[Narrator]

Hello everyone, welcome back to the TokenMinds Training series.

Today we’ll explore how Agentic AI is transforming crypto treasury operations by reducing costs, removing manual bottlenecks, and dramatically improving decision speed.

This session focuses on operational impact.

We’ll look at how autonomous AI agents eliminate manual approvals and delays, and how treasury teams can redesign execution and risk control to capture time-sensitive opportunities at machine speed.

Agentic AI refers to autonomous systems that monitor, decide, and execute treasury actions without human approvals.

These agents operate within predefined policies and risk limits.

They continuously manage multi-chain balances, execute trades, optimize costs, and enforce treasury rules in real time.

Crypto treasuries face growing complexity.

Assets are spread across many blockchains, requiring constant manual monitoring and reconciliation.

Approval workflows and multisig delays slow decision-making in volatile markets.

Poor gas timing, slippage, and manual accounting increase execution costs and reduce returns.

Traditional treasury operations run during business hours, rely on dashboards, meetings, and manual transactions.

Agentic AI shifts this model to continuous on-chain monitoring, real-time analysis, and autonomous execution.

Decisions become policy-driven, actions are executed directly on-chain, and reconciliation happens automatically in real time.

Agentic AI removes manual friction across treasury operations.

Automated gas optimization and intelligent routing reduce transaction costs by sixty to seventy percent.

Manual monitoring and reconciliation work is reduced by up to eighty percent, allowing teams to focus on strategy instead of execution.

Execution speed moves from hours to seconds.

Slippage is minimized through intelligent order routing.

Risk policies are enforced automatically in real time, not after trades are completed.

Continuous optimization leads to better execution quality and stronger risk control.

Theoriq demonstrates agentic AI in practice.

Treasury teams wake up to find liquidity rebalanced, risk exposure reduced, and yield positions adjusted automatically.

No dashboards, notifications, or manual approvals are required.

Autonomous agents observe on-chain data, reason across multiple inputs, and execute across protocols.

Theoriq starts with real-time multi-chain data integration.

Live on-chain data, market signals, and cross-chain analytics are combined into a single view.

This eliminates reconciliation errors and accelerates decision-making.

Coverage spans all major EVM chains, with additional networks in progress.

AI-driven strategy logic evaluates signals using forecasting models and reinforcement learning.

Risk boundaries are enforced through immutable policy limits on assets and position sizes.

Signals include on-chain metrics, sentiment data, and AI price predictions.

Decision latency drops from hours to seconds, significantly improving opportunity capture.

Signal agents collect and normalize market data.

Strategy agents refine approaches using aggregated signals and learning models.

Action agents execute transactions on-chain based on approved strategies.

This closed-loop system runs without manual intervention, cutting gas costs, generating yield on idle assets, and reducing compliance work to near zero.

TokenMinds helps organizations design treasury autonomy safely and effectively.

We define where AI can decide and where humans retain control.

Our solutions include custom AI agents, multi-chain execution, encoded compliance policies, secure multisig infrastructure, integrated oracles, and continuous audit trails.

Thank you for watching and see you in the next training video.