--- title: "How to Scale an Agent Context Layer in 2026" url: "https://atlan.com/know/ai-agent/how-to-scale-agent-context-layer/" description: "Learn how to scale an agent context layer: bootstrap context once, package it into reusable repos, test with evals, and serve it across every agent runtime." author: "Ayswarrya G" author_role: "Contributing Writer, Data Engineering & Metadata" published: "2026-07-14" updated: "2026-07-14T00:00:00.000Z" --- --- Build Your AI Context Stack Get the blueprint for implementing context graphs across your enterprise. This guide walks through the four-layer architecture, from metadata foundation to agent orchestration, with practical implementation steps for 2026. Get the Stack Guide --- ## How scaling an agent context layer works Scaling an agent context layer requires turning scattered business context into one shared, governed source of truth that serves accurate, consistent, updated context to multiple agents. Atlan's Context Layer for AI is built to make exactly this possible. **How scaling an agent context layer works**: * **Scope first, then standardize**: Start with one bounded use case, not a company-wide model. * **Bootstrap context from existing systems**: Draft context from query history, BI semantics, and lineage. * **Package into reusable Context Repos**: Build a domain repo once; let many agents consume it. * **Use the same context across runtimes**: Serve one approved substrate to every platform, not per-agent copies. * **Test the context**: Validate context against real business questions before rollout. * **Improve from traces and feedback**: Route runtime corrections back into the next version. * **Keep humans in the approval loop**: Let people resolve conflicts and certify what ships. --- The problem today is keeping many agents, across many platforms, aligned to the same business truth as the business changes, the operational reality most [enterprise context management](https://atlan.com/know/context-management-cdo-enterprise-scale/) programs eventually hit at scale. A context layer scales when teams stop rebuilding business context inside every new agent and instead create a shared, portable, tested source of truth that can serve many runtimes at once. Atlan's context layer for AI builds that living, shared source of truth and makes it reusable (with Context Repos), deployable across platforms ([MCP](https://atlan.com/know/what-is-model-context-protocol/)) and open interfaces, and continuously improvable (evals, traces, and human-guided refinement). --- ## Why is it so hard to scale the AI agents? Most organizations stall because they lack shared, living context infrastructure for their agents to read from. A [multi-agent system](https://atlan.com/know/single-agent-vs-multi-agent-systems/) fails because each one reads from its own private, static version of business context that drifts the moment definitions, schemas, or ownership change. An [agent context layer](https://atlan.com/know/agent-context-layer/) is the governed business context an AI agent reads before it acts: definitions, metrics, relationships, lineage, ownership, and policies that tell the agent what your data means and what is trusted. Building context for demo agents is quick and often effective as teams can hand-curate a few definitions, and get a convincing result in an afternoon. The difficulty appears at the second, tenth, and thirtieth agent. A 2025 McKinsey report found that just 10% of organizations had scaled their AI agents in any individual function ([McKinsey, "The state of AI in 2025," 2025](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai)). Each new agent re-creates context in its own format, and [definitions drift](https://atlan.com/know/context-drift-detection/). According to a 2025 arXiv study co-authored by Google DeepMind and Google Research scientists ([Kim et al., "Towards a Science of Scaling Agent Systems," 2025](https://arxiv.org/pdf/2512.08296)), independent [multi-agent systems (MAS)](https://atlan.com/know/multi-agent-coordination-patterns/) amplify errors 17.2x through unchecked error propagation, while centralized coordination with a shared context architecture contains this to 4.4x. Coherence across many agents requires shared context, not repeated context. Scaling an agent context layer successfully means agents across different teams and platforms all access the same trustworthy, business-specific knowledge grounded in real, current infrastructure, not context re-invented per agent. --- For Data Leaders Evaluating Where to Start Atlan's CIO guide to context graphs walks through a practical four-layer architecture from metadata foundation to agent orchestration. Get the CIO Guide --- ## What practical scaling looks like Scaling in practice comes down to eight moves: scope the first use case, bootstrap from what already exists, package it once, serve it everywhere, test it, improve it, and keep a human on the approval loop. ### 1. Start with one bounded use case, not a company-wide abstraction Teams scale faster when they start with a specific high-value use case or domain and create a bounded unit of context around it, rather than trying to build a universal all-enterprise model first. Scope first, then standardize. This helps you iterate and expand the agent context layer by relying on real business needs and proven outcomes. ### 2. Bootstrap context from systems that already contain it Bootstrapping business context is the hard part because useful context is already scattered across warehouses, dbt models, BI tools, pipelines, docs, wikis, Slack threads, and people's heads. Instead of starting from scratch, enterprises should unify those signals in the [Enterprise Data Graph](https://atlan.com/know/how-to-build-context-graph-enterprise-ai/) and use AI to draft the first version of the context layer from query history, BI semantics, lineage, definitions, and related enterprise signals. ### 3. Package context into reusable Context Repos A [Context Repo](https://atlan.com/know/ai-agent/context-repository-for-ai-agents/) is the reusable, portable, versioned source of truth for a given use case or domain, so teams stop [context-engineering](https://atlan.com/know/what-is-context-engineering/) separately into every individual agent. To scale an agent context layer, enterprises should build a bounded repo once and let agents consume it. ### 4. Use the same context across multiple runtimes Enterprises rarely standardize on one agent platform. Teams experiment with [Snowflake Cortex](https://atlan.com/know/context-layer-for-snowflake-cortex/), [Databricks Genie](https://atlan.com/know/ai-agent/databricks/genie-ontology-and-atlan-context-layer/), Claude, Copilot-style surfaces, and custom agents at the same time. The scaling problem appears when [teams context-engineer](https://atlan.com/know/context-engineering/context-engineering-for-multi-agents/) separately into each of those environments. They end up with different platforms "speaking slightly different versions of the truth." To scale agent context layers, you need: * **Single substrate, many consumers**: One approved context substrate that feeds every runtime. * **Consistent answers**: The response shouldn't change because a user asked in Cortex instead of Genie. * **No per-platform rebuild**: New runtimes consume existing context rather than re-authoring it. The runtime a team picks should be a delivery decision. The context underneath it should not have to be rebuilt for each choice. ### 5. Solve the three walls of scale The best way forward is to adopt a framework that solves the following three walls of scale: * **Context bootstrapping**: Easy to ship an agent, hard to give it the right business context. * **Testing hell**: [Hard to know whether the context is good enough](https://atlan.com/know/ai-agent/context-layer/context-layer-evaluation-criteria/) before users lose trust. * **Portability and scalability**: [Hard to move from one or two agents to many](https://atlan.com/know/how-to-build-ai-agent-harness/) without rebuilding the same context repeatedly. Each wall needs a different mechanism: bootstrapping from existing systems, evals before rollout, and portable repos for reuse. ### 6. Test the context, not just the model A context layer that cannot be [regression-tested](https://atlan.com/know/ai-agent-evaluation-benchmarks-and-metrics/) as it changes doesn't really scale; it just drifts more slowly. Scaling requires a repeatable quality loop grounded on dashboards and reports your teams already trust. So, generate [evals and simulations](https://atlan.com/know/llm-evaluation-frameworks-compared/) from dashboards, reports, queries, and real business questions. Then use failures to identify exactly what context is missing. You can use a context layer for AI to run these at scale. For instance, [Atlan's Context Engineering Studio](https://atlan.com/context-engineering-studio/) auto-generates eval suites from existing dashboards and uses failures to pinpoint exactly what context is missing, before users encounter it. ### 7. Improve the context from traces and runtime feedback A context layer should be treated as living infrastructure that compounds as more agents use it and more feedback routes back into it. You cannot just "build and freeze." So, deploy the repo, observe agent behavior, capture [traces and corrections](https://atlan.com/know/ai-observability/), and turn those signals into the next version of the shared context. ### 8. Keep humans in the approval loop AI should bootstrap, simulate, and suggest fixes, but humans should refine edge cases, resolve conflicts, and approve what becomes production context. Human review lets enterprises reuse context broadly without letting silent errors multiply across every consuming agent. ### How do you scale across platforms like Cortex, Genie, and custom agents? Cross-platform reality is where most scaling plans meet friction. Let's explore two common scenarios, using Snowflake/Databricks or custom agents to build enterprise-wide agent context layers. Both scenarios come down to the same question: does your business context live inside the platform, or above it? When you are scaling an agent context layer, the platform an agent runs on should be a delivery choice, not the place your definitions live. The moment context is authored inside a specific runtime, every comparison, migration, or parallel deployment forces you to rebuild and reconcile that context by hand. **Scenario 1: Building agent context layers with Snowflake Cortex or Databricks Genie** Many enterprises consider Snowflake Cortex and Databricks Genie to unify their fragmented data and AI ecosystems. They actively compare the two, run them side by side while deciding where to standardize. Both work well within their perimeters. The trap is engineering context natively into each platform, which locks business logic to that vendor's representation. When they have to migrate, they must rebuild context: recreate definitions and reconcile answers, again. To scale agent context layer, you should [build context once in a vendor-agnostic platform](https://atlan.com/know/ai-agent/agent-context-layer-design/) that's open, interoperable, and vendor-agnostic. Use an [open protocol like MCP](https://atlan.com/know/mcp/why-mcp-matters-for-ai-agents/) to expose the same approved context to each platform. **Scenario 2: Building agent context layers with custom agents** Beyond the major data platforms, teams build agents on custom frameworks like [LangGraph](https://atlan.com/know/ai-agent/ai-agent-memory/what-is-langgraph/), Azure-managed OpenAI agents, and domain-specific internal copilots, often several at once. Each interface is different, but all of them need the same business context to answer correctly. Building context from scratch for each tool leads to the same "multiple versions of the same business context" problem. This is where a shared context substrate with an [MCP-compatible server](https://atlan.com/know/mcp-server-implementation-guide/) makes a difference. It provides a single foundation with consistent business context across interfaces, whichever framework a team picked. --- ## How Atlan specifically enables scaling agent context layers Atlan's [context layer for AI](https://atlan.com/know/ai-readiness/context-layer-101/) helps teams build context once, package it into reusable repos, validate it with evals, [activate it across many agent surfaces](https://atlan.com/know/enterprise-ai-context-management-tools/), and keep it current through traces and human-guided updates. ### Enterprise Data Graph Atlan unifies context from data systems, BI tools, pipelines, business applications, and other enterprise sources into one graph. ### AI bootstrapping Atlan AI drafts descriptions, [semantic logic](https://atlan.com/know/semantic-layer/), metrics, [ontology](https://atlan.com/know/what-is-ontology-in-ai/) elements, and related context artifacts, so that teams can get to a usable first version faster. ### Context Engineering Studio This is the build-test-deploy workspace where teams draft, refine in a bounded space, simulate against real questions, and deploy one repo to many targets. ### Context Repos These are the reusable domain-scoped, portable, versioned units that many agents can consume. ### Evals and simulations These let teams know whether context is working before broad rollout. They make the context layer maintainable [as it grows across teams and use cases](https://atlan.com/know/context-management-strategies-enterprise-ai/). ### MCP, APIs, and open deployment These are critical because scale is cross-platform. Context should flow into whatever runtime the enterprise chooses, instead of getting trapped in one platform's native representation. ### Traces, observability, and feedback loops These capture [agent behavior](https://atlan.com/know/ai-agent-observability/) and route corrections back into context, so the layer improves under production use. --- ## Real stories from real customers building and scaling agent context layers Two data leaders describe what a governed context layer looks like once it's running in production, not just in a slide deck. ### How Workday is building an AI-ready semantic layer "All of the work we did to get to a shared language at Workday can be leveraged by AI via Atlan's MCP server. As part of Atlan's AI Labs, we're co-building the semantic layer that AI needs, with new constructs like context products." Joe DosSantos, VP of Enterprise Data & Analytics, Workday Watch Now ### How DigiKey built a unified, sovereign context layer for its data and AI estate "Atlan is our context operating system to cover every type of context in every system, including our operational systems. For the first time we have a single source of truth for context." Sridher Arumugham, Chief Data & Analytics Officer, DigiKey Watch Now Ready to See It Run at Scale? Estimate the cost of unscaled, per-agent context work today, and what a shared context layer saves as you move from one agent to many. Try the ROI Calculator --- ## Moving forward with scaling the agent context layer Treat business context as a shared, governed system every agent can read, and the cold-start, testing, and portability walls stop being a fight you refight on every new initiative. Treat context as per-agent project work instead, and you hit all three walls again on the next rollout. Bootstrap context once, package it into portable repos, test it with evals, distribute it across runtimes, and improve it from traces. This is the architecture Atlan is built around, turning context into a shared, vendor-agnostic, living infrastructure for agents. --- ## FAQs about the agent context layer ### 1. What does scaling an agent context layer involve? Scaling an agent context layer involves bootstrapping context from existing systems, packaging it into versioned reusable units, testing it before deployment, distributing it through open protocols, and improving it from runtime feedback. The defining shift is treating context as shared, living infrastructure. ### 2. Why do AI agents that work in demos fail at scale? Most demo agents rely on hand-curated context that one person owns and maintains. That approach does not survive contact with ten or twenty agents, where definitions diverge, testing becomes unbounded, and each new agent re-creates context from scratch. The failure is usually a context problem, not a model problem. ### 3. What is a Context Repo, and why does it matter for scale? A Context Repo is a reusable, portable, versioned, domain-scoped unit of business context that any compatible agent can consume. It matters because it stops teams from engineering context separately into every individual agent. Build it once for a domain, version it like code, and let many agents read from the same source of truth. ### 4. How do you keep answers consistent across Cortex, Genie, and custom agents? Engineering context natively into each platform is what causes the divergence in the first place. Keep the context outside any single runtime and serve it to all of them through an open protocol such as MCP. When every platform consumes the same approved context substrate, the answer does not change based on where the user asked. ### 5. How do you test a context layer before deployment? The most reliable approach generates evaluation suites from the dashboards, reports, and queries your teams already trust, then runs the agent against those questions. When an eval fails, the gap points to a specific missing relationship, synonym, or default rule in the context. Evals should persist across versions so every change is benchmarked against the same standard. ### 6. What keeps a context layer accurate after it ships? Treat it as living infrastructure rather than a one-time build. Capture production traces, turn user corrections into suggested context updates, and turn confirmed-correct answers into regression tests. Routing this feedback back into versioned context is how the layer self-corrects as schemas, definitions, and lineage change. --- ## Sources 1. McKinsey, "The state of AI in 2025: Agents, innovation, and transformation," 2025. https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai 2. Kim et al., "Towards a Science of Scaling Agent Systems," arXiv, 2025. https://arxiv.org/pdf/2512.08296