The Agentic Control Plane for
AI Agent Authority in Web3 and Enterprise
DeAgenticAI's Agentic Control Plane enforces cryptographic policy over AI agent authority — separating what an agent can do from what it is authorized to do — in Web3 and enterprise financial environments.
Master Happy Path Diagram
Happy Path Protocol Active
How the Control Plane Works
DeAgenticAI enforces safe autonomous execution through three architecture differentiators: Intent Sanitization at the boundary, Policy-First Enforcement at the cryptographic signing layer, and MPC Distributed Execution with no single point of failure.
Intent Sanitization
Every agent request is normalized and sanitized before enforcement, blocking malformed or unsafe payloads at the boundary competitors leave exposed.
Policy-First Enforcement
Authorization is enforced cryptographically at signing time, not as an API middleware check, so policy remains binding under real execution conditions.
MPC Distributed Execution
Threshold execution is coordinated across distributed MPC nodes so no single key holder or infrastructure component can unilaterally move capital.
Modular building blocks
for agentic systems
DeAgenticAI is organized into six control-plane layers: Agent Identity & Registry, Intent Sanitization, Policy Engine, Fraud Detection, MPC Distributed Execution, and Chain Abstraction. Together they enforce safe, verifiable autonomous execution across Web3 and enterprise environments.
Agent Identity & Registry
Verifiable identity and authority registry for autonomous agents, including scope, provenance, and delegated execution boundaries.
Intent Sanitization
Every request is normalized and sanitized at the boundary to remove malformed, unsafe, and ambiguous execution payloads.
Policy Engine
Cryptographic policy enforcement at signing time, not middleware-only controls, with explicit limits for spending, targets, and delegation.
Fraud Detection
Runtime anomaly and fraud controls score each execution request and block high-risk behavior before signatures are produced.
MPC Distributed Execution
Threshold signatures are generated across distributed MPC nodes so no single key holder or service can move capital alone.
Chain Abstraction
Chain-agnostic execution routes approved intents across networks with consistent policy enforcement and settlement semantics.
Control, safety, and autonomy
for agentic AI in Web3
DeAgenticAI is built for Web3 builders, AI engineers, and security-conscious organizations seeking policy-based control and safe execution for autonomous agents. Our infrastructure enables human-defined constraints and composable integration across decentralized services.
AI Infrastructure Teams
- Ship governed execution without building key management from scratch
- Replace ad-hoc wallet orchestration with deterministic policy controls
- Avoid single-vendor custody lock-in as the stack evolves
- Get audit-grade traces for every agent decision and signature
Web3 Startups
- Reduce platform risk from third-party wallet vendor consolidation
- Launch agent products without rebuilding custody and signing primitives
- Enforce policy boundaries before transactions hit the chain
- Preserve product velocity while hardening execution governance
DAO Treasury
- Cut governance latency on routine treasury operations
- Prevent signer concentration and key-person execution risk
- Enforce proposal-aligned spending policies automatically
- Keep transparent, on-chain auditable trails for every action
Enterprise
- Map autonomous execution to internal approval and risk controls
- Enforce cryptographic policy across teams, wallets, and workflows
- Integrate governed delegation without replacing core systems
- Produce regulator-ready evidence for oversight and audit
RWA Management
- Operate tokenized portfolios with bounded agent authority
- Constrain rebalancing, settlement, and transfer rules by policy
- Reduce continuity risk with distributed signing infrastructure
- Maintain traceable control for institutional counterparties
Compliance / MiCA & DORA
- Implement explicit control surfaces for regulated environments
- Detect anomalous intents before signing and broadcast
- Generate verifiable audit trails for supervisory review
- Support MiCA and DORA resilience expectations by design