The Swiss Cheese Problem: Why AI Agents Need Symbolic Backbone
As organisations roll out AI agents, they hit a paradox: systems with superhuman capabilities can still stumble in ways that feel shockingly simple. This tension - sometimes called the “Swiss cheese problem” - shows up when a large language model (LLM) performs brilliantly on complex tasks for many steps, only to take one small but significant wrong turn and produce an output that’s wildly - and sometimes dangerously - illogical. For leaders raised on the determinism of traditional software, this probabilistic fickleness can be deeply unsettling. This is not what we expect from a computer. Let’s dig into why it happens.
🔵 Distributed vs Local Representations
ChatGPT and Gemini are built on a radically different architecture. Neural networks like LLMs operate on distributed representations: concepts are “smeared” across millions of parameters, enabling creativity, tolerance of noise, and impressive generalisation from messy data.
By contrast, we grew up with logic-based computer systems. These symbolic systems use local representations: symbols and variables are discrete, unambiguous, and composable. This is the foundation of logic, programming, and mathematics.
🔵 The Power of Symbolic Systems
Symbolic logic excels where composability matters. Equations, software, and formal rules allow us to construct new structures with predictable meaning. This enables systematic generalisation - the ability to handle an infinite set of novel inputs reliably, something that purely neural systems often fail to achieve.
🔵 It’s All About Integration
Neural and symbolic systems are not rivals; they are complements. Neuro-symbolic AI combines the strengths of both. Neural networks learn from noisy, unstructured data. Symbolic logic enforces rigour, interpretability, and verifiability. Together, they form a feedback loop where symbols constrain networks and networks extend symbolic knowledge.
🔵 The Hybrid Pattern in Action
We’ve already seen this succeed. AlphaFold didn’t break through with deep learning alone - it integrated physical and geometric constraints. AlphaEvolve applied the same principle to software, combining LLMs with symbolic code testing. The same logic applies to enterprise AI: neuro-symbolic integration is the most promising path to agents that are not only powerful and creative, but also reliable and trustworthy.
For enterprises, the missing link is often a Knowledge Graph. Knowledge Graphs provide the symbolic backbone - a structured, interpretable layer of meaning, identity, and relationships - that can both guide neural models and be enriched by them. They allow organisations to ground AI in shared semantics, enforce consistency, and create the feedback loops where learning and logic reinforce one another.
⭕ Working Memory Graph: https://www.knowledge-graph-guys.com/blog/the-working-memory-graph
⭕ Continuous and Discrete: https://www.linkedin.com/posts/tonyseale_semanticweb-ai-llms-activity-7093138529299378177-B0Nw/
⭕ Vectorizing Your Knowledge: https://www.linkedin.com/posts/tonyseale_knowledgegraph-llms-datascience-activity-7095675217133359104-EsxC/
