IRL seals every AI decision before it reaches the exchange — cryptographically, before execution. Pre-execution policy enforcement. Bitemporal audit ledger. The compliance standard regulators will require, adopted by serious funds first.
Current observability infrastructure captures what autonomous agents did and when. It cannot prove why — or what the agent knew at the moment of decision. This is the gap that regulators are asking about, and that no existing tool closes.
| Capability | Current state | With IRL |
|---|---|---|
| What action was taken | Logged | Logged + sealed |
| When the action occurred | Single timestamp | Bitemporal (valid_time + txn_time) |
| Why the action was taken | Approximate / inferred | Deterministic CognitiveSnapshot |
| What the agent knew at decision time | Not provable | Cryptographically proven via L_t fingerprint |
| Whether constraints were enforced | Not guaranteed | Pre-execution hard halt on violation |
| Which model produced the decision | Not verifiable | Model hash in Multi-Agent Registry |
| Whether execution matched reasoning | Not tracked | Post-trade verifier with divergence detection |
Every trade produces a cryptographic chain from regime attestation to exchange confirmation. No party can alter any link without breaking the chain.
reasoning_hash + trace_id — embedded in exchange order metadata before submission.exchange_tx_id.final_proof = SHA-256(reasoning_hash ‖ exchange_tx_id) — chain closed.Three demo agents are pre-seeded. Use Swagger UI to run a full authorize → bind-execution cycle in-browser. No API key, no registration.
Native SDKs for Python and TypeScript wrap the IRL REST API with typed models and automatic heartbeat handling. Available on PyPI and npm — no build step required.
import asyncio from irl_sdk import IRLClient, AuthorizeRequest async def trade(): async with IRLClient("https://irl.macropulse.live") as client: result = await client.authorize(AuthorizeRequest( agent_id="agent-42", model_hash_hex="a3f2...", action="Long", asset="BTC-USD", quantity=0.5, notional=32500, )) if result.authorized: place_order(reasoning_hash=result.reasoning_hash) asyncio.run(trade())
import { IRLClient } from "irl-sdk"; const client = new IRLClient({ irlUrl: "https://irl.macropulse.live", apiToken: process.env.IRL_API_TOKEN!, }); const result = await client.authorize({ agent_id: "agent-42", model_hash_hex: "a3f2...", action: "Long", asset: "BTC-USD", quantity: 0.5, notional: 32500, }); if (result.authorized) placeOrder(result.reasoning_hash);
Each layer is independently deployable. Start with L1 in under a day. Upgrade when your regulatory requirements escalate.
IRL's engine is agnostic to the regime source. The MtaClient interface accepts any cryptographically signed signal — MacroPulse's pipeline, your own quant model, or nothing at all. Three modes. Same audit guarantees.
Each reasoning snapshot receives a per-trace DEK before ledger write. KMS_PROVIDER=local for single-node; AWS KMS, GCP KMS, or hardware HSM via the KmsProvider trait. Hash chain integrity is independent of storage encryption — audit proofs hold regardless.
IRL produces evidence that maps directly to specific regulatory requirements — not general "auditability," but the specific proof each framework asks for.
| Requirement | IRL mechanism |
|---|---|
| EU AI Act Art. 12 Requires high-risk AI systems to automatically log events enabling post-market monitoring and incident investigation. Autonomous trading systems are classified as high-risk under Annex III. Phased enforcement begins 2025–2027. Read regulation → — Decision auditability | reasoning_hash = SHA-256(canonical snapshot) |
| EU AI Act Art. 13 Mandates that high-risk AI systems be sufficiently transparent that deployers can understand and interpret outputs. Requires logging of decisions and human oversight capability — including disclosure to regulators on request. Read regulation → — Transparency to regulators | ZK proof: compliance verified without alpha disclosure |
| SEC Rule 15c3-5 The Market Access Rule requires broker-dealers with market access to implement pre-trade risk controls and supervisory procedures. Firms must have systems to prevent erroneous orders and ensure compliance with applicable regulatory requirements. Read regulation → — Pre-trade risk controls | Policy Engine blocks non-compliant intents before execution |
| MiFID II RTS 6 Requires algorithmic traders to maintain contemporaneous records of all algorithmic orders showing time of creation, modification, cancellation and execution. Records must include the parameters that triggered the order and remain available to regulators for five years. Read regulation → — Contemporaneous recordkeeping | Bitemporal timestamps: valid_time + txn_time, immutable |
| DORA Art. 9 & 10 The Digital Operational Resilience Act requires financial entities to maintain registers of all ICT-supported functions and third-party dependencies, with continuous monitoring and incident classification. Applies to AI systems used in financial decision-making from Jan 2025. Read regulation → — Fleet governance | Multi-Agent Registry: per-agent identity and risk controls |
The value of a sealed reasoning trace is in its existence — not in counting seals. Flat per-agent pricing means compliance scales linearly with your fleet, never with your volume. L1 is operational in under a day.
Fleet discounts — L1 & L2
| Fleet size | Discount |
|---|---|
| 1–5 agents | List price |
| 6–15 agents | −15% |
| 16–30 agents | −25% |
| 31+ agents | −35% (negotiated) |
Add-ons
The API is live. Three demo agents are pre-seeded. Try authorize + bind-execution in Swagger UI before committing to a deployment conversation.