🎉 What's new

Your team already knows things about your data that can't be inferred from a column name: that this table refreshes at 06:00 UTC, excludes test orders, or that a null here means "not yet reconciled," not zero.

You may already have that context in Atlan as a Knowledge File.

But until now, the Context Agents that drive enrichment across so much of your data estate couldn't read it. They enriched purely from schema and lineage, with no way to point them at the document that already had the answer.

That changes now, with two new offerings.

First, link a Knowledge File to an asset (GA), and the Description and README context agents read it the next time they enrich that asset, grounding what they write in what your team already documented instead of inferring it. SOPs, best practices, data contracts, data dictionaries, runbooks, migration notes: all of this unstructured and semi-structured information can now shape how Atlan describes your data.

✨ Let's dig deeper

• Link from two places on the asset profile. Use the Knowledge files section in the details panel, or the + in the resources row under the Readme. Either opens the same picker: search, select one or more files, and click Link files.
• Scoped by design. An agent reads only the files linked to the asset it is enriching, never all of them. Linking is how you decide which document applies where.
• Written, not copied. Agents take the facts a document establishes and express them for the asset. You get the same facts in different words, not a trimmed passage.
• Nothing gets overwritten. Enrichment fills gaps. Descriptions and READMEs you already have stay exactly as they are.
• The document has to be a Knowledge File in Atlan. If yours isn't uploaded yet, create a Knowledge Folder and upload it first.

Second, and launching in Private Preview: Agent Templates

Everything above works in one direction: an agent reads a document you linked and writes onto an asset you already have.

Agent Templates run the other way. Instead of enriching what's in Atlan from schema and lineage, an agent reaches the systems where your documentation already lives, reads what's written there, and creates governed context that wasn't in Atlan at all - citing its source every time.

Each template is built for one outcome. You pick the outcome you want, connect the source, say what you're after, preview, then run. The first one is live:

Business Graph Term Generator turns your docs into business graph terms and categories, enriches terms that have no definition, and links them to the assets they describe. Every term carries a citation back to the document it came from.

• You supply four things, and none of them is the prompt. The source to read, the scope inside it, run instructions saying which terms you want, and optionally your own skills for house rules a template can't know. Atlan packages the rest: the skills that make a run reliable, the quality gates it has to pass, and the write path into Atlan.
• Sample before you publish. A sample run reads a small part of your sources and proposes a handful of terms in a minute or two. It publishes nothing. Read it, adjust your instructions, sample again, then run for real.
• Your Knowledge Files are a source here too. The documents you're linking to assets today can also be what a template reads from, with nothing to connect and no credentials to manage.
• Read-only by construction. For GitHub and Confluence, an admin connects the source once in Settings with a read-only credential you provide. That credential is the access boundary: the agent reads exactly what the token can read, can't write back to the source, and never asks an end user to sign in.
• More outcomes are coming. Selecting the bell on a template card records your interest, and that signal decides which one gets built next.

Agent Templates are available in the New UI only, enabled per tenant, and open to workspace admins and governance admins. Runs are free for the duration of the preview. If you don't see a Create agent button in Agents Studio, your tenant isn't switched on yet - talk to your Atlan contact!

New to this? Start with Understand agent templates.

Atlan for SAP: Ground Your Agents in the System of Record

● Generally Available

This release turns SAP's notoriously opaque data into a governed, machine-readable context layer for AI. SAP is the system of record for most large enterprises — and the hardest source to ground agents in, thanks to cryptic tables, coded field names, and configuration-driven logic. SAP ECC and SAP S/4HANA are now GA with two new capabilities that give agents (and the people who supervise them) two things they've never had over SAP: provenance — where a number truly comes from — and a map of the human layer, where SAP data is actually created and consumed.

CDS View Column-Level Lineage

Provenance

What's new: Column-level lineage is now generally available for SAP CDS (Core Data Services) views. Trace every column in a CDS view back through its transformations to the exact source table columns that feed it.

What you can do:

• Follow a single field end to end, from the semantic CDS layer down to the underlying SAP table columns.
• Run precise impact analysis — see exactly what breaks downstream if a source field changes.
• Debug and validate at the column, not just the object, level.

Why it matters for agents: This is the provenance backbone of the context layer. When an AI agent surfaces a metric, it can cite the precise origin and transformation path of every value — making the output auditable and trustworthy, and letting governance and quality signals propagate accurately across the SAP estate.

Fiori Apps as a New Asset Type

The Human Layer · First to market for Atlan

What's new: SAP Fiori apps are now a native asset type in Atlan, with asset-level lineage from Fiori apps → CDS views → upstream SAP tables.

The problem this solves: Fiori is the modern SAP UI — where business users actually work, reading, entering, and changing data every day, with near-zero visibility into the data beneath the screen. This release connects the app the user sees to the data it actually touches.

What you can do:

• See, for any Fiori app, the chain of CDS views and source tables it draws from.
• Give business users and stewards a clear map from the interface to the underlying data.
• Trace where sensitive or business-critical data is exposed and modified at the point of use.

Why it matters for agents: Fiori is the human layer of the context graph — where data is created and consumed. Mapping it lets an AI agent understand what an app does in data terms, and lets governance follow data all the way to the screen.

On the roadmap: column-level lineage for Fiori apps · popularity & usage signals for Fiori apps.

What's Next

Next: SAP Business Data Cloud (BDC). Zero-copy data sharing across Snowflake, SAP Databricks, Google BigQuery, and Microsoft Fabric. Context that follows your data across platforms without ever moving or duplicating it — so lineage, meaning, and governance stay intact wherever SAP data is consumed. Connectors for SAP Datasphere and SAP Analytics cloud, complementing our ERP and BW connectors.

More context ingestion from the SAP ecosystem, coming soon:

• Field-level help text as glossaries — SAP's own field documentation, automatically converted into governed business glossary terms and linked to the exact columns they define. The semantic layer that gives agents authoritative meaning for every SAP field.
• Master data as data products — your unique SAP configuration, packaged into governed data products: material types, customer and vendor account groups, and Business Partner groupings, roles, and categories. Context that reflects how your enterprise actually classifies its master data.
• SAP long text as knowledge files — SAP's free-text long texts (notes, descriptions, and documentation) harvested and published as knowledge files, ready to ground agents — for retrieval in agent studios and RAG workflows.

Beyond that — context from across the SAP application landscape (actively working with customers on outcomes and use cases for the below):

• SAP Signavio — business process context (how work actually flows).
• LeanIX — enterprise architecture and application portfolio context.
• SAP IBP — integrated business planning and supply chain context.
• SAP Concur — travel, expense, and spend context.
• SAP SuccessFactors — people and HR context.

The Bigger Picture: SAP + the Leading Non-SAP Context Layer

Pair the deepest context layer for SAP with the leading context layer for everything outside it — cloud warehouses, lakehouses, BI, transformation, and AI tooling — and the whole enterprise becomes legible to AI in ways neither side can deliver alone. An agent can trace a metric from a BI dashboard, through the cloud warehouse, across a zero-copy SAP share, into the CDS view and its source table — with provenance at every hop.

The estate is opening. The connective tissue is here. The result is a single, trustworthy map of how the business really runs — and the foundation for enterprise AI you can actually rely on.