Maiga runs AI agents that research, decide, and execute across digital-asset and RWA markets. Built on TEE-secured execution, a multi-venue connectivity layer, and institutional controls, so funds, fintechs, and platforms can deploy agentic strategies with confidence.
The first wave of AI in markets stopped at signals and dashboards. Maiga closes the loop: agents that perceive market state, reason under a defined mandate, execute across venues with hardware-secured signing, and prove every action with an audit trail. The same engine that reached millions of retail users now serves institutions moving into agentic, cross-asset finance.
Each layer is independently hardened and composable. Run the full agent, or integrate a single layer into your own systems.
Agents that move from analysis to action: strategy generation, order sizing, signing, and routing, with human-in-the-loop approval where you require it.
Private keys, proprietary models, and strategy code execute inside Trusted Execution Environments. Signing and attestation happen where they can be verified, not exposed.
Order books, funding rates, and on-chain liquidity from CEXs, AMMs, and RWA rails flow through a unified Model Context Protocol interface, abstracting away per-venue APIs.
Agents read charts, order-book heatmaps, and on-chain activity directly, turning visual and structured signals into quantitative, explainable decisions.
On-chain aggregation across networks, social and sentiment signals, and machine-learning labels, delivered to agents and to your own systems via APIs and SDK.
One agent framework spanning perpetuals, spot, and DeFi through to tokenized treasuries, credit, and commodities as the asset base moves on-chain.
Every cycle is bounded by policy and recorded end to end. Nothing executes outside the limits you set.
Pull live order books, funding, basis, and on-chain flow across every connected venue through the MCP layer.
Multimodal models weigh the regime, score conviction, and form a strategy with an explainable rationale.
Orders are checked against mandate, signed inside the Trusted Execution Environment, and routed to venue.
Fills, slippage, and cryptographic attestations are written to an immutable audit trail for review.
A single agent framework that extends from native digital assets into the tokenized real economy.
Deploy Maiga as a product, a desk capability, or an embedded layer inside what you already run.
Ensemble agents, cross-venue liquidity sensing, and automated execution with mandate-level risk controls.
Give your users AI-driven analysis and execution through our API and SDK, without building the agent stack yourself.
Move into tokenized treasuries and RWA yield with agents that respect policy, duration, and reporting needs.
Integrate Maiga agents into your dApp or wallet to deepen engagement and volume across your user base.
Autonomy is only useful when it is bounded, verifiable, and accountable. That is the default, not an add-on.
Keys, models, and strategy logic run inside TEEs. Signing and attestation are verifiable; secrets never leave the enclave.
Every perceive-reason-execute cycle is logged with inputs, rationale, fills, and cryptographic proof for reconciliation and review.
Set mandates on notional, leverage, venues, and assets. Choose where agents act autonomously and where a human approves.
Smart contracts audited by SolidProof, with a security posture built to extend toward formal institutional attestations.
Reach the full stack through one API and SDK: market context, agent reasoning, and TEE-signed execution. Connect your venues, set your mandate, ship.
# Deploy an agent under a defined mandate from maiga import Agent, Mandate agent = Agent( venues=["binance", "hyperliquid", "pancakeswap"], assets=["BTC", "ETH", "tokenized-tbill"], runtime="tee", ) agent.set_mandate(Mandate( max_notional=250_000, max_leverage=3, require_approval=False, )) trace = agent.run() # perceive -> reason -> execute -> verify print(trace.attestation) # 0x9f...c4
The token coordinates access, data contribution, and ecosystem incentives. The product stands on its own; the network rewards the participants who make it stronger.
A token model anchored to real activity: trading volume, liquidity, and market demand rather than emissions alone. It ties network value to usage, applying (3,3) game theory to align participants toward a fair, durable outcome.
Request institutional access for a technical walkthrough of the platform, TEE architecture, and integration path.