AINL helps turn AI from "a smart conversation" into "a structured worker." It is designed for teams building AI workflows that need multiple steps, state and memory, tool use, repeatable execution, validation and control, and lower dependence on long prompt loops. AINL is a compact, graph-canonical, AI-native programming system for (READ: README)

Governance-as-Code for AI agents. Declarative constraints with deterministic enforcement. Provisioning Identity, Tool-based rules, Brokering Credentials & Ensuring safe deployment

Deterministic agent runtime with explicit plans, typed tools, permissions, and replay. Implements Google DeepMind's Intelligent AI Delegation framework. Named after ELP's Karn Evil 9.

Stop Claude Code from doing irreversible damage. Policy-gated execution + receipts so you can ship agents without sweating production.

An architecture-level AI harness that compiles constraints and NFRs into explicit, reviewable architecture decisions. Combines a deterministic compiler, curated pattern registry, and agent workflow skills to drive approval, re-approval, and implementation against an architectural contract.

Official HDEX Q1 public release mirror: signed Windows offline package, verification hashes, license, and documentation for controlled local execution workflows.

M87 Studio's audit agent enforcing SPOT and FORT governance rules for Python codebases.

Decentralized compute protocol turning idle devices into verifiable AI infrastructure. Mobile-first, open, and accessible.

A kernel-userland protocol enforcing information-theoretic bounds on AI adaptivity leakage, benchmark gaming, and capability spillover.

Deterministic graph execution engine built in Rust

Production-grade Autonomous Execution Platform. Deterministic local orchestration, self-healing AST patching, Watchdog process governance, and Time-Travel Replay telemetry. 🚀

AIΩN – A deterministic architecture for history-native computation. From WARP, a recursive provenance, to Continuum, a protocol for distributed causal computing.

A policy-gated autonomous execution substrate for AI agents. Proposal ≠ Execution. BSL 1.1.

Execution OS with human approval gates and pressure-driven task control.

A cycle-accurate simulation research comparing deterministic execution on the Patmos processor against a conventional speculative CPU, demonstrated through a real-time obstacle avoidance scenario with live WCET, jitter, and deadline analysis.

R-LAM is a reproducibility-constrained execution framework for Large Action Models in scientific workflow automation. It enables adaptive, agent-driven workflow execution while enforcing strict guarantees on auditability, determinism, and replayability.

Normative protocol core for Riverbraid deterministic verification and fail closed governance.

Constitutional Verification Model (CVM), a layered framework for reasoning about how decentralized systems achieve legitimate rule authority and independently verifiable execution through invariant correctness, deterministic execution, and replayable history.

Deterministic intermediate representation for AI agents — compile, verify, execute, and replay structured intent.

The Security Kernel for AI Agents — MCP/A2A gateway with policy enforcement, taint tracking, sandboxed execution, deterministic envelopes, and Sigstore audit. OWASP ASI 2026 compliant.