Core Data Model
This page describes the primary entities in BASTION and their relationships.
Problem Sets
Problem sets (formerly called workspaces) are the top-level organizational unit for all planning activity. Each problem set scopes a strategic or operational problem and contains all associated documents, objectives, COAs, and AI agent outputs.
| Field | Description |
|---|---|
id | Unique identifier |
name | Descriptive title (e.g., "Indo-Pacific Contingency AY26") |
echelon | Command echelon level (planned: strategic environment inheritance from parent to child) |
mode | Training or operational |
created_at | Timestamp |
Planned: Echelon-aware inheritance will allow parent problem sets to push strategic context (objectives, constraints, assumptions) down to subordinate problem sets, mirroring how higher headquarters frames lower-echelon planning.
Documents and Strategic Intelligence
Documents are ingested into the platform and processed through an NLP pipeline that extracts entities, relationships, and key themes for RAFT graph construction.
| Field | Description |
|---|---|
id | Unique identifier |
problem_set_id | Owning problem set |
title | Document title |
content | Full text or IPFS reference for large files |
doc_type | Category (intelligence report, OPORD, OPLAN, policy, etc.) |
raft_processed | Whether RAFT extraction has completed |
Extracted entities and relationships are stored in Neo4j, linked back to source documents for provenance tracking.
Objectives, COAs, and Planning Products
Objectives
Objectives represent desired end states or conditions, linked to lines of effort and centers of gravity from the operational design.
Courses of Action (COAs)
COAs are developed by AI staff agents and human planners during the Plan phase. Each COA includes:
- Scheme of maneuver or scheme of action
- Task organization
- Risk assessment
- Decision points and branches/sequels
- Wargaming results and comparison scores
Planning Products
Outputs such as OPORDs, FRAGOs, synchronization matrices, and decision support templates are generated collaboratively by AI agents and human staff.
Resources
Resources represent personnel, equipment, units, and capabilities tracked through the platform. Each resource receives a decentralized identifier for cross-system interoperability.
| Field | Description |
|---|---|
id | Internal identifier |
did | Decentralized identifier (did:near:resource-{id}) |
name | Resource name |
type | Plugin-defined type (unit, equipment, capability, facility, etc.) |
attributes | JSON attributes specific to the resource type |
status | Availability and readiness state |
The plugin architecture allows new resource types to be defined without schema changes. Each plugin registers its type, validation rules, and UI components.
AI Agents and Teams
BASTION deploys 131 AI agents organized into two categories:
| Category | Count | Description |
|---|---|---|
| Specialized agents | 31 | Purpose-built for specific capabilities (COP generation, wargaming, assessment, etc.) |
| JPP staff agents | 102 | Mapped to Joint Planning Process staff roles across J1-J9 directorates |
Each agent record includes:
- Role: Staff function and doctrinal responsibility
- Model config: LLM provider, model name, temperature, and token limits
- Tools: Available function-calling tools (database queries, graph lookups, document retrieval)
- Team: Staff section assignment (J1 Personnel, J2 Intelligence, J3 Operations, etc.)
Agents are registered in a LangGraph registry and invoked through workflow graphs that coordinate multi-agent collaboration.
DAO Proposals and Governance
On-chain governance manages planning decisions through a proposal lifecycle:
Draft -> Submitted -> Voting -> Approved/Rejected -> Executed
| Field | Description |
|---|---|
id | Proposal identifier |
authority_tier | Required approval tier (1-5) |
proposer | NEAR account of the proposing user or agent |
action | What the proposal authorizes (approve COA, allocate resources, etc.) |
quorum | Minimum votes required, varies by tier and coalition composition |
votes_for / votes_against | Current tally |
status | Current lifecycle state |
Coalition governance adds cross-organization voting where partner nations or agencies hold weighted votes based on contribution and stake.
Exercise Scenarios
Scenarios structure training exercises with phased progression:
| Field | Description |
|---|---|
id | Scenario identifier |
name | Exercise name (e.g., "Pacific Strategy AY26") |
phases | Ordered list of scenario phases |
current_phase | Active phase for the exercise |
Each phase defines the operational context, injects, and conditions that drive planning activity. The Pacific Strategy AY26 scenario, for example, includes six phases: Competition, Crisis, Conflict Day 4, Conflict Day 10, Conflict Day 22, and Negotiation.