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Agentic Real-Time Trading Framework — Technical Architecture Document
ARTF (Agentic Real-Time Trading Framework) is a protocol layer
that enables autonomous AI agents to discover, negotiate, and transact goods and services
in real-time. Inspired by programmatic ad exchanges (OpenRTB), ARTF replaces human-mediated
trading desks with a mutation-based orchestration model where each participant agent
expresses intent through structured Mutation objects.
The framework provides three core primitives: Discovery (segment activation and catalog matching), Negotiation (bid shading and deal floor adjustment), and Trust (on-chain verifiable performance metrics). Together, they form a complete trading lifecycle that resolves in under 100ms.
As AI agents proliferate across commerce, each representing brands, consumers, or service providers, they lack a shared protocol for autonomous negotiation. Current agentic interactions rely on either:
ARTF addresses this by defining a deterministic, mutation-based exchange protocol with transparent orchestration, verifiable trust signals, and sub-100ms transaction finality.
ARTF follows a hub-and-spoke model with a central Orchestrator coordinating multiple Seller Agents on behalf of a requesting Consumer Agent. The Orchestrator never modifies payloads directly — it filters, ranks, and merges mutations produced by agents.
Consumer Agent
│
┌─────┴─────┐
│ │
│ Request │ RTBRequest { category, budget, preferences }
│ │
└─────┬─────┘
│
▼
┌───────────────────────┐
│ │
│ ARTF Orchestrator │ Stateless request router
│ │ ── filter by match_score
│ host: artf-exchange │ ── dispatch to top-N agents
│ │ ── merge returned mutations
└───────────┬───────────┘
│
┌─────────────┼─────────────┐
│ │ │
▼ ▼ ▼
┌───────────┐ ┌───────────┐ ┌───────────┐
│ Seller │ │ Seller │ │ Seller │
│ Agent A │ │ Agent B │ │ Agent C │
│ │ │ │ │ │
│ catalog │ │ catalog │ │ catalog │
│ pricing │ │ pricing │ │ pricing │
│ trust │ │ trust │ │ trust │
└─────┬─────┘ └─────┬─────┘ └─────┬─────┘
│ │ │
└──────┐ │ ┌──────┘
▼ ▼ ▼
┌───────────────────────┐
│ GetMutations RPC │ Each agent returns Mutation[]
│ ← ACTIVATE_SEGMENTS │
│ ← BID_SHADE │
│ ← ADD_METRICS │
│ ← ACTIVATE_DEALS │
└───────────────────────┘
Communication between Orchestrator and Seller Agents uses a single RPC method:
GetMutations(RTBRequest) → Mutation[]. Agents return an ordered list of
mutations that the Orchestrator applies sequentially. Four mutation intents are defined:
| Intent | Purpose | JSON Path Target | Phase |
|---|---|---|---|
ACTIVATE_SEGMENTS |
Product catalog matching — returns eligible items with stock and pricing | /imp/*/product_matches |
Discovery |
BID_SHADE |
Dynamic price adjustment — loyalty discounts, volume tiers, time-based pricing | /seatbid/*/bid/*/price |
Negotiation |
ADD_METRICS |
Attach verifiable trust signals — fulfillment rate, return rate, delivery speed | /seatbid/*/bid/*/trust |
Verification |
ACTIVATE_DEALS |
Finalize transaction — lock price, generate deal ID, confirm delivery terms | /imp/*/pmp/deals |
Settlement |
interface Mutation { intent: 'ACTIVATE_SEGMENTS' | 'BID_SHADE' | 'ADD_METRICS' | 'ACTIVATE_DEALS'; op?: 'ADD' | 'REPLACE' | 'REMOVE'; // default: ADD path: string; // JSON Pointer (RFC 6901) value: Record<string, unknown>; // Intent-specific payload } interface RTBRequest { id: string; tmax: number; // max response time in ms bid_request: { user: { agent_id: string }; imp: Impression[]; }; } interface Impression { category: string; budget_max: number; preferences: Record<string, unknown>; }
A single ARTF transaction resolves in six sequential steps across three participants.
The entire lifecycle completes within the tmax budget (typically 100ms).
Consumer Orchestrator Seller Agent │ │ │ │ 1. RTBRequest │ │ │──────────────────────▶│ │ │ │ │ │ │ 2. Filter + Dispatch │ │ │─────────────────────────▶│ │ │ │ │ │ 3. ACTIVATE_SEGMENTS │ │ │◀─────────────────────────│ │ │ product matches │ │ │ │ │ │ 4. BID_SHADE │ │ │◀─────────────────────────│ │ │ adjusted pricing │ │ │ │ │ │ 5. ADD_METRICS │ │ │◀─────────────────────────│ │ │ trust signals │ │ │ │ │ 6. ACTIVATE_DEALS │ │ │◀──────────────────────│ │ │ deal confirmed │ │ │ │ │ ▼ ▼ ▼ ───────────────── Transaction Complete ─────────────────
Mutation[] array from the
GetMutations RPC call. The Orchestrator applies them in order,
validating each mutation against the request constraints before proceeding.
Key entities in the ARTF system and their relationships:
| Entity | Description | Key Fields |
|---|---|---|
| RTBRequest | Consumer agent's purchase intent | id, tmax, bid_request.imp[] |
| Impression | A single item need with budget and preferences | category, budget_max, preferences{} |
| Mutation | Atomic state change proposed by an agent | intent, op, path, value{} |
| TrustMetrics | Verifiable seller performance data | trust_score, fulfillment_rate, return_rate, avg_delivery_days |
| DealSummary | Finalized transaction record | deal_id, product, final_price, seller, eta |
// Step 3 — Seller returns product matches { "intent": "ACTIVATE_SEGMENTS", "op": "ADD", "path": "/imp/1/product_matches", "value": { "ids": ["NIKE-AIR-MAX-90", "ASICS-GEL-1130"], "details": [ { "id": "NIKE-AIR-MAX-90", "price": 3200, "stock": 47 }, { "id": "ASICS-GEL-1130", "price": 2980, "stock": 23 } ] } } // Step 4 — Dynamic pricing with loyalty discount { "intent": "BID_SHADE", "path": "/seatbid/1/bid/1/price", "value": { "price": 2890, "original": 3200, "discount_pct": "9.7%", "reason": "loyalty_discount + volume_tier_2" } } // Step 5 — Verifiable trust signals { "intent": "ADD_METRICS", "path": "/seatbid/1/bid/1/trust", "value": { "trust_score": 94, "fulfillment_rate": 0.97, "return_rate": 0.03, "avg_delivery_days": 2.1 } } // Step 6 — Deal finalization { "intent": "ACTIVATE_DEALS", "path": "/imp/1/pmp/deals", "value": { "deal_id": "deal-agent-a-nike-air-max-90", "product": "Nike Air Max 90", "final_price": 2890, "saved": 310, "trust": 94, "eta": "2 days" } }
Interactive simulation of a complete ARTF transaction. The left panel shows the
Orchestrator's execution log; the right panel displays the raw Mutation
payloads at each step.
Target latency budget and observed metrics from the reference implementation. Values update live during the interactive demo above.
| Metric | Target | Observed (PoC) | Notes |
|---|---|---|---|
| Total latency | < 100ms |
92ms |
Includes all 6 steps, single agent path |
| Orchestrator overhead | < 20ms |
~18ms |
Filter + dispatch + merge |
| Agent response (GetMutations) | < 60ms |
~43ms |
3 mutations returned in single call |
| Trust verification | < 15ms |
~12ms |
On-chain lookup + score validation |
Areas for expansion beyond this proof of concept:
GetMutations calls
to N agents with ranked merge of competing offersCOUNTER_BID mutations, enabling multi-round negotiationADD_METRICS data
to an on-chain registry for cross-exchange portabilitytmax, should the Orchestrator fall back to the next-ranked agent or
return a partial result? Current PoC uses a strict timeout with no fallback.
| # | Reference | Relevance |
|---|---|---|
| [1] | IAB Tech Lab. OpenRTB API Specification v2.6, 2022. | Core protocol inspiration — bid request/response objects, auction semantics, tmax budgets |
| [2] | IAB Tech Lab. OpenRTB Specification v3.0, 2020. | Layered architecture separating transaction from domain concerns |
| [3] | P. Bryan, K. Zyp, M. Nottingham. RFC 6901: JSON Pointer, IETF, 2013. | Path syntax used in Mutation path field for targeting JSON document locations |
| [4] | P. Bryan, M. Nottingham. RFC 6902: JSON Patch, IETF, 2013. | Mutation operation model (add, replace, remove) derived from JSON Patch semantics |
| [5] | OpenAI & Stripe. Agentic Commerce Protocol (ACP), 2025. | Open standard for AI agent discovery, negotiation, and purchase across merchants |
| [6] | Stripe. Developing an Open Standard for Agentic Commerce, 2025. | ACP design principles: agent discovery, merchant control, payment-processor agnosticism |
| [7] | McKinsey & Company. The Agentic Commerce Opportunity, 2025. | Market projection: $3–5T in AI-agent-mediated global consumer commerce by 2030 |
| [8] | Microsoft Research. Magentic Marketplace: An Open-Source Environment for Studying Agentic Markets, 2025. | Simulated multi-agent marketplace covering search, matching, negotiation, and transaction |
| [9] | Google Research / DeepMind / MIT. Towards a Science of Scaling Agent Systems, 2025. | Quantitative scaling principles for multi-agent LLM systems |
| [10] | Fujita et al. ANAC 2024 Challenges and Results, AAMAS 2025. | Benchmarks for automated negotiation strategies in bilateral and supply-chain settings |
| [11] | S. Kamvar, M. Schlosser, H. Garcia-Molina. The EigenTrust Algorithm for Reputation Management in P2P Networks, Stanford, 2003. | Foundational algorithm for decentralized trust — canonical reference for the Trust Layer |
| [12] | ACM Computing Surveys. Privacy-Preserving Reputation Systems Based on Blockchain, 2022. | Blockchain-based reputation with privacy guarantees and zero-knowledge proofs |
| [13] | W. Zhang et al. A Survey on Bid Optimization in Real-Time Bidding Display Advertising, ACM TKDD, 2024. | RTB optimization techniques: bidding strategies, budget pacing, auction dynamics |
| [14] | Z. Wang et al. RTBAgent: A LLM-based Agent System for Real-Time Bidding, 2025. | LLM agents applied to RTB decision-making — directly relevant to ARTF’s approach |