Neuro-Symbolic AI:
A Practitioner's Taxonomy
In the last two years, we've been compared to graph databases. To vector RAG systems. To Python scripts doing NLP.
Each comparison taught us something: there's a terminology gap in this space so wide that fundamentally different architectures get lumped together. When a Neo4j instance and an ontology-driven reasoning system both get called “knowledge graphs,” buyers can't evaluate the difference. Neither can builders.
That's why we wrote this.
Not to claim our approach is the only valid one—but to share what we learned while figuring out where we actually fit. Building reliable AI systems isn't a spectrum with “more neural” on one end and “more symbolic” on the other. It's a multi-dimensional set of tradeoffs, and the right choice depends on the problem you're solving.
This article is our attempt to map that landscape. A framework to help those building agents understand the choices they've made, the tradeoffs they've accepted, and the paths still open to them.
The bet didn't pay off.
Issue DetectedThe Collective Bet
Over the past two years, the AI industry made a collective bet: that implementation rigor could overcome architectural limitations. Better chunking. Smarter embeddings. Elaborate prompt engineering.
Not because the engineering was poor—some of the best technical minds worked on this. It didn't pay off because they were solving the wrong problem.
The Reliability Ceiling
For many applications (chatbots, search assistants), 85% is fine. But for healthcare, financial services, and pharma, it's not. When a compliance officer asks "why did the system say that?", the answer cannot be "the embedding space placed those concepts near each other."
"The problem is that our vocabulary doesn't expose this difference."
The vocabulary is broken. Not imprecise. Not evolving. Broken.
Every vendor claims the same words. No two mean the same thing. When "Knowledge Graph" can mean a Neo4j database or a formal reasoning system, buyers can't evaluate the difference.
Thin application layer over API calls
Dismissive slur for anything not training custom models
Formal representation of entities, relationships, and semantics
Any database with connections between things
Retrieval using graph traversal for contextual grounding
Marketing label for 'we added a graph somewhere'
Principled integration of neural pattern recognition and symbolic reasoning
"We use an LLM and also have some rules"
Autonomous multi-step reasoning and tool use
Any LLM that calls an API
What Clarity Requires
Escaping the terminology trap requires a framework that exposes the actual tradeoffs. Not a spectrum. The reality is multi-dimensional.
The question isn't "which is best." It's "which shape fits your problem?"
Answer Consistency
"The question: If I ask the same thing tomorrow, do I get the same answer?"
You can't debug what you can't reproduce. In regulated environments, inconsistency isn't a UX problem—it's a compliance failure.
High consistency often means constraining flexibility. The same determinism that makes outputs reproducible can make the system brittle to novel inputs.
Decision Traceability
"The question: Can you show why the system said what it said?"
When a compliance officer asks why the system recommended X, "the embedding space placed those concepts close together" is not an acceptable answer.
Full traceability requires explicit reasoning structures—more upfront investment, less flexibility in responses.
Knowledge Explicitness
"The question: Where does domain expertise actually reside?"
If you can't inspect what the system "knows," you can't verify it's correct, update it when regulations change, or explain it to auditors.
Explicit knowledge requires someone to make it explicit. The more formalized, the more investment to create and maintain.
Handling Ambiguity
"The question: What happens when the query is messy, novel, or underspecified?"
Real users don't speak in perfect queries. A system that only works with well-formed inputs will fail in deployment.
High ambiguity handling often requires the system to infer intent—which conflicts with consistency and traceability.
Setup Investment
"The question: What does it take to get this working for my domain?"
Time-to-value matters. Not every organization has six months and a knowledge engineering team.
Low setup investment often means lower reliability guarantees. You ship fast, but inherit whatever inconsistencies exist in your sources.
Change Tolerance
"The question: When domain knowledge updates, how painful is the fix?"
Regulations update. Products evolve. Policies change. A system that's painful to update becomes a system that's out of date.
High setup investment often correlates with high change cost. The same formalization that enables reliability creates maintenance overhead.
What Is an Ontology?
A knowledge graph tells you what is connected. An ontology tells you what those connections mean and what you can conclude from them. When evaluating a system, ask: What role does the ontology play?
Role 1: No Ontology
The LLM extracts entities and relationships based on what it finds in the text. No formal definitions guide extraction. No constraints govern what relationships are valid.
Consistency and traceability are bounded by LLM behavior. The graph may contain contradictions the system can't detect.
Role 2: Ontology as Schema
The ontology guides extraction. It defines what entity types to look for and what relationship types are valid. The LLM extracts, but within defined boundaries.
Retrieval may be deterministic, but interpretation is still LLM-driven. System relies on LLM to decide what facts mean for a specific patient query.
Role 3: Ontology as Governor
The ontology governs not just extraction, but retrieval and inference. When a query arrives, the ontology determines what's relevant, what constraints apply, and what conclusions are valid.
Flexibility. The system can only reason about what the ontology formalizes. Ambiguous queries may require clarification rather than interpretation.
Generative vs. Executed Reasoning
The term "reasoning" is used loosely. Clarifying it matters.
LLM Reasoning (Generative)
Models like o1 or Claude produce step-by-step explanations. They "show their work."
- Not reproducible: The same query may produce different reasoning chains.
- Not verifiable: You can judge if it *seems* sound, but can't verify against formal criteria.
- Not auditable: The reasoning chain is generated, not derived from explicit rules.
Ontology-Governed Reasoning (Executed)
Reasoning isn't generated—it's executed. The system traverses relationships and applies constraints.
- Reproducible: Same query, same traversal, same result. Path is deterministic.
- Verifiable: Each step can be checked against the ontology definition.
- Auditable: Trace shows "Query matched X -> Rule Y applied -> Conclusion."
"Two categories. Not five options."
The real question isn't "Graph RAG or KG?" It's: Does the architecture allow the LLM to decide what's true?
- CAT_01: LLM-Interpreted
- Vector RAG
- Graph RAG
- Knowledge Graph + LLM
- CAT_02: Ontology-Governed
- Ontology-Driven Systems
LLM-Interpreted Architectures
These architectures use various strategies to retrieve relevant information. But the LLM synthesizes the final response. It interprets what the retrieved information means. It resolves ambiguities.
Vector RAG
What It Is
Query gets embedded, similar document chunks get retrieved, LLM synthesizes response from retrieved context.
Graph RAG
What It Is
Documents or chunks become nodes in a graph. Relationships connect related content. Query triggers graph traversal to gather context, then LLM synthesizes response.
Knowledge Graph + LLM
What It Is
Entities and relationships are extracted into a structured graph. Nodes are concepts, not chunks. Query maps to entity lookup and relationship traversal, then LLM synthesizes response.
The Shared Ceiling
"The LLM decides what retrieved information means."
This isn't a flaw—it's a design choice. For marketing chatbots or assistants, flexible interpretation is desirable. But for consistency and auditability, this ceiling doesn't move.
Ask the vendor: "If retrieved information conflicts, how does the system decide which is correct?"
If the answer involves "context" or "LLM understanding" -> Category 1.
Ontology-Governed Architectures
These architectures use formal ontologies to govern not just what gets retrieved, but what it means. The LLM handles natural language input and output. It doesn't decide what's true.
Ontology-Driven Systems
What It Is
Domain ontologies formally define concepts, relationships, constraints, and inference rules. The LLM is the interface. The ontology is the authority.
Ask the vendor: "Show me the reasoning chain—not the sources retrieved, but the logical steps from query to conclusion."
The Interface Layer
In ontology-governed systems, the LLM becomes the interface layer.
- User (Natural Language)
- -> LLM parses intent -> Formal Query
- -> Ontology governs retrieval & reasoning
- -> Deterministic Result
- -> LLM formats result
- -> User
Ask the vendor: "Show me the reasoning chain—not sources, but logical steps."
If they show rule application grounded in definitions -> Category 2.
What This Taxonomy Means
Implementation Rigor Has Limits
If you're operating with an LLM-interpreted architecture, better engineering improves outcomes within a bounded ceiling. You can move from 70% consistency to 85%. You cannot reach 100%. This is structural.
Governance Doesn't Have to Live Inside Your Application
Your vector RAG pipeline handles retrieval and generation. A separate governance layer validates outputs against formal criteria. Audit trails are captured independently.
The Ontology Investment Pays Off Only in Certain Conditions
High cost of inconsistency, established domain ontologies, and audit requirements. If your use case doesn't meet these, Vector RAG might be the right answer.
Complementary Patterns Don't Change Ceilings
Guardrails and evals are valuable, but they don't change what the underlying architecture can guarantee. They measure and filter a fundamentally probabilistic system.
Vocabulary Precision Enables Better Decisions
When vendors claim "knowledge graph" or "neuro-symbolic", you can now probe: What role does the ontology play? How does the system decide conflicting info? Is reasoning generated or executed?
"We'd rather you choose well than choose us."
A Specific Choice, Not a Universal Claim
We've spent the previous sections mapping a landscape without crowning a winner. That was deliberate. No architecture is universally optimal. But CogniSwitch exists, and we made choices.
01. Extraction
Ontology-governed, LLM-assisted.
Output is structured knowledge, not text chunks.
02. Execution
Deterministic execution.
Rules engine handles inference. Same input, same output, every time.
03. Evolution
Dynamic knowledge management.
Living system. New knowledge ingested, old deprecated.
Where We Land
The Honest Tradeoffs
Ontology selection is where we spend the most time.
Choosing the right ontologies, mapping them to customer-specific requirements, validating coverage—this is real work. It's not something we hide or automate away.
Not suited for domains without established ontologies.
If your industry doesn't have formal knowledge standards, building them from scratch is expensive. We're not the right fit for a domain that's still figuring out its own vocabulary.
Not suited for exploratory or creative use cases.
If you want a system that imagines, riffs, or generates novel ideas, our architecture will feel restrictive. We optimize for correctness, not creativity.
Not a weekend project.
You won't spin this up in a hackathon. The value comes from the rigor; the rigor takes time to establish.
We're betting on a future where regulated industries demand more than "good enough" accuracy.
"If your problem fits this shape, we should talk. If it doesn't, we'd rather point you to an architecture that fits than sell you something that won't."
The terminology is broken. "Neuro-symbolic," "knowledge graph," "agentic"—these terms have been stretched until they communicate nothing. We tried to restore meaning by showing what different architectures actually do, not what they claim.
Six dimensions. Three questions. Not a ranking of better and worse, but a tool for matching architecture to problem. What does your specific use case require? Which tradeoffs can you accept?
Five architectures, each with strengths and limitations. No universal winner. Just shapes that fit different problems.
The Questions That Matter
Where does meaning live?
In the model weights? In retrieved documents? In formalized ontologies?
The answer determines your traceability ceiling.
What happens when sources conflict?
Does the LLM guess? Do rules arbitrate? Is there a formal resolution mechanism?
The answer determines your consistency guarantee.
Can you show the reasoning chain?
Not just what was retrieved—but why it led to this conclusion.
The answer determines your audit readiness.
What's the cost of being wrong?
If a bad answer means a frustrated user, that's different from a compliance violation or patient harm.
The answer determines how much rigor you need.
What governance infrastructure exists independent of your core application?
If governance is embedded in your LLM pipeline, you're coupling two different problems.
If it's adjacent, you have flexibility.
"The tension between flexibility and consistency... these are enduring design choices, not temporary limitations."
For those building in regulated industries, the question isn't whether to address governance. It's when, and how.
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