Systems and Methods for Agent-Based Orchestration Using Transactional Grammar, Temporal Logic, and Dynamic Coordination Graphs
Inventor: Robb Bush
Assignee: INDUSTRY 5, Inc.
Application Type: U.S. Provisional Patent Application # 63/812,498
Filing Date: May 27, 2025
Abstract
A multi-agent orchestration system that enables distributed decision-making across enterprise operations. The system incorporates:
- a structured transactional grammar for encoding and interpreting operational intents,
- a causal temporal reasoning framework for evaluating execution constraints, and
- a dynamic coordination graph that agents use to simulate, negotiate, and allocate resources in real time.
This architecture supports schema-agnostic data ingestion, continuous re-prioritization, and context-aware coordination—allowing agents to perform decentralized, live orchestration across supply chains, logistics, and production systems.
Field of the Invention
This invention relates to the field of artificial intelligence and distributed systems, specifically to architectures enabling real-time, multi-agent orchestration across heterogeneous enterprise data sources and operational environments.
Background
Summary
Traditional enterprise systems rely on centralized planning, rule-based automation, or retrospective analytics. They often lack the ability to operate in real time, adapt to uncertainty, or reason over timing and constraint interdependencies. Existing solutions such as ERP systems, advanced planning software, and digital twins either require schema alignment, manual configuration, or lack agent autonomy.
Cognitive architectures (e.g., ACT-R, SOAR) exist in research settings but are not directly applied to dynamic operational orchestration. Similarly, existing planning solutions (e.g., SAP IBP, Blue Yonder, o9) do not use agent-based simulation with embedded temporal reasoning and live coordination graphs.
Details
Most enterprise planning and execution systems are built around centralized, deterministic workflows. These systems are optimized for environments where data is complete, processes are linear, and plans can be executed as written. However, in modern operational environments—characterized by volatility, interdependence, and ambiguity—this approach is increasingly inadequate.
Traditional solutions such as:
- ERP suites (e.g., SAP, Oracle),
- Advanced Planning Systems (e.g., Blue Yonder, Kinaxis, o9),
- and workflow automation tools
rely on batch-based planning, manual overrides, or rule-based automation, which are often fragile in the face of real-world complexity. These systems typically assume:
- That data is trustworthy and current,
- That execution timelines are static or predictable,
- That human operators will resolve exceptions manually.
Even modern enhancements, including AI/ML-powered forecasts and digital twins, tend to reinforce centralized paradigms. They optimize static plans but do not enable live coordination or distributed decision-making.
Furthermore, these systems are often limited by:
- Rigid data schemas, requiring complex ETL or master data alignment.
- Flat time modeling, lacking the ability to reason over causal or nested constraints.
- Siloed optimization, where planning, execution, and logistics operate independently, requiring human intervention for reconciliation.
In contrast, multi-agent systems, constraint-based programming, and temporal AI planning frameworks have shown promise in academic and simulation contexts—but have not been effectively applied to live enterprise coordination at scale, due to challenges around performance, trust, and data heterogeneity.
There exists a need for a system that:
- Enables autonomous coordination across diverse enterprise functions,
- Is capable of reasoning under uncertainty, time pressure, and partial information,
- And supports live adaptation and negotiation as a native part of its operational model.
The present invention addresses these limitations by introducing a distributed orchestration architecture, in which autonomous agents reason over structured operational grammar, evaluate timing constraints using causal logic, and coordinate via a live negotiation graph. This approach allows for real-time, resilient decision-making across systems, teams, and timelines.
Description of the Invention
The invention includes:
- A transactional grammar layer that ingests and transforms diverse operational data into structured, intent-based objects representing product, quantity, location, and time.
- A temporal logic layer that encodes each object with causal constraints, execution windows, and priority rules derived from agent context and system state.
- A negotiation graph structure that models possible flows, commitments, and reallocations across agents. Each agent can simulate paths through the graph, propose actions, and commit changes based on evolving operational logic.
Agents operate asynchronously, using these three structures to evaluate trade-offs, coordinate with peers, and update shared state—without centralized control.