Because the Context graph debate is on, here's a prediction on what next - By February, pitch decks & conference slideshows will feature "decision traces," "knowledge graphs," and naturally "neuro-symbolic AI." Someone will declare 2026 as the year of neuro-symbolic. The problem?
The terminology is already confusing, and broken. How do I know? Last year a VC analyst compared us with Neo4j.
"Neuro-symbolic" once meant something precise: principled integration of neural pattern recognition and symbolic reasoning. Today it can also mean "we use an LLM and also have some rules."
Marketing clouts and makes architecture decisions difficult and that’s why we published a simple framework that focuses on what the problem needs and not what the architecutre offers. Even within an enteprise workflow, not every workflow requires 100% accuracy, and consistency.
There are workflows where a simple LLM call suffices. You don't need a knowledge graph.
There are workflows where Graph RAG looks sophisticated but can't answer the auditor's question: why did the system say that?
There are workflows where not using an ontology to guide retrieval means you're setting yourself up for compliance failure.
That’s why a simple framework that starts with your core workflow. Ask these six questions before you get to vendors, or even a build vs buy discussion
1. How important is consistency to this workflow? Does the same question need to yield the same answer tomorrow? A slightly different email summary each time? Fine - that's a feature. A prior authorization decision that flips between "approved" and "denied" on the same inputs? That's a compliance failure. Know which one you're building for.
2. Can you show why the system said what it said? Not what it retrieved - why that led to this conclusion. "Based on Policy Doc v3.2, page 14" isn't traceability. "Query matched 'refund request' → Policy 4.2.1 applies → 30-day window exceeded by 3 days → Denial"—that's traceability. When the auditor asks, which answer do you have?
3. Where does domain expertise actually live? In the model weights? You can't inspect it, version it, or update it when regulations change. In retrieved documents? You see what it pulls, but not the rules for how it's applied. In formalized ontologies? Now you can audit it, update it, explain it. Different architectures, different ceilings.
4. What happens when the query is messy or underspecified? Real users don't speak in perfect queries. Does your system ask clarifying questions? Make reasonable inferences? or simply refuses to answer.
5. What does it take to get this working for your domain? Weekend hackathon to upload docs and ship? Or 3 months with clinical SMEs formally modeling treatment protocols and contraindication rules? Both are valid - for different problems. The architecture that fits your problem might not fit your timeline. Know the tradeoff upfront.
6. When knowledge updates (and it will), how painful is the fix? New policy doc gets uploaded and flows through automatically? Or changing a regulatory definition triggers expert review, downstream impact analysis, and regression testing across every affected query? A system that's painful and costly to update becomes a system that's out of date.
Different architectures optimize for different dimensions. No architecture maxes all six. The right question isn't "which is best?" It's "which shape fits my problem?"
Read the framework and let me know which one have you considered and what have you discarded. Again - no right and no wrong answers.
Five architectures. Six dimensions.
Best,
Vivek K