Most AI agent frameworks treat the LLM output as ground truth. It comes back, you act on it. That's the problem. Axiom is a new Python runtime that changes the contract between agent and LLM. Every belief your agent forms carries: A confidence score (0.0–1.0) — how sure is the agent? A provenance chain — where did this belief come from? An is_actionable flag — should the agent act on this? And if you're running multiple agents, Axiom lets them verify each other without a central orchestrator . The problem with current frameworks LangChain, CrewAI, AutoGen — they all give you tool use and orchestration. Some give you memory. None of them ask: how confident is this agent in what it just said? This matters because: Agents hallucinate with full confidence In multi-agent systems, you're trusting Agent B's output blindly There's no audit trail of why an agent did something What Axiom gives you from axiom import AxiomAgent , BuiltinConstraints import anthropic client = anthropic .…