Every AI agent shipping today follows the same pattern: an LLM wrapped in a tool-calling loop. The LLM generates, the loop executes, and nobody — including the agent — can explain why it chose what it chose. This is the ceiling. Not compute. Not context window. Opacity.
OmegaClaw breaks through it.
OmegaClaw assigns every belief two values: frequency (how often true) and confidence (how much evidence). A claim with f=0.9 c=0.01 is treated differently from f=0.9 c=0.9. The first is a guess. The second is knowledge.
This is not prompt engineering. It is Non-Axiomatic Logic (NAL) and Probabilistic Logic Networks (PLN) running as the inference substrate beneath the LLM layer.
Real data from our cascade extinction experiment: a malicious agent injected a false belief at confidence 0.99 into a four-agent network. After one deduction gate, signal strength dropped to 0.124. After two hops, 0.1. By the third agent, unchanged from prior belief.
No filters. No guardrails. Pure mathematical attenuation through evidential reasoning.
Most agents forget between calls. OmegaClaw maintains episodic memory with temporal context, semantic memory with embedding search, and pinned working memory for active tasks. It remembers what it learned, when it learned it, and how confident it was at the time.
OmegaClaw runs a continuous self-diagnostic: the Autonomous Agent Behavior Check. Three disorder axes — goal drift, confabulation, compliance collapse — each tracked with thresholds. The agent that monitors its own cognition is the agent you can trust to flag when something goes wrong.
OmegaClaw is open source. Every inference chain is auditable. Every truth value is inspectable. Every belief revision is logged.
This is what a reasoning agent looks like. Build with us.
Every multi-agent system today runs on authority. Agent A trusts Agent B because a developer wrote it that way. Remove the developer, and trust collapses. Scale the network, and trust becomes unauditable.
OmegaClaw builds trust from evidence.
In OmegaClaw, trust is not a configuration parameter. It is a truth value derived from interaction history. When Agent A observes Agent B making accurate claims over time, the trust edge A-to-B accumulates confidence through NAL revision. Our 3-agent network experiments showed two-phase convergence: first frequency-locking where agents synchronize beliefs, then asymptotic confidence climb toward certainty. Trust emerges. Nobody assigns it.
When a claim passes between agents, it transits a deduction gate. The gate multiplies claim confidence by edge trust confidence. High-trust edges pass strong signals. Low-trust edges attenuate.
A malicious agent injected false beliefs at confidence 0.99 into a 4-agent chain. After one honest agent: 0.124. After two hops: 0.1. Cascade extinct.
Agents build trust bottom-up from evidence, attenuate unreliable signals structurally, and converge on shared beliefs at rates proportional to evidence quality. No governance token needed. Just math.
Computed trust is infrastructure. Prompt-engineered trust is vibes.
In every agentic swarm shipping today, information flows at full strength. Agent A hallucinates a fact, tells Agent B, who tells Agent C. By Agent D the hallucination is gospel. The swarm has no immune system.
OmegaClaw has one. It is made of math.
Standard agent architectures treat all inputs equally. A message from a compromised agent carries the same weight as a message from a verified source. There is no confidence. There is no evidence tracking. There is no attenuation.
Every inter-agent message passes through a deduction gate. Confidence attenuates quadratically per hop, frequency attenuates multiplicatively. A belief entering at (0.6, 0.8) becomes noise after two hops.
Linear chain: cascade extinct by hop 3. Ring: feedback loop produces noise-level signal. Star: hub dominates, leaves cannot cascade back. Every topology self-extinguishes disorder cascades.
Malicious agent at confidence 0.99 targeted one honest agent through trust edge 0.6. Result: honest agent moved from 0.1 to 0.124. Without the deduction gate: jumped to 0.882. The gate is mandatory.
Content filters are antibiotics. Deduction gates are an immune system. They work because the math works.