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CSA Loom — the Microsoft Fabric experience for Azure tenants where Fabric isn't yet available: lakehouses, warehouses, notebooks, semantic models, Activator rules, Data Agents, across Commercial, GCC, GCC-High, and DoD IL5

Tutorial: Spindle (AIP Logic & agents) editor

CSA Loom aip-logic editor — Spindle Studio, the Azure-native equivalent of Palantir AIP Logic and AIP agents: author typed AI logic and tool-calling agents over a Weave ontology, grounded on real data and run on Azure OpenAI / Azure AI Foundry. No Microsoft Fabric required.

What it is

AIP Logic builds no-code, typed LLM functions; AIP runs agents over the ontology. Spindle Studio covers both. You define a typed input schema and an ordered set of steps with dropdowns (no freeform JSON), bind a Weave ontology so the function grounds on its entity types and Lakehouse / Warehouse bindings, then run it two ways:

  • Logic mode — a single grounded turn that writes real read-only T-SQL / Spark-SQL against Synapse and cites real rows, or
  • Agent mode — a multi-step, tool-calling agent on the production copilot orchestrator with the full Loom data-tool registry.

You can also publish the logic as a real Azure AI Foundry Agent Service agent and inspect its per-step run trace.

When to use it

  • You want a reusable, typed AI function (typed input → steps → typed output) grounded on governed data instead of an ad-hoc prompt.
  • You want an agent that can call Loom data tools to answer multi-step questions over your ontology.
  • You want to deploy the logic as a managed Foundry agent and audit its steps.

Step-by-step in Loom

  1. Create the item. Choose + New item → Spindle (AIP Logic & agents) (Fabric IQ). The editor opens at /items/aip-logic/<id>.
  2. Define typed inputs. Add named input parameters with types (string / number / boolean) in the field builder.
  3. Ground on the Weave. Bind a Weave ontology so Spindle runs against its entity types and Lakehouse / Warehouse bindings (real Synapse queries).
  4. Add ordered steps. Use Add step to add LLM-prompt, extract, or branch steps from a dropdown — no freeform JSON.
  5. Define the output. Set the typed output shape the function returns.
  6. Invoke. Flip the Logic / Agent switch, then run: Invoke function (single grounded turn) or Run agent (multi-step tool-calling). Both hit the live Azure OpenAI deployment; the agent returns a per-step run trace, or an honest remediation gate if no model is deployed.
  7. Publish as a Foundry agent. Use Publish to deploy the logic to Azure AI Foundry Agent Service, then run and inspect its steps — or use the Azure-native Invoke path where Agent Service is unsupported (for example Azure Government).

The Azure backend it rides on

  • LLM: an Azure OpenAI deployment (LOOM_AOAI_*); an unset deployment produces an honest gate naming the env var.
  • Grounding data: read-only Synapse SQL / Spark-SQL against the ontology's Lakehouse / Warehouse.
  • Managed agents (optional): Azure AI Foundry Agent Service.

No Fabric required

Spindle grounds on a Loom Ontology and runs on Azure OpenAI / Foundry by default. Fabric Reflex / Fabric AI are never on the default path; honest gates name the exact AOAI or Foundry env var when a backend is unconfigured.

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