Neural OS
How ISAAC thinks
Neural OS is not a single model. Every request flows through a deliberate chain so tone, safety, and accuracy stay stable even as engines swap underneath.
Request → Neural Gateway → Planner / Researcher / Critic swarm
Swarm outputs → Verifier (checks evidence + risk)
Verifier → optional retry → Signed answer + audit trace
Why a multi-agent swarm?
Different LLMs excel at planning, retrieval, or critique. ISAAC spins up purpose-built agents per request so you always get the best role for the moment.
Agents reason in parallel. The verifier compares answers, calls for more evidence if claims look weak, and only then releases the response.
How the verifier reduces hallucinations
Every answer is graded against objective checks: citations present, policies satisfied, risks mitigated.
If signals look off, the verifier restarts the swarm with new instructions or switches engines entirely.
What lands in your logs
Planner, tool calls, retrieved documents, and verifier decisions are streamed to Steward so you can replay a conversation, export it to auditors, or fine-tune adapters later.