cs.AIOct 25, 2026
SaveAgentic AI Orchestration Framework for Spatial Intelligence: Supply Chain Deployment
Organizations: Anassa.ai USA · Research Scientist USA
Abstract
Industrial spatial decisions are assembled across systems that share neither a reference frame nor an owner, and analysts spend substantial effort reconciling those systems rather than reasoning about space. This paper presents ANASSA, a reference architecture integrating structured reasoning, multi-agent workflow orchestration, authoritative spatial grounding, and feedback-driven execution, and reports its instantiation in an operating supply-chain deployment. The architecture places typed data contracts at agent boundaries, validates spatial claims against a certified authoritative foundation, and enforces two blocking human-in-the-loop approvals inside a six-step cognitive loop. The deployment covers multi-hazard risk assessment on an ocean-and-inland freight lane, including cold-chain excursion detection over container telemetry. Across a three-month proof of concept (POC) with three implementation iterations, three deployment outcomes are reported: 25% improvement in data quality, 50% reduction in spatial workflow runtime, and 60% lower decision latency relative to the preceding manual workflow. Absolute deployment-scale quantities and baseline counts are confidential; reported percentages are observed relative changes. These are uncontrolled deployment observations, not controlled experiments. We therefore specify instrumentation and ablation protocols for mechanism-level evaluation.
Figures & tables
| System | Dec. | Contracts | Ground. | Gov. | Eval. |
|---|---|---|---|---|---|
| GeoAgent ( Hu et al., 2025 ) | Y | N | P | N | bench. |
| GeoFlow ( Bhattaram et al., 2025 ) | Y | P | N | N | bench. |
| Spatial-Agent ( Bao et al., 2026 ) | Y | P | P | N | bench. |
| GeoAgentic-RAG ( Liang et al., 2026 ) | Y | N | P | N | bench. |
| Mansourian ( Mansourian and Oucheikh, 2026 ) | Y | N | N | N | bench. |
| GeoBenchX ( Krechetova and Kochedykov, 2025 ) | – | – | reject | – | bench. |
Table 1. Properties explicitly specified in cited systems. Y = explicitly specified; P = described but not operationally specified; N = not specified; – = not applicable to the benchmark purpose.
Figure 1. Types of GIS workflows. Comparison of manual GIS, LLM-augmented GIS, and agentic GIS workflows, emphasizing the transition from analyst-coordinated steps to a closed agentic reasoning and execution loop.
| Layer | Components | Function |
|---|---|---|
| Governance/Output | C7,C10 | Validates outputs and governs agents/workflows |
| Operational | C1–C4,C6 | Participants, sectors, deployment, system-of-systems, spatial data |
| Cognitive Engine | C5 | Continuous sensing, reasoning, planning, decision, execution, learning |
| Foundation | C8,C9,C11 | Trusted data/knowledge and process-flow connections |
Table 2. Architectural layers and components.
Figure 2. ANASSA high-level architecture. ANASSA reference architecture showing eleven components across governance and output, operational, cognitive engine, and foundation layers, with downward and upward process flows.
| Step | Agent | Function | HITL |
|---|---|---|---|
| S1 | Sensing | Receives prepared data from C6, detects spatial events and anomalies warranting cognitive processing, and reports status to the Orchestrator | No |
| S2 | Reasoning | Applies spatial intelligence to establish context and pattern, and selects models, tools, and workflows | No |
| S3 | Planning | Generates geospatial workflows and solution strategies aligned to participant needs and industry requirements (C1, C2) | Blocking |
| S4 | Decision/validation | Evaluates options and validates outputs against authoritative ground truth, checking every spatial claim against C8 | Blocking |
| S5 | Execution | Deploys through C3 infrastructure, publishes maps and dashboards, triggers downstream workflows, and notifies stakeholders | No |
| S6 | Learning | Captures outcomes and feeds improvements to all components, updating Orchestrator coordination rules | No |
Table 3. Geospatial AI Cognitive Loop (C5).
| Level | Agent | Origin | Function | Validation condition |
|---|---|---|---|---|
| A0 | ANASSA Orchestrator | Platform | Decomposes intent into a task graph, sequences agents, and enforces governance | Every node declares an input contract; an unresolved node blocks rather than proceeds |
| A1 | Cold-Chain Telemetry (C5-S1) | Platform | Ingests reefer and container telemetry from C6 | Bundle rejected if CRS is absent; a series gap is a monitoring failure, never interpolated |
| A1 | Learning (C5-S6) | Platform | Updates preference models, parameters, and agent capabilities each cycle | Every update carries a cycle identifier and lists changed parameters |
| A2 | Administration | Org role | Maintains organizational, contractual, and jurisdictional context | A consignment must resolve to a legal owner before downstream agents act |
| A2 | Supply Origin | Org role | Resolves supplier facility geometry and dispatch readiness | Facility matched to a certified C8 address point, or the claim is unverified |
| A2 | Port State | Org role | Maintains terminal state at load and discharge ports | Terminal geometry from certified C8 assertions, not carrier free text |
Table 4. Supply-chain agent roles and validation conditions.
| Boundary | Payload | Precondition | Failure mode prevented | |
|---|---|---|---|---|
| K1 | C6 C8 | Telemetry and hazard writes to the foundation | Certification level assigned at write; storage version recorded | Uncertified data indistinguishable from certified |
| K2 | C6 C5-S1 | Telemetry bundle for sensing | Explicit CRS on every bundle; gaps flagged as monitoring failures | A missing window smoothed into a fictitious history |
| K3 | C5-S2 C5-S3 | Candidate workflow as a task graph | Each node declares tool, parameters, and expected output type | Inapplicable tool selection propagating undetected |
| K4 | HITL gate at S3 | Plan approval | Blocking; approver identity and timestamp recorded | Execution of a plan no accountable human reviewed |
| K5 | C8 C5-S4 | Grounding assertion set | Each claim matched to a certified assertion or listed as unverified | Fabricated coordinates published as analysis |
| K6 | HITL gate at S4 | Decision approval | Blocking; rejection returns to S3 with a reason code | Unaccountable publication of a high-stakes output |
Table 5. Supply-chain data contracts.
| ID | Outcome | Measurement basis | Change |
|---|---|---|---|
| D1 | Data quality | Published claims lacking supporting references; human-reviewed | 25% lower rate |
| D2 | Runtime | Wall-clock time per multi-hazard assessment | 50% lower |
| D3 | Decision latency | Objective to approved decision; gates included | 60% lower |
Table 6. Deployment outcomes against the manual baseline.
| ID | Instrument | Definition | RQ |
|---|---|---|---|
| I1 | Analyst time | Analyst-attributable wall-clock time, objective to approved decision | RQ1 |
| I2 | Systems touched | Distinct systems analyst accesses directly | RQ1 |
| I3 | Contract violations | Failed preconditions per thousand contract firings | RQ1,RQ2 |
| I4 | Spatial-reference errors | Outputs with absent/incorrect CRS or datum | RQ2 |
| I5 | Unsupported claims | Published claims lacking matching certified assertion | RQ2 |
| I6 | Unverified-claim list | Claims routed to human review per analysis | RQ2,RQ4 |
Table 7. Instrumentation mapped to research questions.
| RQ | Mechanism under test | Evidence in this paper | Status |
|---|---|---|---|
| RQ1 Coordination | Typed contracts K1–K3; Orchestrator task-graph sequencing | D2 (50% workflow runtime reduction) and D3 (60% lower decision latency) against the manual baseline | Partially supported. Uncontrolled and confounded; I1 and I2 were not separately recorded |
| RQ2 Grounding | K2 CRS and gap enforcement; K5 claim validation; Claim Validation Agent | D1 (25% data-quality improvement) attributed to interception of low-confidence claims | Partially supported. The composite quality measure is not decomposed into I4, I5, and I7, so the earlier-detection claim is argued rather than measured |
| RQ3 Fusion and reproducibility | C8 foundation; K7 versioned provenance; spatio-temporal join behind typed contracts | Section 4 walkthrough; version-recorded indexes support audit re-issue by construction | Illustrated only. No quantitative fusion or determinism measure (I8, I9) was recorded |
| RQ4 Governance under machine pace | K4 and K6 blocking gates | D3 measured from objective to approved decision, with gate cost inside the interval | Partially supported. The gates did not eliminate the latency advantage, but I11 and I12 were not captured, so the cost of the gates is not isolated |
Table 8. Evidence obtained against each research question.