--- title: "Context Agents — The AI Teammates That Make Your Data AI-Ready" url: "https://atlan.com/context-agents/" description: "Context Agents are the AI teammates that write, maintain, and continuously evolve the documentation your team never did. Make your enterprise data AI-ready in 30 days." keywords: "context studio, enterprise context layer, ai context, data governance, atlan" --- > Atlan is hosting Context Conference, bringing together the leaders and builders at the frontier of giving AI the context it needs to understand their business. It runs online on October 28, 2026, from 11:00 AM to 2:00 PM ET. Atlan co-founder Prukalpa Sankar opens and closes the day. Leaders from AstraZeneca, BNY and Verizon share why they invest in context and what they get from it. Registrants get early access to The AI Context Gap, a new study from MIT Technology Review Insights. Register: https://atlan.com/context-conference/ **Context Agents: the team that makes your data AI-ready.** Documentation has been an unsolved problem for years, and AI needs more documentation than humans can write. Context Agents are the AI teammates that write, maintain, and continuously evolve the documentation your team never did. [Book a Demo](https://atlan.com/forms/talk-to-sales-contact/) ## Meet your team | Agent | Role | Stage | What it does | Superpowers | Works best with | |---|---|---|---|---|---| | Scout | Usage Intelligence | 1 | Ranks assets by what your team actually queries. | Query Analysis, Usage Signals, Asset Ranking | Scribe, to prioritize what gets described first | | Scribe | Description Writer | 1 | Writes descriptions from SQL patterns and lineage. | SQL Parsing, Lineage Signals, Auto-Write | Doc, to turn descriptions into full dataset readmes | | Lexis | Glossary Builder | 1 | Builds your business glossary from column patterns. | Term Extraction, Domain Patterns, Definitions | Nexus, to connect glossary terms to every column | | Doc | Readme Author | 2 | Synthesizes signals into comprehensive dataset readmes. | Signal Synthesis, Full Documentation, Lineage Context | Atlas, to document every domain's key datasets | | Nexus | Terms Linker | 2 | Links every column to the business term analysts use. | Term Mapping, Column Linking, Zero Manual | Sage, to resolve conflicting term definitions | | Sage | Metric Arbiter | 2 | Finds conflicting metric definitions and locks one answer. | Conflict Detection, Metric Resolution, Team Routing | Orion, to lock metrics into the ontology | | Atlas | Domain Classifier | 3 | Tags every asset with its business domain at scale. | Classification, Domain Mapping, Context Routing | Vera, to score every domain's data quality | | Vera | Quality Scorer | 3 | Scores every critical asset on completeness and freshness. | Completeness, Accuracy Check, Freshness Scoring | Orion, to ensure only trusted data reaches AI | | Orion | Ontologist | 3 | Maps the ontology connecting every domain and metric. | Relationship Mapping, Term Resolution, Semantic Graph | Scout, to close the loop: usage feeds back into context | ## 87% of customers say Context Agents write higher quality content than humans - "We were stunned and perplexed by the quality of the content. How could the agents create such high-quality context from lineage, SQL, and dbt logic? It shows how much business information is hidden in metadata that we can't see with human eyes. But agents consume it all and organize it." — Kenneth Jebjerg, Head of Data Engineering, Baader - "The output shifted from solid generic descriptions to something that felt like it had been written by someone who understands our business. That's the moment I stopped thinking about this as a time-saving tool and started seeing it as a strategic capability." — Izabela Wilczyńska, Data Governance Officer, PayU - "Without this context we would have spent months/years manually updating the metadata required for these efforts and would not let us move at the speed we can now." — Bernie Daley, Director of Data Management, Nelnet Servicing, LLC ## The journey: data catalogs were built for humans... who never documented them - **The first copilot (2023).** Atlan launched the first AI documentation agent, Atlan AI. It could write descriptions automatically, but accuracy was at 75%: good enough to show the vision, not good enough to replace human work. - **We hit a wall.** To be accurate, AI needed rich signals like lineage, query history, usage patterns, and relationships between assets. Atlan stored all of that, but AI couldn't use it. So Atlan rebuilt the foundation: the [Context Lakehouse](https://atlan.com/context-lakehouse/). - **The new reality.** Context agents now outperform humans on quality. Customers say agent-written descriptions are more accurate and more complete than what their teams produced manually. Stats: acceptance rate today **90%+**; AI descriptions applied **350K+**. Start your AI-readiness sprint: Context Agents can get you to AI readiness in 30 days. ## How it works: the teammates that solve the biggest blocker to context, documentation Context Agents work together to continuously compound context. Each agent's work feeds the next: descriptions inform classifications, classifications unlock quality scoring, quality scoring makes everything downstream more trustworthy. ### Stage 1: Foundational — agents that read raw metadata to build foundational context - **Scout — Usage & Query Intelligence.** Finds the most important assets needing context. Analyzes query history and access patterns so enrichment targets the assets people actually use. Task plan: scan SQL query history across all teams and use cases; identify top-queried assets by team, frequency, and function; rank by usage score and assign enrichment priority. - **Scribe — Description Agent.** Writes descriptions that hold up under scrutiny. Reads SQL usage patterns, column names, and lineage signals to generate accurate descriptions for every table and column. - **Lexis — Glossary Bootstrapping.** Your business glossary has been "coming soon" for years. Lexis builds it from existing definitions, column naming conventions, and domain patterns. ### Stage 2: Derived — agents that synthesize foundational context into structured business knowledge - **Doc — Readme Agent.** Turns scattered signals into documentation your team will use. Takes descriptions, usage signals, and lineage and turns them into comprehensive dataset documentation (overview, source tables, key columns, usage), synthesized from Stage 1 signals. - **Nexus — Terms & Metrics Linkage.** Closes the gap between code and conversation. Bridges technical column names and the business terms your analysts actually use. - **Sage — Metric Conflicts.** Finds where two teams define the same metric differently, and locks in one answer. Surfaces conflicts, routes to team stewards, and updates definitions once approved. ### Stage 3: Compounded — agents that build advanced, enterprise-grade intelligence - **Atlas — Domain Tagging.** Maps every asset to its place in the world. Classifies every asset into the right business domain so AI agents know how to find the right context based on the user. Task plan: read Scribe descriptions and usage signals; match asset metadata against domain patterns; score domain fit for each asset; apply domain tag or route to steward. - **Vera — Data Quality.** Surfaces which assets your AI can and can't trust. Automatically scores your critical assets on completeness, accuracy, and freshness. - **Orion — Ontologist.** Maps what every term means in every context. Maps every relationship between domains, terms, and assets, so when an agent asks what "revenue" means, it gets the right answer for the right context. On demand: [Inside Mastercard's Context Agents Rollout](https://atlan.com/context-in-practice-mastercard-recording/) — how Mastercard let AI document 30,000 assets in two weeks, with stewards certifying every description. ## Rollout in 30 days, not 12 months - **Start with what matters.** Most of your catalog nobody touches. Context Agents identify your Gold Layer, Popular BI, Popular SQL, and upstream dependencies first, enriching the assets people actually use before spending cycles on the long tail. Value shows up in days, not months. - **AI scores every output.** Each agent output carries a composite confidence score across accuracy, clarity, style, and completeness. High-confidence outputs auto-apply. Lower-confidence outputs route to humans. - **Humans decide and govern.** AI generates descriptions, classifies assets, builds metrics, and scores quality at scale. Stewards shift from documentation to certification: sampling, validating, and resolving the cases that require judgment. One click. Not 847 manual reviews. ## Industry recognition: validated by Forrester and Gartner - **Leader in the 2025 Gartner® Magic Quadrant™ for Metadata Management Solutions:** "Atlan's solution focuses on automation, allowing every action to be performed programmatically via APIs and calling via an LLM. Its core components also include a knowledge graph for business domains and vector storage & analytics, which is purpose-built for AI." [Report](https://atlan.com/gartner-magic-quadrant-metadata-management-solutions-2025/) - **Leader in the 2026 Gartner® Magic Quadrant™ for Data & Analytics Governance:** "Atlan stands out in AI-native governance through context-based partnerships, agentic stewardship and orchestration of enterprise agentic systems. The underlying metadata lakehouse architecture boosts performance, scalability, extensibility and time travel auditability." [Report](https://atlan.com/gartner-magic-quadrant-data-governance-2026/) - **Leader in The Forrester Wave™: Data Governance Solutions, Q3 2025:** "Atlan offers features that are among the best in class for policy management, stewardship, and collaborative governance. Its knowledge graph and AI-powered automation support clear data ownership, surfacing policy-relevant context and automating governance workflows." [Report](https://atlan.com/know/forrester-wave-data-governance-2025/) ## Customer love: users can't stop gushing about their new AI teammates Every enterprise runs on context that was never written down. Business logic embedded in how systems are actually used, tacit knowledge in the heads of people who've been around long enough, and the trail of decisions that explains why things work the way they do. Context Agents surface that context from your business systems. Here's what teams found when they ran it in production. ### Move faster: months of work, done by lunch Business systems multiply. Tacit knowledge compounds in people's heads. And AI needs all of it, accurate, current, and at a scale no team can reach by writing descriptions one at a time. The gap isn't a resourcing problem. It's a structural one. These teams found a different model. - Bernie Daley, Nelnet Servicing (quoted above). - "With single-digit hours of effort from our team, we were able to accomplish work that would have taken months for a larger team to finish — and realistically, we likely would not have ever started it." — Data Governance Leader, Consumer Packaged Goods (CPG) - "If we tried to map and document this data estate manually, it would have taken years for an entire team. I joked internally that I'd be ready to retire before we finished. Automation turned a multi-year hurdle into an immediate win." — Data Governance Leader, Financial Services - "Our pilot proved that automated context drafting makes our entire documentation process infinitely more efficient. The results show that it is significantly faster for our producers to review and fix an existing draft than to write one from scratch." — Data Engineering Leader, Retail & E-commerce - "This serves as an excellent accelerator to get critical governance conversations going across the company. It instantly provides rich content for our teams to discuss and refine, rather than leaving them staring at empty pages." — Data Architect, Energy & Utilities - "This completely jump-started the population of our descriptions, allowing us to permanently overcome the 'cold start' issue that stalls out most traditional enterprise governance initiatives." — Data Engineering Leader, Technology & SaaS - "The ability to quickly prefill initial descriptions and READMEs is extremely beneficial because taking that first manual step is always the most time-consuming. Once the baseline is established, you can focus human energy strictly on refining your highest-value assets." — Data Engineering Leader, Life Sciences & Healthcare ### Business accurate: context that captured the business DNA Business systems have their own language: industry-specific terms, audit columns that only insiders understand, business logic embedded in query patterns that no wiki captures. The question was whether automated context could learn that language without being taught, or default to something generic enough to be useless. Context Agents learned it, precise enough to mean something to the people who know the data best. - "I expected boilerplate — instead, it inferred business context I never explicitly provided, correctly describing how an asset fit into our customer journey just from column names and lineage. That's when I realized this was knowledge synthesis, not just documentation." — Swatilekha Saha, Data Architect, DAT Freight & Analytics - Izabela Wilczyńska, PayU (quoted above). - "It picked up on the nuance of our underlying database structures, successfully defining audit columns and system attributes that human stewards usually ignore out of fatigue. It proved we can move past line-by-line micromanagement." — Data Governance Leader, Insurance - "We have highly custom, specialized domain terminology that standard dictionaries completely miss. The system parsed our query behavior and accurately translated that unique jargon right out of the box, without heavy manual training." — Data Governance Leader, Travel & Hospitality - "Operating in heavy manufacturing means our data carries highly specific operational context. The agents interpreted and documented these industry-specific assets flawlessly, showing a deep understanding of our business logic without manual fine-tuning." — Data & Analytics Leader, Industrial Manufacturing - "The specialized documentation and structural context that the agents generated were surprisingly accurate. They captured our data architecture so cleanly that, with only a few minor corrections, they were completely production-ready." — Data Engineering Leader, Retail & E-commerce - "The depth of the context it pulled automatically was incredible. It successfully mapped out highly complex, specialized tables that are notoriously difficult to track manually, giving us a level of baseline precision we couldn't maintain on our own." — Data Governance Leader, Life Sciences & Healthcare ### Untapped signal: surfaced business logic that was hidden in queries The most accurate description of what a data asset does isn't written anywhere. It's in the queries people run against it, the lineage that connects it, the SQL that defines how it moves. These teams found a way to read that signal. - "We were stunned with the quality of the content that was produced by these AI agents. We were perplexed. How could it create such high-quality context from lineage, from SQL logic, from dbt logic. It really shows how much business information is hidden in the metadata following a lineage that we simply cannot see with human eyes. But agents pick it up, consume it all, and spit it out in an organized way." — Kenneth Jebjerg, Head of Data Engineering - "The AI enables us to do things we wouldn't be able to do otherwise. I don't know if we would ever get to this level of quality with descriptions. Getting a consistent definition when different lines of business describe things differently? Everyone ends up learning more about the data than they knew." — Cody Brees, Data Governance Lead, The Bancorp - "This provided an exceptional opportunity to generate deep technical insights via SQL Intelligence that would be incredibly difficult and labor-intensive to gather manually across our core data pipelines." — Data & Analytics Leader, Retail & E-commerce - "We put the automated descriptions in front of our most rigorous enterprise data gatekeepers, and it cleared an 80% accuracy baseline immediately. It proved that automated context can meet the strict standards of a global corporate data office." — Data Governance Leader, Technology & SaaS - "Instead of just filling in blank text boxes, the system decodes the inner logic of our data through actual usage footprints. It turns documentation into a live, continuous feedback loop that evaluates our overall platform health." — Data Architect, Life Sciences & Healthcare - "By reverse-engineering raw query footprints, the system automatically captured the exact business questions our users naturally ask. It understands human intent far better than a static data dictionary ever could." — Data Governance Leader, Non-Profit & Education - "The natural language questions surfaced by the SQL Intelligence feature were a major milestone for us. Seeing the machine reverse-translate complex SQL queries back into clear, user-friendly business logic was highly impressive." — Data & Analytics Leader, Technology & SaaS ### Finally unblocked: no more chasing busy analysts and engineers Context has never lived in one place. Sales knows who a customer is. Finance knows what revenue means. Legal knows the process through which a customer gets signed. Getting to a complete picture has always meant tracking down the right people, in the right moment, willing to stop what they're doing to share what only they know. That was survivable when the only cost was friction and delay. It isn't when AI agents query at machine speed, confident in whatever they find and indifferent to what they don't. - "Once we showed this to our BI team, you could see the attitude shift — from 'another tool, more work' to excited to get in and start looking around. The instant value just changed everything." — Sayali Avalakki, BI & Analytics Lead, Brightspeed - "This system gives us an incredibly solid foundation to build our new data governance culture. By automatically generating initial context, it completely eliminates the 'blank page effect' that stops users from adopting tools." — Data Governance Leader, Logistics & Operations - "This pilot gave our team clear reassurance that a massive portion of the administrative heavy lifting that used to derail data governance can now be completely and safely automated." — Data Engineering Leader, Technology & SaaS - "We completely bypassed the uphill battle of chasing down busy business analysts for definitions. We generated a highly accurate, reviewable baseline of our data estate in an hour — a process that normally takes weeks of corporate coordination." — Data Governance Leader, Technology & SaaS - "This successfully moves the organizational bottleneck from motivating busy stewards to document, to building efficient human-in-the-loop review processes. It saves time on manual creation and focuses energy on strategic verification." — Data Governance Leader, Industrial Manufacturing - "At the start of any data catalog journey, getting definitions for physical data is a massive hurdle. These agents give us a great starting point to engage business users, shifting their role from writing definitions to simply reviewing them." — Data Governance Leader, Insurance ## Learn more about context agents and the context layer - [Why your AI agents hallucinate on real data](https://atlan.com/know/enterprise-context-layer/) — 53+ resources on the enterprise context layer, the missing infrastructure between your data platform and your AI agents: what it is, how to implement it, and why teams that have it are 5x more likely to reach production. - [84% invest in AI. 17% reach production. Here's the gap.](https://atlan.com/resources/ai-broke-the-data-stack-predictions-for-2026/) — 550+ data leaders on the 7 structural shifts forcing data teams to rebuild for an AI-first world. - [Traditional data cataloging is dead](https://atlan.com/context-in-practice-agentic-data-catalog-recording/) — Mastercard, Sophos and ASOS on letting AI document their data: 30,000 assets enriched in two weeks, certified by their own stewards, with Prukalpa Sankar on the rise of the agentic data catalog. Leave metadata management behind. Compound context with agents: [Get started](https://atlan.com/forms/talk-to-sales-contact/)