P213 — AIEP — Resource Allocation and Compute Governance Engine
Applicant: Neil Grassby Classification: Patent Application — Confidential Priority: Claims priority from GB2519711.2 filed 20 November 2025 Architecture Layer: AIEP AGI Cognition Layer — Phase 2
Framework Context
[0001] This specification operates within an AIEP environment as defined in GB2519711.2 and GB2519798.9. The present specification defines the resource allocation and compute governance mechanism for Phase-2 AIEP multi-agent cognition environments, ensuring that reasoning processes operate within defined compute budgets and that resource allocations are governed, auditable, and evidence-bound.
Field of the Invention
[0002] The present invention relates to compute resource governance architectures for evidence-bound artificial intelligence systems.
[0003] More particularly, the invention relates to a system for allocating compute resources to concurrent reasoning agents and workflow steps in accordance with governance policy constraints, tracking resource consumption against allocated budgets, and terminating or throttling processes that exceed their allocations.
Background
[0004] Multi-agent AI systems operating on shared compute infrastructure must allocate resources equitably and in accordance with priority policies. Without a governed resource allocation mechanism, high-priority tasks may be starved by lower-priority processes, or the total compute budget may be exhausted, causing system-wide reasoning failures.
Summary of the Invention
[0005] The invention provides a Resource Allocation and Compute Governance Engine (RACGE) that: receives resource allocation requests from reasoning agents and workflow steps; evaluates requests against the current compute budget and governance policy; allocates resources within approved bounds; monitors actual consumption against allocated budgets; and throttles or terminates processes that exceed allocations.
[0006] Resource allocations are recorded as evidence artefacts, enabling post-hoc audit of compute consumption patterns and supporting the Predictive Resource Planning Engine (P226) with historical consumption data.
ASCII Architecture
Reasoning Agent / Workflow Step
Resource Allocation Request
|
v
+------------------------------------------+
| Resource Allocation & Compute Governance |
| |
| Budget Registry: |
| Total_Budget: 1000 units |
| Allocated: 640 units |
| Available: 360 units |
| |
| Allocation Decision: |
| approve / partial / deny |
+-------------------+----------------------+
|
+--------+---------+
| |
v v
Allocation Grant Throttle / Terminate
(token returned to (overage policy
requester) enforcement)
|
v
Allocation Record
(evidence artefact
admitted to ledger)
|
v
Predictive Resource
Planning (P226)
(historical data feed)
Definitions
[0007] Resource Allocation and Compute Governance Engine (RACGE): The subsystem that receives compute resource requests from reasoning agents and workflow steps, evaluates requests against available budget and governance policy, and manages active allocation tokens.
[0008] Allocation Token: A governance-issued authorisation record granting a specified compute resource quantity to a named requester for a bounded time period, serving as the exclusive mechanism by which processes may consume governed compute resources.
[0009] Budget Registry: A real-time record of total compute budget, currently allocated quantity, and available quantity, updated atomically on each allocation grant or release event.
[0010] Overage Condition: A state in which an active process’s measured resource consumption exceeds its allocated token quantity.
[0011] Allocation Record: An evidence artefact recording each allocation decision, comprising requester identifier, request quantity, approved quantity, timestamp, governance policy version hash, and decision reason code.
Detailed Description
Resource Request Processing. [0012] The RACGE exposes a resource allocation interface accepting allocation requests from any Phase-2 reasoning agent or workflow step. Each request specifies: the requester identifier; the resource category (compute, memory, or network); the requested quantity; the expected duration; and the priority classification assigned by the Goal Formation Engine (P210) GoalVector. The RACGE evaluates the request against the budget registry and the active governance policy simultaneously.
Allocation Decision Logic. [0013] The RACGE applies a three-outcome decision logic. APPROVE: if the requested quantity is available within the current budget and governance policy permits the allocation for the requester’s priority class, a full allocation token is issued. PARTIAL: if the full quantity is unavailable but a reduced quantity sufficient for minimum viable operation is available, a partial token is issued with the approved quantity and a partial-grant flag. DENY: if the budget is exhausted or the governance policy specifically prohibits allocation to the requester’s class (for example, a suspended agent), a denial record is issued with a reason code. Partial and denied requests are retried after a policy-defined back-off interval.
Consumption Monitoring. [0014] For each active allocation token, the RACGE maintains a consumption monitor that samples actual resource usage at a policy-defined interval. If a process’s measured consumption exceeds its token quantity (overage condition), the RACGE applies a graduated response: first, a throttle instruction is sent to the consuming process, reducing its permitted consumption rate; if the overage persists beyond a policy-defined tolerance window, the process is terminated and a compute governance violation artefact is admitted to the evidence ledger. Legitimate computation interrupted by termination is flagged for goal re-scheduling.
Allocation Record Admission. [0015] Every allocation decision — approve, partial, or deny — generates an immutable allocation record admitted to the AIEP evidence ledger. Allocation records serve dual purposes: governance audit (demonstrating that resource consumption did not exceed policy) and historical data feed for the Predictive Resource Planning Engine (P226), which uses the allocation record stream to model future demand and recommend budget adjustments.
Token Release and Budget Reconciliation. [0016] When a process completes or is terminated, it releases its allocation token. The RACGE validates that the released token matches an active grant record, updates the budget registry atomically, and emits a completion entry appended to the original allocation record. Budget reconciliation is performed at the end of each reasoning session, verifying that total allocations issued equal total tokens released, with any discrepancy recorded as a reconciliation exception artefact.
Technical Effect
[0017] The invention provides governed, auditable compute resource allocation for multi-agent AI reasoning environments. By requiring explicit allocation tokens before resource consumption and recording all allocation decisions as evidence artefacts, the system prevents uncontrolled resource exhaustion and provides a complete audit trail of compute governance. By monitoring active consumption against tokens and enforcing overage policy, the system maintains budget integrity across concurrent reasoning processes.
Claims
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A computer-implemented method for resource allocation and compute governance, the method comprising: (a) receiving resource allocation requests specifying requester identifier, resource category, requested quantity, expected duration, and priority classification; (b) evaluating each request against a budget registry and active governance policy to produce a three-outcome decision: APPROVE, PARTIAL, or DENY; (c) issuing allocation tokens for approved and partial grants, and admitting allocation records to the AIEP evidence ledger for all decision outcomes; (d) monitoring active token consumption at a policy-defined sampling interval, applying throttle instructions on overage detection and terminating processes that exceed overage tolerance; and (e) validating token releases, updating the budget registry atomically, and performing end-of-session budget reconciliation with discrepancy exception recording.
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The method of claim 1, wherein partial-grant tokens carry an approved-quantity and a partial-grant flag, and are automatically retried after a policy-defined back-off interval.
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The method of claim 1, wherein process termination under overage policy generates a compute governance violation artefact admitted to the evidence ledger with the consuming process identifier and measured excess quantity.
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The method of claim 1, wherein allocation records are fed as historical data to a Predictive Resource Planning Engine for future demand modelling.
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The method of claim 1, wherein the budget registry is updated atomically on each allocation grant and token release, preventing concurrent inconsistency between total, allocated, and available budget figures.
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A Resource Allocation and Compute Governance Engine comprising: one or more processors; memory storing a budget registry, active token store, and allocation record buffer; wherein the processors are configured to execute the method of claim 1.
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A non-transitory computer-readable medium storing instructions that, when executed by a processor, implement the method of claim 1.
Abstract
A resource allocation and compute governance engine for multi-agent artificial intelligence systems receives compute resource requests, evaluates them against a budget registry and governance policy, and issues allocation tokens with a three-outcome decision model. Active consumption is monitored against token quantities, with graduated throttle and termination enforcement on overage. All allocation decisions are recorded as immutable evidence artefacts, providing governance audit coverage and historical data for predictive resource planning. | v Allocation Record (evidence artefact) + Real-Time Consumption Monitor
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## Detailed Description
[0007] **Budget Hierarchy.** The resource budget is structured as a three-level hierarchy: a system-level budget (total available compute per cycle); agent-level allocations (per-agent quotas based on priority tier); and task-level allocations (per-task sub-allocations within agent quota). Allocations at each level must not exceed the parent level's remaining balance.
[0008] **Allocation Decision Protocol.** When a resource request arrives, the RACGE checks: whether the requested amount is within the system budget; whether the requesting agent has remaining quota; and whether the governance policy permits this resource type to be allocated to this agent class. Approved requests are recorded and the allocation deducted.
[0009] **Consumption Monitoring.** The RACGE monitors actual resource consumption against allocations in real time. When an agent or task consumes its entire allocation, the RACGE issues a termination signal unless an extension request is submitted and approved. Extension requests are governed by the same approval protocol.
[0010] **Audit Trail.** All allocation records, consumption measurements, and termination events are admitted as evidence artefacts to the AIEP ledger, enabling governance audit of compute usage. The Predictive Resource Planning Engine (P226) uses these records to forecast future demand.
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## Claims
1. A compute resource governance engine for an evidence-bound reasoning architecture wherein resource allocations are recorded as evidence artefacts.
2. The system of claim 1 wherein allocations are structured in a three-level hierarchy: system, agent, and task.
3. The system of claim 1 wherein governance policy constrains resource type eligibility by agent class.
4. The system of claim 1 wherein consumption monitoring triggers termination signals on allocation exhaustion.
5. The system of claim 1 wherein allocation records support historical consumption analysis for future planning.