Secure AI.
Before It Gets Dangerous.

Capability-Partitioned, Flow-Controlled AI Workflows

High-Level Public Overview

What This Is

This architecture describes a safer way to build automated systems and AI assistants by structurally separating thinking from doing. Instead of allowing an AI or automated agent to both decide and act, the system enforces a pipeline where:

Reasoning can propose actions, but cannot execute them.
Execution can perform actions, but cannot reason or self-direct.

The Core Idea: Read → Reason → Propose → Validate → Authorize → Execute

• Read / Reason — analyze information (read-only, no side effects)
• Propose — create immutable task descriptions
• Validate — check proposals against policy and safety rules
• Authorize — explicit approval by humans or governance processes
• Execute — perform actions, but only when authorized

Why This Matters

Modern "agentic AI" systems often combine reasoning, tool access, credentials, and execution inside a single always-on process. This collapses trust boundaries and creates systemic risk: prompt injection can lead to real-world actions, compromised interfaces can trigger irreversible operations, and one bug can cause cascading failures.

This architecture eliminates those failure modes by design, not just by policy.

Key Safety Properties

Structural Safety: Reasoning components cannot call APIs, use credentials, or perform network actions.

Immutable Proposals: Task specifications are content-addressed and declarative. They describe intent without performing actions.

Monotonic Validation: Validators can only restrict execution, never grant additional authority.

Explicit Authorization: Every real-world action requires human approval, multi-party approval, time delays, or governance rules.

Flow Control: The pipeline is capacity-limited. Reasoning can't overwhelm execution. Approvals can't be bypassed.

No component may both decide and act. Authority must flow through explicit approval gates. Safety must be enforced by architecture, not behavior.

Deterministic, Attested, and Replayable AI Execution

High-Level Public Overview

What This Is

This architecture defines how to run AI and automation in a way that is provable, replayable, and tamper-resistant by construction. Instead of trusting that an AI system "behaved correctly," the system makes it cryptographically verifiable that the output was inevitable given the declared inputs, software, and randomness.

How It Works: Build → Attest → Execute → Log → Replay → Verify

• Build deterministically — environments built from hash-verified components, no network access during installation
• Attest the environment — each execution proves cryptographically what software stack ran
• Pre-commit all randomness — fixed before execution, outputs can't be cherry-picked
• Execute immutable workflows — content-addressed by hash
• Log everything — sufficient to independently re-run or verify
• Replay to verify — anyone can verify the output is exactly what must have happened

Key Guarantees

Deterministic Environments: Built from immutable, hash-verified software. No runtime package fetching. Any change produces a new root hash.

Anti-P-Hacking: All stochastic behavior derived from a pre-committed seed. No rerunning until you like the result.

Replayable Logs: Logs include all artifact hashes, inputs, derivation seeds, environment attestations, and execution traces.

AI outputs must be inevitable, not discretionary. Trust must derive from cryptographic proof, not reputation. Verification must be possible by third parties.