How to Build (Marketing) Context Architecture: The 8 Skills Every Marketing Agent of Context Needs
This is what my research has shown: marketers are the ones who can build the context AI depends on. Here's what that work looks like in practice.
In the first post of this series, I argued that the most important agent in the agentic era isn’t AI. It’s the marketer who architects the context everything else depends on. I called this role the agent of context: the practitioner who translates business reality, customer understanding, and operational constraints into structured context powering both human and AI-driven decisions.
The argument resonated. The question that followed: what does context architecture look like as a daily practice? What specific skills does it require? How do you develop them?
This post answers those questions using my proprietary Experimental Marketer Framework’s 8 Es, mapped to three phases of context architecture work: understanding the landscape, building and testing structures, and scaling the capability across the organization.
Dear Reader, where Do You Stand Today?
Please, before reading further, take a quick self-assessment. Rate yourself (strong / developing / not yet started) on each of these eight capabilities as they relate to building context for your marketing operation.
Explore — Do you systematically map the data, business rules, and operational constraints before building?
Engage — Do you extract and formalize domain knowledge from cross-functional stakeholders?
Exchange — Do you create reusable structures that carry context across teams, markets, and systems?
Experiment — Do you test context structures against real outcomes and iterate?
Embrace — Do you adopt new tools and frameworks into your context layer selectively and deliberately?
Everything — Do you think in systems across the full stack and customer journey and communicate with other stakeholders appropriately?
Explain — Do you make context architecture work visible and valued within your organization?
Evolve — Do you continuously adapt your context structures as capabilities and business needs change?
Hold your answers. By the end of this post, each rating will point to specific actions.
PHASE 1 — UNDERSTANDING THE CONTEXT LANDSCAPE
This phase covers the discovery and stakeholder work that needs to happen before you build anything. Skipping it is the most common mistake in context architecture, and the most expensive to fix later.
Explore: Map Before You Build
Context architecture starts with discovery. Before creating any structure, template, or governance model, you need to map three things: what customer data exists and where it lives, what business rules govern decisions across channels and markets, and what operational constraints shape how teams execute.
Most organizations skip this step or treat it as a checkbox exercise, which results in context structures built on assumptions rather than reality. A customer data template designed without understanding the intricacies of data collection can create more problems than it solves. A governance model built around how a team thinks things work, rather than how they work in practice (and how this work can be improved), gets abandoned within weeks.
The Explore skill is the discipline of doing a context inventory before a data inventory. A data inventory tells you what exists. A context inventory tells you what matters for decisions. The difference is significant. You might have 200 data fields in a customer profile, but only 30 of them are actively used in segmentation, activation, or decisioning. Knowing which 30, and why, is the starting point of context architecture.
Where domain knowledge hides: Process documents capture intended workflows, not actual ones. Tribal knowledge lives with long-tenured team members who have built workarounds nobody documented yet. Vendor configurations carry business logic that got encoded during implementation and forgotten afterward. Regional teams operate with local adaptations that headquarters doesn’t know about. A thorough Explore phase surfaces all of these.
Action if you rated yourself “developing” or “not yet started”: Pick one activation workflow in your organization. Trace it from data collection through execution. Document every decision point, every handoff, every business rule applied along the way. Compare what you find to what your documentation says should happen. The gaps between the two are your context architecture starting points.
Engage: Extract Knowledge Across Functions
Context doesn’t live in one team. The operational knowledge making context valuable is distributed across product, sales, service, legal, regional marketing, and IT. Each function holds a piece of the puzzle. No single function holds the complete picture.
The Engage skill is cross-functional coordination with a specific purpose: turning implicit expertise into explicit, structured inputs for context architecture. This goes beyond the typical stakeholder alignment meeting. You’re extracting specific knowledge from each function and translating their answers into shared definitions and rules.
What you’re getting from each function: Product teams hold feature logic, roadmap constraints, and usage patterns that shape what’s possible in customer engagement. Sales teams hold customer objection patterns and deal dynamics that reveal what customers respond to and what they resist. Service teams hold complaint patterns and resolution logic that expose where the customer experience breaks. Legal and compliance teams hold consent rules, regulatory constraints, and market-specific requirements that define boundaries. Regional teams hold local adaptations, market-specific behaviors, and exceptions to global rules that determine whether a centralized structure works in practice.
The translation challenge: Each function speaks its own language. Product talks in features and releases. Sales talks in deals and pipeline. Legal talks in risk and compliance. The Marketing Agent of Context needs to translate these perspectives into shared definitions, standardized rules, and structured inputs both humans and systems use. This translation work is where the value gets created. Without it, you have a collection of departmental knowledge. With it, you have a context layer.
Action if you rated yourself “developing” or “not yet started”: Identify one business rule in your marketing operation that different teams define differently. “Active customer” is a common one: marketing, sales, and service often use different criteria. Run a 30-minute alignment session focused solely on creating one shared definition. That exercise, applied repeatedly across your operation, is the Engage skill in practice.
PHASE 2 — BUILDING AND TESTING CONTEXT STRUCTURES
This phase covers the operational work of creating context structures, testing them against real outcomes, and incorporating new capabilities as they mature. This is where context architecture moves from planning to production.
Exchange: Build Reusable Context Structures
Exchange is the core build skill of context architecture. Creating reusable structures (templates, taxonomies, governance rules, data definitions) that carry domain knowledge across teams, markets, and systems so every downstream action doesn’t start from zero.
The principle is this: a good context structure encodes decisions so they don’t need to be remade over and over again. Data definitions that standardize what “active customer” means across 20 markets, for example. Activation templates that carry business rules so campaign setup follows consistent logic. Governance models that define who owns what data and what approval paths apply. Each of these is a context structure. Each one reduces the decision load for every person and system operating downstream.
Characteristics of effective context structures: They are reusable across teams and markets, not custom-built for a single use case. They are portable, meaning they work across systems rather than being locked inside one platform. They encode business logic explicitly rather than relying on the person using them to know the unwritten rules. And they reduce decision load downstream: every team or system consuming the structure gets context they don’t need to assemble themselves.
Common failure modes: Over-engineering, where the structure becomes so detailed it’s harder to use than the manual process it replaced. Building for one team’s needs rather than shared use, which creates adoption problems when other teams encounter the structure. Not versioning as business rules change, which means the structure slowly drifts from reality and loses trust.
Action if you rated yourself “developing” or “not yet started”: Take one repeatable process in your marketing operation (a campaign setup, a data activation, a segmentation workflow). Document the business rules applied during that process. Package those rules into a reusable format another team or market could follow without asking you for guidance. That’s your first context structure.
Experiment: Test Your Context Against Reality
Context architecture isn’t a one-time build. You test whether your structures produce the right downstream results. Did the standardized data definitions reduce activation errors? Did the governance model speed up or slow down time to market? Did the shared template enable experimentation or did it constrain teams in ways you didn’t anticipate?
The Experiment skill applies the Experimental Marketer mindset directly to context work: treat context structures as hypotheses. Build, measure, learn, iterate.
What to measure when testing context structures: Time to activation (did the structure speed up or slow down execution?). Error rates (did standardized definitions reduce downstream mistakes?). Team adoption (are people using the structure or working around it?). Experimentation velocity (did the shared foundation create more capacity for testing?). Each metric tells you something different about whether the context structure is working as intended.
How to test without disrupting production: Run parallel processes for a defined period. Apply the new context structure to one market or one campaign type while maintaining the existing process for others. Compare outcomes. This avoids the risk of a full-scale rollout before you’ve validated the approach, while generating real evidence for the business case.
Action if you rated yourself “developing” or “not yet started”: Take one context structure you’ve created (or one that exists in your organization) and define two measurable outcomes it should produce. Track those outcomes over 30 days. The gap between expected and actual performance will tell you where to iterate.
Embrace: Adopt New Capabilities Selectively
New capabilities change what context architecture requires and enables. AI agents, MCP connectors, real-time decisioning engines, and context engineering tools expand both what’s possible and what’s necessary. The agent of context needs to evaluate and adopt new capabilities as they mature, without chasing every new tool that appears.
The discipline is selective adoption. Not every new AI capability strengthens your context layer. Some add complexity without corresponding value. Some duplicate context management you’re already doing elsewhere. Some require structured context inputs you haven’t built yet, which means adopting the tool before building the foundation creates more problems than it solves.
Questions to ask before adoption: Does this tool need structured context I already have, or do I need to build new context structures to feed it? Does this create new context requirements my team isn’t staffed to maintain? Does this duplicate context management happening elsewhere in the stack? Does the value of adoption outweigh the integration and maintenance cost? These questions prevent the common pattern of tool proliferation without corresponding context maturity.
Action if you rated yourself “developing” or “not yet started”: The next time a new AI tool or capability is proposed for your marketing stack, run it through those four questions before evaluating features or pricing. The context readiness assessment tells you more about likely success than any product demo.
PHASE 3 — SCALING AND SUSTAINING CONTEXT ARCHITECTURE
This phase covers the systems thinking, organizational visibility, and continuous adaptation required to make context architecture an enduring capability rather than a one-time project.
Everything: Think in Systems
Context architecture is fundamentally a systems discipline. The three-layer model from post one (platform context, technical context, operational context) needs to work as an integrated whole, not three independent efforts.
Systems thinking means understanding how a change in one layer affects the others. A new platform capability (platform context) requires updated governance rules (operational context) and potentially new data connectors (technical context). A regional market expansion changes business rules, data definitions, and activation workflows simultaneously. A vendor migration shifts where technical context lives, which changes what operational context structures need to reference.
Where integration breaks down most often: Platform upgrades that outpace governance updates, leaving teams using new capabilities without updated rules. Technical context (data pipelines, connectors) that doesn’t reflect current business rules because the rules changed after implementation and nobody updated the technical layer. Operational context that gets documented once during a project and never updated, slowly drifting from reality until a downstream failure forces a refresh.
The Everything skill is also the practice of maintaining a current map of how the three layers connect and where the dependencies live. Not a static architecture diagram created during a planning phase. A living understanding of how context flows through your operation and where the weak points are.
Action if you rated yourself “developing” or “not yet started”: Draw a simple map of your current marketing operation showing where platform context, technical context, and operational context interact. Identify the three weakest connection points, places where a change in one layer is most likely to break something in another. Those connection points are your highest-priority areas for systems-level attention.
Explain: Make the Work Visible
One of the biggest challenges for context architects is that the work is invisible until it breaks. Leadership sees the outcomes (faster activation, fewer errors, more consistent customer experience) but rarely connects those outcomes to the underlying context architecture enabling them. When context architecture works well, it looks like everything is running smoothly. When it’s absent, failures get attributed to tools, teams, or execution rather than to the missing context layer.
The Explain skill is making the work visible: connecting context architecture to business metrics leadership cares about, framing the investment case, and showing what happens when context is absent or poorly maintained.
How to communicate context architecture value: Frame around outcomes, not infrastructure. Leadership responds to speed (time to market reduced by X%), consistency (error rates across markets dropped by Y%), risk reduction (compliance gaps identified before they became incidents), and experimentation capacity (team freed Z hours per cycle for testing). Connect these outcomes explicitly to the context structures that enabled them. Without the connection, leadership will attribute improvements to the tools or the team’s effort, not to the architecture making both more effective.
Action if you rated yourself “developing” or “not yet started”: The next time your team delivers a strong result, trace it back to the context structure that supported it. Document the connection in one paragraph. Share that paragraph with leadership alongside the result. Over time, this builds organizational understanding that context architecture is a value driver, not overhead.
Evolve: Build for Adaptation
Context architecture is a living system. Business needs change. AI capabilities mature. Markets expand or contract. Regulatory environments shift. Customer behaviors move. The agent of context treats their structures as adaptive, not fixed.
This connects to Florian Delval’s point about the spectrum of AI autonomy shifting over time. As AI agents take on more autonomous decision-making roles, the context they depend on needs to evolve in parallel. Context structures designed for human-driven activation carry certain assumptions about how decisions get made, what needs to be explicit versus implicit, and how much detail the downstream consumer needs. Agent-driven activation changes all of those assumptions. What a human marketer infers from a brief description, an AI agent needs to receive as structured input.
How to build evolution into your practice: Establish review cadences for your core context structures (quarterly for high-impact structures, semi-annually for stable ones). Define trigger events that signal a context structure needs updating outside the regular cadence: a new market launch, a new AI capability deployment, a new regulatory requirement, a post-mortem revealing a context gap. Assign ownership for each context structure so evolution isn’t dependent on one person remembering to check.
Action if you rated yourself “developing” or “not yet started”: Identify the three most critical context structures in your operation (data definitions, governance rules, activation templates). Check when each one was last updated. If the answer is “during the original project” or “I’m not sure,” schedule a review within the next 30 days. Stale context is worse than missing context because teams trust it without knowing it’s outdated.
PUTTING IT ALL TOGETHER: CONTEXT ARCHITECTURE MEETS DECISIONING
The 8 Es describe the skills and habits of the agent of context. But those skills need a home, a system where context feeds into real customer decisions at scale. Martech Square’s Customer Decisioning Blueprint provides that system.
This Blueprint treats AI as a capability within a broader decisioning architecture, not its center of gravity. The limiting factor in most organizations isn’t model sophistication. It’s decision ownership, consistent logic across channels, and alignment between strategy and execution. Those are the problems context architecture solves. The Blueprint provides the structure showing where that context goes.
Here’s how the 8 Es feed the Blueprint’s core components:
Explore and Engage feed the Blueprint’s Data Foundation and Business Context layers. The discovery and stakeholder work from Phase 1 produces the inputs these layers need: mapped data, validated business rules, and formalized domain knowledge from cross-functional teams.
Exchange and Experiment feed the Decisioning Orchestration Spine. The reusable context structures from Phase 2, tested and iterated against real outcomes, become the operational logic connecting decisions to channels, journeys, and customer moments.
Embrace feeds the Decisioning Intelligence layer. Selective adoption of AI capabilities, decisioning engines, and context engineering tools strengthens the system’s ability to generate predictions, assessments, and treatment options grounded in structured context.
Everything and Explain feed the Governance and Adaptation Loop. Systems thinking ensures the decisioning architecture stays integrated across layers. Organizational visibility ensures it stays funded, staffed, and valued.
Evolve is the engine keeping the entire system adaptive. As AI capabilities move along the autonomy spectrum, from agent-assisted decisioning toward more autonomous operations, context structures need to evolve in parallel. The Blueprint’s emphasis on human stewardship of the decisioning framework reinforces the same principle: as AI agents take on more autonomous roles, the human responsibility for context architecture becomes more important, not less.
Revisit Your Self-Assessment
Return to the diagnostic you completed at the start. Where are you strong? Where are you developing? Where haven’t you started?
The skills making someone an effective agent of context are not abstract. They are the same skills strong martech practitioners have been building for years: data strategy, cross-functional coordination, governance design, systems thinking, and the ability to translate business reality into structures teams and systems use. The agentic era raises the stakes on these skills and makes the investment in developing them deliberately the highest-return career move a marketer working with technology can make.
For each E where you rated yourself “developing” or “not yet started,” the action items throughout this post give you a concrete starting point. Pick one. Start this week. Context architecture is built through consistent, small-scale practice, not through a single transformation initiative.
If you’re looking for a structured path to build these capabilities across all eight dimensions, the Experimental Marketer Framework was designed for exactly this kind of development: moving from tactical execution toward strategic, technology-informed expertise through deliberate practice and experimentation.
The agentic era doesn’t reduce the need for this work. It makes this the foundation everything else runs on.
Really solid piece. The core insight resonates: context is the bottleneck, not the AI. What I see in practice is that most teams jump straight to tools and skip the messy work of mapping what actually matters for decisions. Your Explore phase nails that. And the point about stale context being worse than missing context is something more people need to hear. Great framework. 🦊🎓
I love it! This article was incredibly insightful.