become flexible enough for a world that refuses to stay predictable. At the same time, AI is delivering an unprecedented boost to productivity and capability. These twin forces are elevating an emergent discipline called context engineering from a technicality left to software engineers into an organizational practice of strategic importance.
Context engineering is the work of building systems that let humans collaborate with agentic contributors while retaining the capacity to observe, steer, and understand what’s produced, keeping outcomes aligned with the needs of customers and partner ecosystems.
The case for “deep-seated flexibility”
A few weeks ago, BCG’s Henderson Institute released Beyond Tomorrow: Four Scenarios for the World of 2050, and it has been an engaging read.
The report constructs four scenarios to evaluate the often implicit strategic assumptions underlying organizational strategy and development.
The AI Abundance scenario imagines a post-Compute-Wars world of cheap energy, tripled GDP, and AI-only firms. Battling Blocs is a multipolar stalemate where global trade falls and defense spending nearly triples in a re-regionalized world. Climate Coalition charts the stagnant dream of slow, expensive decarbonization disciplined by a $300-per-ton carbon price, while in Digital Darwinism, governments retreat (due to a failure in managing the climate crash), corporations fill the vacuum, we meddle with the atmosphere, and the wealthiest 1% holds nearly half of global wealth.
Currently, the world seems heading towards a mix of all four scenarios. While the scenarios diverge on growth, geopolitics, energy, governance, and demography, they converge on some strategic recommendations. Among the five “low-regret moves” BCG identifies as payoff-positive across all four futures, the same action recurs in a different form: organizational, operational, and technological modularity.
In AI Abundance, modularity defends against compressed technology cycles, modular IT architectures, multicloud and multiregion stacks, and “modular factories that can rapidly adapt to new innovations.” In Battling Blocs, it lets an organization disconnect from one region without collapsing the value chain, thanks to operating structures designed for “relatively easy separation of markets,” and supply networks that can run semi-autonomously while infrastructure is built for rapid swapping and reconfiguration. In Digital Darwinism, with weak institutions and violent markets, modularity becomes the architecture for surviving turbulence, stacks that can be “swapped, isolated, or localized” as conditions shift. In the hopeful scenario of Climate Coalition, modularity and legibility become key at the product layer: circular design and sustainability constraints require components that can be evaluated and recombined.
All these pressures demand a structural answer: an organization whose units, products, and technology stacks are composable. That holds across all four futures.
The convergence is not accidental. McKinsey’s 2026 State of Organizations report, based on a survey of 10,018 executives across 15 countries, reaches the same conclusion: 86% of organizations are unprepared for AI operations, 72% report direct geopolitical impact, and 38% cite rigid structures as the primary barrier. McKinsey describes three significant forces: Technology Disruption, Economic Disruption, and Workforce Shifts, which reflect deeper structural moves: AI redesigning markets by lowering coordination costs, constant boundary erosion between firms and partner ecosystems, and compressed planning horizons under polycrisis.
McKinsey calls for “deep-seated flexibility” but is frustratingly vague about its operational meaning, surprisingly so, because the operational answer has been practiced for years. We’ve described the evolution from functional to matrix to platform organizations for some time, and the implication has remained the same: deep-seated flexibility is what you get when you do the modularization work, not something abstract.
A deeper strategic pattern is emerging. We’ve been calling for companies to complement a platform motion (centralized on coherence and strong integration) with a portfolio motion. Disruptions and technological democratization have nudged companies toward radical optionality for a while, and AI intensifies this perspective by making portfolio motion dominant over platform motion. AI’s coordination capabilities reduce friction in composition and the portfolio motion, creating more options, empowering small teams to attack niche markets with multiple strategies, and making it more viable, reducing the need for traditional centralized approaches. I wrote about it recently in TTB 2, After the Platform.
Both reports miss the implications of the modularity prescription. When everything becomes composable and coordination costs approach zero, competitive advantage shifts from doing things well, which becomes commoditized, to doing the right thing. In an environment where individual optimization is insufficient and connection ability is decisive, something is “right” only if many others can read the same picture you can, and your strategy is wary of partners’ constraints, capabilities, and interests. Investing in shaping a direction and creating shared meaning becomes the work. Ecosystem partners need to converge on interfaces, workflow models, and integration contracts. Shared languages matter more than traditional business development.
Modularity without context engineering produces fragmentation, not composition. The pieces fit together only if they share a language at the boundary, which must be authored rather than assumed.
Consider two teams modularizing a customer-facing capability. One team defines “customer” as the paying account; the other means the end user. When they try to compose their modules, integration fails: not from technical incompetence but from semantic misalignment. The modules are independently correct and jointly incoherent. Multiply this across dozens of bounded contexts, internal teams and external partners, and you get the cost of modularity without shared meaning: drift. Interfaces accumulate ambiguity, and the organization loses the ability to reconfigure quickly.
Context engineering is the discipline that prevents this: explicitly authoring the concepts, boundaries, and constraints that let autonomous pieces compose into coherent wholes. This semantic work is the focus now.
When iteration goes cheap, intent goes expensive.
The transition inverts competitive advantage. For the past four decades, advantage came from operational excellence, executing known processes faster, cheaper, and more reliably. In the AI era, that gap compresses. When iteration is cheap, and the cost of producing a working artifact drops significantly, doing things well stops being the edge. It becomes the price of entry.
Execution capacity, once scarce, is now abundant. The answer to what we should be executing at all becomes the bottleneck. In software, the focus shifts from writing code to modeling business contexts, organizational capabilities, and ecosystem interactions. Gathering requirements turns out, in hindsight, to have been the strategic discipline all along; the cost of bad modeling was hidden under poor execution. AI makes it visible.
This makes intent the new scarce resource, and intent modeling and context engineering are the new strategic disciplines. As iteration becomes easier, we have more time to think. The hard work is upstream of execution: deciding which problems are worth modeling, which boundaries to draw, and which constraints to author into the system for downstream composable generation. Most of us aren’t investing that returned time effectively.
What remains constant: the human at the boundary
One might think that AI, by making translation across contexts easier, reduces the need for explicit semantics. The opposite is true. As contexts multiply and the market fragments, so do the boundaries. Boundary languages become more important, not less, and not for machines to understand each other but for humans to read what happens at the boundary and drive it intentionally.
In a recent conversation with Alberto Brandolini, he reminded me of the distinction between internal and boundary models, which matters here. The internal language within a bounded context serves different purposes than the published language exposed to units and partners, responding to different evolutionary pressures. There will always be pressure for contextual freedom within domains, but the multiplication of AI-enabled contexts makes boundary legibility more crucial.
This connects to what I have come to think of as the invariant design constraint: human observability and interpretability.
If you build something at the limit of human observability and legibility, model evolution may not affect your product in meaningful ways. When designing new collaboration tools, use human cognitive limits as the primary design constraint rather than AI capabilities. Even as AI improves, human limits remain stable, which makes them a more durable foundation for organizational design than a shifting model frontier. You can design prosthetics that augment observation, a tool for engineers to review workflows faster, or a linting dashboard monitoring KPIs for software best practices, but ultimately, humans need to review, read, and close the feedback loop.
The primary value of context engineering, then, is not in tightly controlling AI outputs, but in maintaining intentional direction-setting and the ability to observe, evaluate, and comprehend the generated content. Subjecting our AI leverage to our understanding will keep things small, modular, and human.
Control matters less than clarity and strategic alignment.
Context engineering becomes the discipline of deciding which artifacts define truth and of authoring the constraints, both of which are central organizational capabilities.
Not a function. A discipline.
Context engineering is a pervasive organizational capability, not a technicality. Large organizations will need multiple context engines operating at different scales; each bounded context requires its own micro-context engine while maintaining coherence with the broader organizational ontology. External ecosystem players must participate in defining the languages, which represents a structural break from traditional platforms that imposed their own linguistic standards.
Context engineering is not about software. It’s a deliberate practice through the same framework across contexts where an organization builds artifacts by integrating agentic capabilities. Whether managing autonomous IT infrastructure, creating AI-assisted media, or recombining organizational capabilities with their interfaces and costs in response to customer needs, the same principles apply.
Three legs, one bundle
In our experience at Boundaryless, context engineering has a three-pronged architecture:
A deep meaning layer, typically a context map identifying the bounded contexts in scope, and a navigation language between the map elements describing the key objects existing at the contexts’ boundaries.
A business and problem logic layer detailing roles, their capabilities, and use cases with related scenarios, all mapped to the bounded contexts.
A user experience and interaction layer, explaining how the capabilities are available to users across interfaces, depending on the product(s).
The definition of all three layers cannot be fully delegated to AI. It must remain at human scale; if not fully human-produced in front of a board (ideal), it must at least be fully human-comprehensible.
A Context Bundle is the structured artifact that answers four questions:
Which concepts are valid?
Which problem areas are separate and can be developed independently?
What abilities do actors have?
What experience do users have?
It’s structured context readable by humans and usable by agents, versioned as intent changes. It provides a method to make organizational intent machine-readable while keeping it human-inspectable.
Whom do I choose to share context with?
Conclusion: in a world of microscopic, modular, adaptive, and AI-enhanced elements, the organization’s value lies in its ability to contextualize.
Identity is no longer defined by our actions but by our configurations, chosen constraints, preferred patterns, and accepted trade-offs.
When an organization decomposes into small, agent-powered units, context becomes the primary organizational attractor, replacing hierarchy, process, and culture as the forces that hold activity together. Hierarchy, process, and culture do not vanish, but become insufficient as primary coherence mechanisms in front of the unleashed generative forces.
The organizing question becomes: with whom do I choose to share context?
Context engineering is the new frontier of organizing. Companies that neglect it will keep telling themselves they can resist modularization by vertically integrating and managing up and down, and they’ll progressively lose coherence, sometimes gradually, sometimes abruptly.
Companies that get this right resemble living systems of meaning: pieces that rapidly reconfigure while keeping identity through constraints and shared intent.
Curated Links
Beyond Tomorrow: Four Scenarios for the World of 2050, April 2026, Nikolaus Lang et al. (BCG Henderson Institute)
BCG’s scenarios reveal that organizational modularity is the consistent strategic move across all possible futures, whether AI abundance democratizes coordination or digital Darwinism concentrates it among elites.
State of Organizations 2026: Three tectonic forces reshaping organizations (McKinsey)
McKinsey identifies “deep-seated flexibility” as the key organizational capability but stays vague about implementation, exactly the gap that explicit context engineering and modular capability-based organization design (RenDanHeYi / Platform Org) addresses.
Apps and programming: two accidental tyrannies
Matuschak’s lecture advocates for composable product architectures and demonstrates how shared semantic foundations (like CodeMirror’s physics) enable plugin ecosystems, encouraging us to think beyond applications. Increasingly relevant as we move into a software-infused age.
AI in World Machine Theory
Rao’s AI framework creating planetary-scale liveness through shared context and memory integration provides the macro lens for understanding how context engineering scales beyond individual organizations to ecosystem-wide coordination.
What is Artificial Experience (AX)
As humans become the limiting factor, the task is to become artificially intelligent, not like machines, but in consciously designing technological futures rather than sleepwalking into whatever emerges. AI as a problem for thought about how we wish to live, not just a problem to be solved.
The Culture of AI Engineering
The Pace Layers framework reveals why AI makes organizational structure more necessary: different context elements must operate at different speeds while maintaining coherence across the entire system.
Work Updates
We’re making progress on the context engineering framework through client deployments and technical development:
Context Bundle implementation: we built and tested the first Context Bundle prototype, exploring how structured product intent can be made readable and operable by agents.
O2A specification advancement: the Open Organization Alliance standard now includes formal semantics and is about to be released in its first public version. Discussions with the Protocol Institute about support are ongoing. If you don’t know what the Protocol Institute is, check it out.
I’m looking for design partners who recognize that their competitive advantage lies in context engineering rather than operational execution, and who understand that connecting that to the rest of the organization requires modularization. If you struggle to coordinate capabilities across internal teams and external partners, or see AI create more options than your structure can handle, I’d love to explore how these frameworks apply to your challenges.