We are developing the neurosymbolic reasoning foundation for autonomous agents - a System 2 harness for thinking deliberately with System 1 models.
LLM-based agents are powerful but fundamentally brittle — they hallucinate, resist steering, and offer no explanation for what they do. Deploying them in production means accepting opacity as a given. We don't.
Transformer models generate the most probable next token — not the most accurate answer. Without grounding in explicit knowledge, agents hallucinate facts, fabricate reasoning chains, and confidently produce wrong results. There is no architectural guarantee of correctness.
Hardcoded orchestration workflows break the moment requirements shift. Meta-agent approaches pass intent through context windows — instructions that degrade, get ignored, or conflict. Neither model gives operators reliable, inspectable control over what the agent actually does.
Attention weights are not a causal account of a decision. When an LLM-driven agent takes an action, there is no trace of why — only what. Debugging requires guesswork, alignment requires prompt tuning, and auditing is structurally impossible.
We build the algorithmic foundation that sits between your large model and your agent's actions — giving it the ability to reason structurally, act safely, and explain itself clearly.
An explicit and persistent representation of domain concepts, relations, and axioms — the structured knowledge foundation that powers reasoning, orchestration, and explainability across every agent session. Built from expert knowledge.
Composable blocks that combine neural inference with logical reasoning. Agent reasons using rules with conditions and consequences — not just pattern-match on tokens.
Declarative policies that govern how agents think, decide, and execute. Schemas aligned with a human expert's mental model — not imperative code that breaks at scale.
Every decision causally determined and traced. Audit any agent decision with a deterministic lineage of data and knowledge. Ensure agents do not take unauthorized action.
Use Polycog's software framework — cognition — to build your agent harness. Works with any model. Deploy it as you like - we are not a captive platform.
These aren't aspirational values — they're architectural constraints we've built into our stack. Trust is not a vibe; it's intentional design.
Every decision an agent makes should be traceable to a cause. We build infrastructure that makes opacity structurally impossible, not just configurable.
We provide the smallest useful unit of reasoning. You compose. This gives you control over the entire reasoning stack without inheriting our assumptions about your domain.
Safety is not a feature you add later. Constraints are baked into the reasoning loop itself — before any action is taken, not as a post-hoc filter that can be bypassed.
Let's team up to enable your cognitive agents.
Ping us and we'll reach out.