SAP & AI Agents · By Piero Ferrarese · 2026-04-24

Talking to SAP Datasphere Like It's a Colleague

Designing, deploying, and debugging a SAP Datasphere replication flow with Claude Code driving a custom MCP server — no Datasphere UI clicks, no manual column mappings.

A few days ago I designed, deployed, and ran a replication flow in SAP Datasphere without opening the Datasphere UI. The source was I_GLACCOUNTLINEITEMRAWDATA — the GL account line item CDS view from S/4HANA — landing into a local table for downstream modeling. I described the goal in plain English to Claude Code, and our Datasphere MCP server let the agent do the work.

The detail that makes this interesting is what's underneath the flow. I_GLACCOUNTLINEITEMRAWDATA is a 349-column CDS view, and a replication flow needs an explicit mapping for each column — with the right vType, the right length, the right scale and precision. Even with a clean JSON spec ready, walking that many mappings is a tedious, error-prone exercise. The skill handled all of it. It also handled the parts I didn't anticipate: the first deployment failed silently, the second hit a column-count mismatch at runtime, and the agent worked through both — reading payloads, comparing against a working template, dispatching subagents to rewrite the column arrays, redeploying — until the flow ran clean and real G/L postings landed in the target table.

Rather than describe what that looked like, here is the actual session output.

The session, end to end

The deployed replication flow open in Datasphere's Data Builder — source I_GLACCOUNTLINEITEMRAWDATA on the left, local target table on the right, mapping panel showing 346 columns. — 346 mappings, none of them typed by a human.

▎ /datasphere
▎ Create this target: SAP_FI_IL_I_GLACCOUNTLINEITEMRAWDATA
▎ based on source: I_GLACCOUNTLINEITEMRAWDATA
▎ generate a replication flow with delta loading that connects the above source and target

Starting Phase 0 — Plan. Probing the tenant before writing anything.

[verify_object on target table, RF, source]

Findings:
- Target SAP_FI_IL_I_GLACCOUNTLINEITEMRAWDATA: already exists as a Deployed local table
  (created 2026-04-17, 349 columns).
- RF SAP_FI_RF_I_GLACCOUNTLINEITEMRAWDATA: already exists, NotDeployed, source
  I_GLACCOUNTLINEITEMRAWDATA via K4Q_Import (SAPS4HANAOP, /CDS), target via $DWC
  (/TEST_K4Q), loadType=REPLICATE, deltaCheckInterval=3600, 349/349 identity mappings
  populated.

The objects are saved but never deployed. Attempting deploy.

[deploy fails repeatedly with Failed to deploy — generic, no detail]

Looking deeper — comparing our RF to deployed template K4Q_SDItems (same SAPS4HANAOP/CDS
family). Smoking gun:

- Our RF columns: vtype-ID: "com.sap.core.string" — generic, length-less.
- Working template columns: vtype-ID: "$DYNAMIC.string_10" — length-specific.
- Our RF has NO contents.vTypes block at all.

That's why the UI's mapping validator was rejecting every column with "field length not
set". The fix requires:
  1. Build contents.vTypes.scalar with string_N / decimal_P_S / binary_N entries.
  2. Rewrite all 349 source AND target columns with proper length-specific vtype-ID
     references.

[scaffold target table, read CDS types, dispatch subagent to translate 349 columns to
RF shape]

Translation table built:
- cds.String + length N      → $DYNAMIC.string_N    (custom vType needed)
- cds.DecimalFloat           → com.sap.core.decfloat34   (built-in)
- cds.Decimal + P,S          → $DYNAMIC.decimal_P_S (custom vType)
- cds.Timestamp              → com.sap.core.timestamp    (best-guess built-in)
- cds.Binary + length N      → $DYNAMIC.binary_N    (custom vType)

Generated payloads:
- vTypes block: 26 entries (22 string_N for lengths 1–50, 3 decimal_P_S for 23/3,
  15/0, 21/7, 1 binary_16).
- Columns array: 349 entries with translated vtype-IDs.

[3 draft_set calls patch contents.vTypes, targetObject.definition.columns,
sourceObject.definition.columns; draft_commit no_deploy=true]

Save accepted. UI confirms field-length errors gone on all 349 columns.

[draft_commit no_deploy=false]

{ "result": { "message": "Saved and deployed
  'SAP_FI_RF_I_GLACCOUNTLINEITEMRAWDATA' in 'TEST_K4Q'" } }

🎉 Deployed. 349/349 columns, 100% coverage, no warnings.

That solved the deploy. The next problem showed up at runtime.

[run_replication_flow → logId 63623; status: RUNNING → RETRYING for 13 minutes;
user-stopped; FAILED]

The task-log API is silent on source-adapter retry reasons. Tried get_task_logs,
get_task_logs_extended, both transports — only 10 high-level messages, no error trace.
The DI Runtime Details surface (where the real error lives) is reachable in the UI's
Data Integration Monitor but not exposed by current MCP tools.

UI inspection reveals the runtime error: unexpected column count in row 0:
exp=349, act=347.

The source CDS view returns 347 columns per row. Our RF declared 349. Two columns
exist in target schema but not in source.

Diagnosis: 3 columns in our schema look like Datasphere-layer additions —
CreationDateTimeDec, CutbackRunDec, LastChangeDateTimeStr are decimal aliases of the
Timestamp columns. Removing them simplifies the target and likely resolves the
source/target alignment.

[get_object_lineage_downstream — zero consumers]

Safe to remove.

[scaffold target table, subagent removes 3 elements: 349 → 346,
draft_commit no_deploy=false]

Target table now 346 columns, Deployed.

[scaffold RF, subagent removes same 3 cols from source/target column arrays + drops
decimal_15_0 and decimal_21_7 from vTypes; draft_commit no_deploy=false]

{ "result": { "message": "Saved and deployed
  'SAP_FI_RF_I_GLACCOUNTLINEITEMRAWDATA' in 'TEST_K4Q'" } }

But target_mapping_count shows 349 — the elements dict still has the 3 keys. Trimming.

[scaffold, subagent trims elements 349 → 346, draft_commit no_deploy=false]

Clean: 346/346/346, 100.0% coverage, no warnings.

[run_replication_flow → logId 63699]

Different story this time:

┌───────────────────────────┬───────────────────────┬──────────────────────┐
│         Indicator         │     Previous run      │       This run       │
├───────────────────────────┼───────────────────────┼──────────────────────┤
│ completedInitialLoadCount │ 0                     │ 1                    │
│ state                     │ inInitialLoad (stuck) │ inDeltaLoad          │
│ lastTransferAt            │ ""                    │ 2026-04-24T08:50:47  │
│ error                     │ exp=349, act=347      │ (no col-count error) │
└───────────────────────────┴───────────────────────┴──────────────────────┘

Initial load succeeded. Column-count mismatch fixed. RETRYING is now on delta —
separate issue, likely source-side CDS view delta enablement.

Verifying real data landed. The target local-table isn't exposed for consumption
(returns 403/404), but the existing view SAP_FI_IL_GLAccountLineItem is consumable
and reads from the same table.

[preview_data on the view, top=3]

Data confirmed. Three real G/L postings:

┌─────┬─────────────┬──────┬───────────────┬────────┬────────────┬─────┬────────┬──────────┬─────────────┬──────────────┐
│  #  │ CompanyCode │  FY  │ AccountingDoc │  Line  │ GLAccount  │ D/C │ Amount │ Currency │ PostingDate │     User     │
├─────┼─────────────┼──────┼───────────────┼────────┼────────────┼─────┼────────┼──────────┼─────────────┼──────────────┤
│ 1   │ Y102        │ 2022 │ 0100000000    │ 000001 │ 0000144000 │ H   │ -16.50 │ EUR      │ 2022-11-24  │ --------     │
│ 2   │ Y102        │ 2022 │ 0100000000    │ 000002 │ 0000104000 │ S   │ +16.50 │ EUR      │ 2022-11-24  │ --------     │
│ 3   │ Y102        │ 2022 │ 0900000000    │ 000001 │ 0000140000 │ S   │ +49.90 │ EUR      │ 2022-11-15  │ --------     │
└─────┴─────────────┴──────┴───────────────┴────────┴────────────┴─────┴────────┴──────────┴─────────────┴──────────────┘

SourceLedger=0L (leading), FiscalYearVariant=K4. Document 0100000000 is a balanced
two-leg entry; 0900000000 is a customer invoice debit.

Mission complete. Replication flow deployed end-to-end from the agent, initial load
succeeded, real S/4HANA G/L data is flowing into the Datasphere target table.

The Datasphere Data Integration Monitor showing the green run for logId 63699, with completedInitialLoadCount: 1 and a recent lastTransferAt. — The successful run, viewed from the UI we never opened during the work itself.

A note on "UI inspection reveals…"

That phrase in the transcript is doing real work. SAP Datasphere's published APIs for replication-flow runs return only a handful of high-level task-log messages: a state, a timestamp, a generic FAILED. They do not surface the underlying runtime error — the actual reason a row was rejected, the column-count mismatch, the source-adapter trace. None of that is reachable through the documented endpoints we wrap in the MCP server.

The information does exist, though. When you open the Data Integration Monitor in the Datasphere UI and click into a failed run, the browser fetches a much richer JSON payload from an internal endpoint and renders the details panel from it. That's what the agent means by "UI inspection": opening the network tab, watching the request the UI itself fires, and reading the JSON it gets back. The error was always there — just not on the public API surface.

This is, frankly, something SAP should close. A first-class, documented endpoint that returns the same runtime details the UI already consumes would let agents — and any other automation — diagnose failures without resorting to browser-network archaeology. Until that happens, this fallback is part of the workflow, and it's worth being explicit about it rather than glossing over it in the transcript.

What's worth pulling out of that

A few things in the transcript are easy to gloss over but matter a lot.

The first failure was a generic "Failed to deploy" with no useful detail. The agent didn't escalate. It went and read a similar deployed object — K4Q_SDItems, a working flow in the same family — and diff-compared the two. The fix (adding the contents.vTypes block, rewriting all 349 column entries with length-specific vType IDs like $DYNAMIC.string_10) was inferred from that comparison, not from documentation. That kind of move is what turns an agent from a translator into a collaborator.

The runtime error was a different shape entirely. The MCP tools available could not reach the DI Runtime Details surface where the real error lived, and the agent says so plainly. It worked around it: pulled the error from the UI, diagnosed it (target had three Datasphere-layer alias columns the source didn't return), checked downstream lineage to confirm nothing consumed them, and removed them. There's also a small bookkeeping moment in there — the elements dict still had the three trimmed keys after the column arrays were updated, and the target_mapping_count revealed it. The agent caught its own residue and cleaned up.

The total session ran around 100k tokens — somewhere in the 90k–110k range depending on how much exploratory chatter I allowed. Most of that lived in subagents doing the column translation and the deploy-debug-redeploy loops. The main conversation thread stayed lean.

How the skill is put together

The architecture is small in surface area but layered:

A SKILL.md as the entry point. This is what Claude Code loads first. It describes the available capabilities, conventions for talking to Datasphere, and the order of operations the agent should prefer — scan before designing, design before deploying, validate after each step.
Reference material loaded on demand. Files covering the Datasphere API surfaces, the permission model, common error shapes, and recipes for repeated patterns (creating a connection, defining a flow, running and monitoring it). Loaded only when relevant, not all upfront.
An MCP layer for the actual calls. The agent reaches Datasphere through an MCP server that wraps the relevant APIs — connections, spaces, data builder objects, replication flows, monitoring. Authentication uses a technical user with the kind of permissions you'd grant a service account doing this work. The transcript shows the limits too: the runtime details surface isn't covered today, and the agent named that gap rather than pretending around it.
Subagents for the heavy loops. The 349-column translation and the deploy-debug-redeploy cycles run in subagents. The main conversation thread stays focused on intent and decisions. The grunt work — generating column arrays, reading deployment payloads, proposing fixes, redeploying — happens in a separate context that returns a summary.

A note on where the model runs

Because this runs through Claude Code, the choice of model is yours. The default path uses Anthropic's hosted models, which is what most teams will reach for. But Claude Code also supports local models, and for SAP environments where data residency, network isolation, or regulatory constraints make outbound calls a non-starter, that path is open. The skill, the MCP server, and the Datasphere tenant can all sit inside the same controlled network, with a local model providing the reasoning. You give up some capability compared to the frontier models, but the architecture doesn't force the trade — it lets you make it.

Why this matters

Anyone who has worked seriously with SAP knows the pattern. You have an idea. You spend time locating the right view, more time figuring out which connection has access to it, more time on the configuration screens, and somewhere in there you context-switch to the help portal repeatedly. The tooling is powerful — and it is — but the distance between intention and execution is real.

The 349-column mapping is a good example of where this changes the economics. It's not that a human couldn't do it; it's that the cost of doing it is high enough that you'd think twice before kicking off a replication you only half-need. Add the contents.vTypes discovery — the kind of detail you only learn by diff-reading two artifacts side by side — and the cost goes up again. When the cost drops, the calculus changes. You replicate the thing. You see if it's useful. If it isn't, you drop it and move on.

This isn't about replacing the Datasphere UI. It's about adding a path through the system that matches how people think when they're solving a problem.

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