What if our data lives in multiple systems?

This is the default enterprise state. The governance step (Step 2) must inventory all candidate sources across systems. MCP then provides standardized access without requiring custom M x N integrations.

How do we know when our context is good enough for production?

The 80% context accuracy threshold is a practical heuristic. Measure it by generating synthetic test questions from your dashboards and comparing AI answers against ground truth using RAGAs or TruLens.

How to Build a Context Engineering Framework: 6-Step Guide

Q: Can we build a context engineering framework without Atlan?

Yes. The governance step can be done with any data catalog or manually for smaller data estates. For enterprise data estates with thousands of assets and multiple AI use cases, automated metadata management and MCP delivery reduce governance setup from months to weeks.

Building a context engineering framework means governing and delivering the right data to your AI system at the right moment. The six steps are: (1) define your AI’s context requirements, (2) audit and govern your context sources — the step most guides skip — (3) design the retrieval layer (RAG, MCP, knowledge graphs), (4) build context validation, (5) implement delivery and caching, and (6) monitor and version the context layer over time. Every step depends on the one before it. And the governance step is the one that determines whether all the others work.

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Quick overview:

Field	Value
Time required	8-12 weeks for a functional first version (one domain); ongoing refinement
Difficulty	Intermediate
Prerequisites	Data catalog or inventory, LLM access, defined use case, named data domain owners
Tools	Atlan, Pinecone / Weaviate / pgvector, LangChain / LlamaIndex, MCP-compatible server, RAGAs or TruLens, Redis

Why build a context engineering framework?

AI systems fail not because the model is wrong, but because the context it receives is ungoverned. Gartner projects that at least 30% of AI projects will be abandoned after proof of concept by the end of 2025 due to poor data quality, inadequate risk controls, or unclear business value — and a significant share of AI agent failures trace directly to data quality issues, not model limitations or architecture choices. A context engineering framework is the infrastructure layer that governs what the AI sees: which data sources are trustworthy, what business terms are canonical, which context reaches the agent at inference time.

This is what separates context engineering from prompt engineering — not rewriting the question, but building the governed data layer beneath every answer.

The cost of skipping governance: Teams that build retrieval without governing sources produce agents that retrieve confidently and answer incorrectly. The architectural plumbing is complete; the data flowing through it is wrong. Stale business definitions. Metrics defined differently in Salesforce and the data warehouse. Tables with no documented owners. The AI has no way to know which version of a KPI to trust — because nobody decided.

The outcomes when you get it right:

5x improvement in AI response accuracy (Workday with Atlan MCP server)
Below 80% context accuracy, business users reject the system. Above 80%, the adoption flywheel begins — accuracy creates trust, trust drives usage, usage generates corrections, corrections improve accuracy.

Who this guide is for: Data and platform engineers managing AI infrastructure, AI architects designing multi-agent systems, data architects who own semantic layers and lineage graphs, and CDOs accountable for AI readiness.

Prerequisites before you start

Organizational prerequisites

- Named executive sponsor: someone accountable for governing the data the AI uses - Data governance baseline: policies on ownership, certification, and access control exist, even informally - Named data domain owners: governance only works with clear accountability

Technical prerequisites

Review the [core components of a context layer](https://atlan.com/know/core-components-context-layer/) before starting. You will need:

Data catalog or asset inventory (to classify and govern context sources)
LLM access via API or self-hosted deployment
Vector database for semantic retrieval (optional at Step 1, required by Step 3)
Orchestration framework: LangChain, LangGraph, LlamaIndex, or equivalent

Team and time

Role	Responsibility
Data/platform engineer	Source governance and retrieval architecture
AI or ML engineer	Retrieval layer, validation, delivery
Domain expert	Owns the business definitions being encoded
Data governance lead	Optional but accelerates Step 2 significantly

Step	Typical time
Define context requirements	1-2 weeks
Audit and govern sources	2-4 weeks (longest step)
Design retrieval layer	1-2 weeks
Build validation	1 week
Implement delivery and caching	1 week
Integration testing	1 week
Monitor and version (ongoing)	Continuous
Total to first functional version	8-12 weeks

Note on sequencing: Steps 2 and 3 have bidirectional dependencies. You cannot set freshness SLAs (Step 2) without some clarity on retrieval patterns (Step 3), and you cannot choose retrieval patterns without knowing what sources you are governing. Expect one iteration loop between Steps 2 and 3 before locking both.

Step 1: Define what context your AI system needs

What you will accomplish

A precise map of the context types your AI agent requires, tied to specific use cases and the decisions it must make. This step sounds obvious. Most teams rush it — and make every downstream step harder as a result.

Time required: 1-2 weeks

Why this step matters: You cannot audit or govern what you have not defined. If you cannot specify what the agent needs to know to answer a question correctly, you cannot evaluate whether your retrieval delivers it.

How to do it:

Document each AI use case: what question does the user ask? What decision does the agent make?
For each use case, produce a context map — a one-page document per use case that answers:
- Instructions needed (system behavior rules, constraints)
- Retrieved knowledge needed (domain definitions, policies, reference data — and which source system)
- Memory needed (prior interactions, session state — and how far back)
- Tools available (which external systems the agent can call)
- State dependencies (workflow context, user identity, permissions)
For each context type, identify the source system: which database, knowledge base, API, or document
Define freshness and latency requirements: how stale can this context be before it causes errors?
Define the access control model: which users’ context can the AI retrieve, and for which users

Validation checkpoint — you will know this step is done when:

[ ] Every agent use case has a context map document
[ ] Every context type has an identified source system
[ ] Freshness SLAs are defined per source
[ ] Access control model is documented
[ ] A domain expert has reviewed and confirmed the definitions are accurate

Common pitfalls:

Defining context at the query level (“the user asks about revenue”) instead of the data level (“revenue = recognized_revenue_q4 from the Salesforce-to-warehouse pipeline, owned by the Finance domain”)
Skipping this step and moving directly to retrieval setup — which turns every later step into guesswork

Step 2: Audit and govern your context sources

Where most context engineering frameworks fail — and where building correctly changes everything

Time required: 2-4 weeks

Most how-to guides for context engineering start at retrieval. This is the sequencing error that causes production failures. The retrieval architecture is complete; the data flowing through it is ungoverned.

Consider what “ungoverned” means in practice: stale business definitions that contradict each other across teams; metrics defined differently in Salesforce and the data warehouse; tables with no documented owners; lineage gaps that obscure where data transforms; no certification process. The AI retrieves from all of these simultaneously and has no mechanism to know which source to trust.

The Workday case makes this precise. Workday built a revenue analysis agent with full engineering resources. The agent could not answer a single question. Not because the retrieval architecture was wrong — but because the agent did not know what recognized_revenue_q4 meant inside Workday’s data environment, which tables were authoritative, or how revenue recognition mapped to their organizational hierarchy. The context engineering was sound. The data was not governed. Once Atlan’s MCP server provided the semantic layer — the governed glossary and certified assets — accuracy improved 5x.

How to do it:

Inventory all candidate context sources — databases, wikis, BI dashboards, semantic layers, documentation, APIs. Capture a snapshot: asset name, owner, last updated, location. This becomes your baseline for drift detection in Step 6.
Classify by trust level — certified (verified, owned, current); provisional (used but unverified); deprecated (exists but unreliable). Every source gets a classification. Retrieval in Step 3 should draw only from certified and provisional sources, with provisional sources flagged for validation in Step 4.
Establish ownership — every context source needs a named owner with a defined update cadence. No owner means no update means stale context.
Document lineage — trace where each data asset comes from and how it transforms. For enterprise AI, column-level lineage is required. See context layer for data engineering teams for the engineering-specific governance pattern.
Standardize business terms — create or import a business glossary. What is “revenue” in your organization? What is “customer”? What is “active user”? These are governance decisions the AI will resolve incorrectly if you do not make them first.
Define access controls — which AI agents access which context, for which users, under which conditions. For regulated industries: context layer for financial services covers compliance-grade governance; context layer for healthcare AI addresses HIPAA audit trail requirements.
Set freshness requirements — informed by the SLAs from Step 1 and the retrieval patterns you are evaluating for Step 3. Expect to revisit these after Step 3 is scoped.

Where Atlan fits in this step: Atlan’s metadata lakehouse provides asset catalog (inventory and classification), business glossary (canonical term definitions), certified assets (verified, current, owned), column-level lineage, active metadata (real-time usage signals), and access governance. Workday cataloged 6 million assets and established 1,000 glossary terms via Atlan — the shared language that made their revenue analysis agent work. Atlan accelerates Step 2 from months to weeks for enterprise data estates; the step is achievable without it using any catalog with governance capabilities, but slower.

Validation checkpoint — you will know this step is done when:

[ ] All candidate sources are inventoried with a baseline snapshot captured (for Step 6 drift detection)
[ ] Every source has a trust classification (certified / provisional / deprecated)
[ ] Every context source has a named owner
[ ] Business glossary covers all key terms the AI will retrieve
[ ] Lineage is documented for critical data assets
[ ] Access control policy is defined and enforceable
[ ] Freshness SLAs are set (subject to revision after Step 3)

Common pitfalls:

Starting retrieval (Step 3) before governance (Step 2) — the most common and most costly sequencing error
Assuming a semantic layer or BI tool is “governed” without verifying ownership and certification processes
Centralized ownership: governance fails when one team tries to document everything. Federated ownership on shared infrastructure is the required model — domain experts own definitions, the platform provides the infrastructure

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Step 3: Design the context retrieval layer

Retrieval is only as reliable as what it retrieves from

Time required: 1-2 weeks

Governance precedes retrieval architecture — that is the reason Steps 1 and 2 come before this one. Once sources are governed, you can build retrieval that actually works. This step has one iteration loop back to Step 2: after scoping your retrieval patterns, revisit and finalize the freshness SLAs and access controls you set provisionally in Step 2.

The retrieval layer has three core architectural components:

RAG, MCP, and knowledge graphs — how to choose:

Retrieval pattern	Best for	When to use
RAG (vector + keyword hybrid)	Unstructured documents, policies, knowledge bases	High-volume semantic search; precision-recall tradeoff is critical
MCP (Model Context Protocol)	Structured metadata, governed data assets, tool access	Multi-agent environments; standardized interface across M+N systems vs M x N integrations
Knowledge/context graph	Complex entity relationships, multi-hop reasoning, entity disambiguation	Business glossary traversal, lineage-aware retrieval, when facts have temporal dependencies

Most production frameworks use RAG and MCP together, with knowledge graphs added where multi-hop reasoning is required. They are not alternatives.

Architecture decisions:

RAG pipeline — chunking strategy (small for precision, large for richness), embedding model selection (OpenAI, Cohere, open-source), vector database (Pinecone, Weaviate, Chroma, pgvector). Hybrid search combining keyword and semantic retrieval consistently outperforms either alone in enterprise settings. For knowledge graph retrieval: Neo4j or equivalent graph database.
Memory architecture — hierarchical: short-term (current context window), working memory (session state), long-term (persistent knowledge). Use importance scoring to decide what gets promoted to long-term storage.
MCP integration — Model Context Protocol is now the standard for tool access, supported by major AI providers including Anthropic, OpenAI, and Google. Key consideration: each MCP server adds token overhead. A single complex schema can consume 500+ tokens; 90 tools can require 50,000+ tokens before reasoning begins. Audit token costs per tool. Also: write MCP tool descriptions for model retrieval, not human readability — models retrieve on semantic similarity, so overlapping or imprecise descriptions cause silent routing failures. See the context engineering platforms comparison for tool selection guidance. For implementation detail: how to implement an enterprise context layer.
Progressive disclosure — load context in tiers: discovery metadata at startup, full instructions when activated, supporting materials only during execution. This prevents context window bloat without sacrificing depth.

Validation checkpoint — you will know this step is done when:

[ ] Retrieval pattern selected and justified for each context type (RAG, MCP, knowledge graph, or combination)
[ ] Vector database, embedding model, and (if applicable) graph database chosen
[ ] MCP integration scoped with token cost estimates per tool
[ ] MCP tool descriptions tested for model retrieval quality, not just human readability
[ ] Progressive disclosure strategy documented
[ ] Baseline retrieval evaluation run: measure precision/recall on a sample query set against governed sources from Step 2
[ ] Freshness SLAs and access controls from Step 2 finalized after retrieval scope is locked

Step 4: Build context validation and quality checks

Retrieved context and trusted context are not the same thing

Time required: 1 week

Retrieved context can be stale, conflicting, irrelevant, or poisoned (a prior hallucination stored as fact in long-term memory). Retrieval without validation passes all of this through to the model. Validation is not optional — it is the gap between a prototype and a production system.

What to build:

Relevance scoring — filter retrieved chunks by threshold before injecting. Retrieved does not mean relevant. Set a minimum relevance score and discard everything below it.
Freshness checks — validate source timestamp against the freshness SLA defined in Steps 1 and 2. An 18-month-old metric definition is technically retrievable and practically wrong.
Conflict detection — when two sources return contradictory information, surface the conflict rather than silently choosing one. The agent should not arbitrate between ungoverned sources.
Hallucination guard on memory writes — never write agent output back to long-term memory without human-in-the-loop validation. Context poisoning — where hallucinations compound through reuse across future interactions — is a documented production failure mode that most implementations skip. This is the rare failure mode: most guides do not mention it; the risk is real.
Human-in-the-loop gates — for high-stakes context (financial definitions, compliance terms), require human validation before certifying. Automated retrieval is not sufficient for definitions that carry regulatory weight.

Tools: RAGAs, TruLens, custom validation pipelines, human review workflows

Validation checkpoint — you will know this step is done when:

[ ] Relevance threshold set and tested on representative queries
[ ] Freshness validation runs on every retrieval, blocking stale content per SLAs
[ ] Conflict detection surfaces rather than silently resolves contradictions
[ ] Memory write pipeline includes a human review gate before long-term storage
[ ] High-stakes context sources have certification requirement defined and enforced

Step 5: Implement context delivery and caching

Context assembled at inference time, not pre-processed in bulk

Time required: 1 week

Delivery is how validated context reaches the AI agent at inference time. The key principle: just-in-time assembly — context assembled dynamically from the specific query, not pre-processed in bulk. Concretely, this means the orchestration layer (LangChain, LangGraph, or equivalent) assembles context per request: it routes the query to the right sources, retrieves validated chunks, applies caching for stable content, and packages the assembled context for the model’s context window.

Caching strategy:

Cache stable context (business glossary terms, standard operating procedures, policy definitions): reduces latency and cost for frequently accessed content
Skip caching for volatile context (real-time metrics, live system state): freshness requirements cannot be guaranteed with cached content

Standard interfaces via MCP: Expose context through an MCP server so multiple AI applications consume the same governed context layer simultaneously. DigiKey’s model: Atlan as the context operating system, one MCP server delivering governed context to all AI models in production. All major sources onboarded in weeks. The multi-agent architecture matters here: when multiple agents work on the same task, design for concurrent access to a shared MCP server — not per-agent re-retrieval. Per-agent retrieval introduces inconsistency; shared governed context from a single source eliminates it.

Context Studio (Atlan): Atlan’s Context Studio accelerates delivery setup: bootstraps from existing infrastructure (metadata, BI dashboards, SQL, documentation), generates evaluation test cases, identifies gaps (missing relationships, unresolved synonyms), and packages context into versioned repositories deployable via MCP.

Validation checkpoint — you will know this step is done when:

[ ] Orchestration layer configured for just-in-time context assembly per request
[ ] Caching strategy implemented and tested: stable context cached, volatile context bypasses cache
[ ] MCP server deployed and accessible to all intended AI applications
[ ] Multi-agent concurrent access verified under load
[ ] Context accuracy measured against representative query set; target: above 80%. To measure: generate synthetic test questions from your dashboards and reports, compare AI answers against ground truth for your domain’s critical queries using RAGAs or TruLens. If below 80%, return to Steps 3 and 4 to tighten retrieval and validation before production deployment.

Step 6: Monitor, version, and update your context layer

Context is not a one-time build — it is an operational discipline

Time required: Continuous

Source data changes. Business definitions evolve. Regulatory requirements shift. Teams that deploy context once and never update it accumulate context rot: stale, contradictory, outdated information that causes measurable performance degradation over time.

What to build:

Context versioning — treat context like code: full history, branching, staged rollouts, rollbacks when a context update breaks agent behavior. Atlan’s Context Studio implements git-like versioning for context definitions natively.
Production traces — visibility into exactly what context the agent accessed at decision time. Debugging what the agent actually saw is the top practitioner pain point. Without traces, debugging is guesswork.
Drift detection — compare current source state against the baseline snapshot captured in Step 2 (this is why Step 2 requires capturing a baseline). When sources diverge from that baseline — a glossary term updated, a table owner changed, a pipeline modified — the system flags the change for review.
Feedback loops — every user correction to an AI response is a signal about context quality. Build the pipeline from user feedback to context gap identification to context update and re-certification.
Staleness alerts — proactive notification when context sources have not been updated within their defined freshness window. Do not wait for an agent error to discover stale context.

Tools: LangSmith, Langfuse, Atlan Context Studio (git-like versioning and observability), feedback collection pipelines, drift detection

Common implementation pitfalls

The most common failure is not architectural — it is sequencing and data quality. Teams build retrieval before governing the data, discover the data is untrustworthy, and blame the model.

Starting retrieval before governance

The number one mistake. The retrieval pipeline works; the data flowing through it is contaminated. Agents that retrieve from uncertified, unowned, or contradictory sources produce confident wrong answers. Govern first. The architectural work is not wasted — but it cannot be trusted until Step 2 is done.

Treating context as static (schema drift breaks agents)

Context is not a prompt. It is a living layer that changes as the business changes. Teams that deploy once and never update accumulate context rot: stale, contradictory, outdated information that degrades agent performance over time. Step 6 is not optional — it is the ongoing operational discipline that keeps the framework working.

Writing MCP tool descriptions for humans, not models

MCP tools are described for human readability by default. Models retrieve on semantic similarity — if tool descriptions overlap or use imprecise language, routing fails silently. Write tool descriptions with the model’s retrieval pattern in mind: precise, non-overlapping, semantically distinct from other tools in the server. This is an underappreciated failure mode with no visible error.

Oversized, generic context (noise exceeds signal)

More context is not better context. Oversized, generic context files bury critical rules in noise. The counter-intuitive finding: more context in the wrong structure hurts more than less context in the right structure. Select, compress, order, isolate, format. Do not dump.

Building for one agent only (does not scale)

Context built for a single agent becomes a maintenance liability when the second agent ships. Design for federated ownership on shared governed infrastructure from the start: domain experts own definitions, the platform provides governance infrastructure, AI teams consume through standard interfaces.

Real stories: context engineering frameworks in production

"We're excited to build the future of AI governance with Atlan. All of the work that 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 and Analytics, Workday

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"Atlan is much more than a catalog of catalogs. It's more of a context operating system...Atlan enabled us to easily activate metadata for everything from discovery in the marketplace to AI governance to data quality to an MCP server delivering context to AI models."

Sridher Arumugham, Chief Data and Analytics Officer, DigiKey

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The framework that works is the one built on governed data

The guides that treat context engineering as a retrieval problem will produce agents that retrieve confidently and answer incorrectly. The teams that reach production — and stay there — are the ones that governed the source layer first. Every step in this framework flows from that decision. Govern the data. Then build the retrieval. Then validate, deliver, and monitor. That sequence is the framework.

If you are ready to see what governed context delivery looks like in a production environment, the context engineering and AI governance guide covers the governance architecture in depth. For the full enterprise implementation pattern, see how to implement an enterprise context layer for AI.

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FAQs about building a context engineering framework

Do I need a data catalog before I can build a context engineering framework?

Yes. Without a catalog or governance layer, you cannot determine which sources to trust, who owns what, or whether business definitions are canonical. You can build retrieval without a catalog; you cannot build trustworthy retrieval. The governance step (Step 2) requires some form of asset inventory — whether a purpose-built catalog, an alternative governance platform, or a manually maintained inventory for smaller data estates.

How is a context engineering framework different from just setting up RAG?

RAG is one component of the retrieval layer (Step 3). A context engineering framework includes governance (Step 2), validation (Step 4), delivery (Step 5), and versioning and monitoring (Step 6) around the retrieval layer. RAG without governance produces confident wrong answers. The framework is the structure that makes RAG reliable in production.

How long does it take to build a context engineering framework?

A functional first version for one domain typically takes 8-12 weeks, with governance setup being the longest step at 2-4 weeks. Plan for ongoing iteration, not a one-time build. Teams that have an existing data catalog with governance capabilities can move through Step 2 faster; teams starting from scratch should budget toward the 12-week end.

What if our data lives in multiple systems?

This is the default enterprise state. The governance step (Step 2) must inventory and classify all candidate sources across systems. MCP then provides standardized access across heterogeneous sources without requiring custom M x N integrations — M+N standardized connectors instead.

How do we know when our context is good enough for production?

The 80% context accuracy threshold is a practical heuristic. Measure it concretely: generate synthetic test questions from your dashboards and reports, compare AI answers against ground truth for your domain’s critical queries, use RAGAs or TruLens for systematic evaluation. Below 80%: return to Steps 3 and 4 before deploying. Above 80%: deploy and use user feedback to drive continued improvement.

What is the biggest mistake teams make when building a context engineering framework?

Starting retrieval before governing the data. The plumbing works; the water is contaminated. Govern your context sources before building retrieval — this is the step that separates frameworks that work in production from those that do not. Every other mistake is recoverable. This one often requires rebuilding.

Can we build a context engineering framework without Atlan?

Yes. The governance step can be done with any data catalog (Alation, Collibra) or manually for smaller data estates. The question is scale and speed. For enterprise data estates with thousands of assets and multiple AI use cases, automated metadata management, MCP delivery, and git-like context versioning reduce governance setup from months to weeks.

What is the role of MCP in a context engineering framework?

MCP (Model Context Protocol) is the standard interface for the delivery layer (Step 5). It replaces M x N custom integrations with M+N standardized connectors, allowing any MCP-compatible AI tool to consume governed context from any MCP-compatible data source. It coexists with RAG in production frameworks: RAG handles knowing (what information is relevant), MCP handles doing (what actions and data the agent can access).

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How to Build a Context Engineering Framework: Step-by-Step Guide

Key takeaways

How to build a context engineering framework

The 6 steps are

Why build a context engineering framework?