Decision Execution Layer
The Always-On Runtime for AI-Native Decision Agents
One line: A continuous AI execution fabric that turns personalized intent into verifiable actions — with real-time routing, adaptive reasoning, and proof-based automation.
🧭 1. Scope & Responsibilities
Routing & Guardrails Dynamically route decisions to the best-performing data source, service, or venue based on identity, latency, and reliability. Every call runs under contextual limits —
{budget, risk, market, slippage, jurisdiction}— enforced at runtime.AI Orchestration Engine Converts each agent’s strategy (indicators, personalization, context, style) into continuous execution graphs. The system coordinates retries, back-offs, and policy-based branching to ensure decisions complete successfully 24/7.
Proof-Based Execution Each action emits a Proof-of-Delivery (PoD) artifact — a verifiable cryptographic record confirming that the decision or service executed as intended.
Secure Runtime Environment Agent keys and sessions operate inside Trusted Execution Environments (TEE/SGX/SEV) with remote attestation, scoped session keys, and optional MPC signing for elevated operations.
🧩 This layer ensures that AI agents can act, learn, and verify autonomously — without ever leaving user-defined boundaries.
🔐 2. AI Wallet & Key Security
Autonomy without custody. Every agent runs its own AI wallet, cryptographically isolated from both user and platform.
Keys in Enclave: Private keys never leave the hardware-isolated environment. All signatures occur in-place.
Remote Attestation: Each action verifies enclave integrity before execution.
Scoped Sessions: Temporary session keys enforce execution scope and expiration.
Budget Locks: Spend and risk caps defined per session.
MPC Quorum: Multi-party co-signing prevents single-point failure.
Rate & Replay Control: Nonce sequencing guarantees idempotence.
Audit Hooks: Every execution appends a hash-chained log for transparent traceability.
Agents run autonomously, but every move is cryptographically provable.
🧩 3. Proof-of-Execution & Verification
Proof Sources: Every fulfillment — whether a data retrieval, model call, or trade signal — is signed at origin.
zk-/TEE-TLS Attestation: Validates that the data or result truly came from the declared endpoint.
Binding & Context: Each proof is tied to the originating agent, decision ID, and runtime policy — preventing reuse or forgery.
Outcome Registry: Proofs are logged into an on-chain index for later analysis, attribution, and auditing.
Every decision made by an agent is measurable, reproducible, and verifiable.
⚡ Why It Matters
Today’s AI systems stop at “recommendation.” The Decision Execution Layer takes the next step — enabling agents to:
Execute autonomously within risk and policy constraints.
Operate continuously across global data and execution services.
Verify every action through cryptographic PoD and zk-attested context.
Learn safely from feedback without exposing user keys or private context.
TradeOS provides the infrastructure for always-on, self-verifying decision loops — bridging the gap between insight and action, between AI autonomy and human control.
🧠 In Summary
Component
Purpose
TradeOS Advantage
Routing & Guardrails
Dynamic orchestration with contextual limits
AI-adaptive routing based on reputation, cost, and latency
AI Runtime
Continuous reasoning + execution
Runs 24/7 under personalized strategies
Wallet & Keys
Secure, enclave-based isolation
No custodial risk, MPC & attestation supported
Proof-of-Delivery
Verifiable execution log
zk-TLS / TEE-TLS attested, audit-ready
Learning Feedback
Closed-loop verification
Decisions feed future scoring & routing
TradeOS Decision Execution Layer: The runtime where your AI doesn’t just recommend — it acts, verifies, and evolves.
Last updated