A substrate-level architecture for voice AI that solves the state commitment and propagation problem in multi-agent systems.
View the interactive S1/S2/S3 simulation directly in your browser: Live Commitment Kernel Demo (v6)
Run the Commitment Conservation Harness (CCH) to measure semantic stability over recursive loops:
cd harness
# Ensure OPENAI_API_KEY is set in your environment
python3 run_convergence_v2.pyExperimental data for EXP-001 through EXP-007 is archived in harness/results/.
/harness— The Engine. Core logic for the CCH, including the recursive transformation loops and NLI/Jaccard metric implementations./demo— The Visualization. HTML/JS simulation of the S3 parallel governance model./schema— The Contract. Formal JSON schema for the multi-channel Commitment Kernel (K)./docs— The Theory. Deep Wiki and the formal "Conservation Law" paper..planning/codebase/— Technical Handoff Docs. Structured summaries of the stack, architecture, and current technical concerns.
Instead of passing text through a chain of agents (where errors compound), this architecture extracts a canonical state representation K once from the caller utterance, hash-locks it, and has all downstream agents consume K read-only.
- Drift Decoupling: Error becomes ε regardless of agent count, instead of n×ε.
- Bound Projections: Multimodal K (Text, Prosody, Temporal, Affect) with independent hashes.
- Governance at Execution: O(1) validation of agent outputs against the cryptographic root K.
- Latency Collapse: Eliminates inter-agent dependency, allowing for near-parallel execution (S3) and ~87% latency reduction.
In single-turn demo loops, LLM APIs utilize KV Caching, which artificially masks the token-load penalty of sequential (S1) architectures. The 7–11% gap observed in the demo is a floor caused by the measurement environment. In multi-turn production calls, S1 context bloat significantly widens the performance gap relative to the O(1) K-governed architecture.
- State vs. Context: Replaces linear context growth ($O(N)$) with a fixed-size Commitment Kernel ($O(1)$).
- Governance vs. Probability: Replaces probabilistic "next-token" guessing with deterministic execution gating.
- Parallel vs. Sequential: Enables simultaneous agent launch (S3) by removing inter-agent text dependencies.
Business Source License 1.1 — source‑available for non‑production use. Commercial use requires a separate license from Ello Cello LLC.
Patent Pending: U.S. Serial No. 63/877,177 (Provisional), 19/426,028 (Utility) MO§ES™ is a trademark of Ello Cello LLC (USPTO IC 042). © 2026 Ello Cello LLC. All Rights Reserved. See LICENSE for full terms, including the Lineage Custody Clause.