Why AIP Logic Exists
LLMs are flexible, but by nature non-deterministic — nothing guarantees the same question gets the same answer every time. AIP Logic is an authoring tool for visually designing that LLM reasoning ability alongside deterministic logic blocks — conditionals (if/else), loops, API calls — so the unpredictable part stays contained inside controllable business rules.
The key is direct integration with the Ontology. Instead of a vague text prompt, the LLM receives actual Ontology objects (customers, equipment, contracts, and so on) and their relationships as context, so it can reason from real-time business data and reduce hallucination.
Core Features
- Ontology-based data binding: bind Ontology objects and relationships directly into a prompt, so the LLM references real data context.
- Multi-model orchestration: chain different LLMs together within a single workflow depending on task complexity — this also helps reduce dependence on any one model.
- Combining with deterministic logic: assemble classic programming blocks — if/else, loops, API calls — together with LLM reasoning blocks via drag and drop.
- Visual debugging and backtesting: trace each step’s input prompt, intermediate output, and final result through a tracing interface, and validate the logic against historical data before it ever goes live.
- Governance and guardrails: sensitive-information filtering, data access control by user permission (ACL), and lineage tracking of the AI’s decision process come built in.
How Teams Use It in Practice
- Sketch the deterministic skeleton first: nail down which condition leads to which branch first, then carve out just the part the LLM should own — natural-language understanding, summarization, judgment support.
- Bind ontology context minimally: connect only the objects and properties actually needed to the prompt. Passing in unrelated data just bloats the context and blurs the reasoning.
- Validate with backtesting before deployment: run the logic against historical data first and filter out cases where the result diverges from expectations before shipping.
Common Mistakes
- Handing the LLM work that deterministic logic could handle: a simple conditional branch is faster and more predictable as an if/else. It’s more stable to reserve the LLM for points where natural-language understanding or judgment is genuinely required.
- Deploying without backtesting: a workflow with LLM blocks has a wider range of edge cases than ordinary code. Skipping validation against historical data before deployment can surface unexpected responses in production.
- Leaving guardrails for later: sensitive-data filtering and permission control are less likely to get missed if they’re defined from the moment you design the workflow, rather than bolted on after the logic is already finished.
Next Steps
If you need a workflow that handles document-based data, look at Document Intelligence next; to extend into a conversational interface, look at Chatbot Studio.