GNOSTRA Project Roadmap

GNOSTRA is a complete, multi-stage autonomous kernel. Our development is structured into four milestones, all of which are now structurally complete and operational for the closed beta.

M0: The Kernel

COMPLETE
  • Goal: Prove the core logic.
  • Status: COMPLETE.

Key Features:

  • NeedIndex: The 12 Pillars of Need objective function.
  • CP-SAT Allocator: The core solver that generates optimal plans.
  • Trust Gate: The validator, attestation, and privacy services (M0.3).

M1: The Distributed Backbone

COMPLETE
  • Goal: Scale the kernel into a persistent, distributed network.
  • Status: COMPLETE.

Key Features:

  • Data Plane (M1.1): Transitioned to a persistent Neo4j Knowledge Graph and NATS Event Bus.
  • Orchestrator (M1.2): Autonomous, event-driven service (gnostra_orchestrator) that dispatches plans via gRPC.
  • Mock Adapter: A functional gRPC agent that can receive and acknowledge tasks.

M2: The Trust & HMI Layer

COMPLETE
  • Goal: Make the network verifiable, auditable, and human-usable.
  • Status: COMPLETE.

Key Features:

  • PII Vaulting (M2.1): PII is no longer redacted, but replaced with verifiable cryptographic hashes.
  • Verifiable Planning (M2.2): The Allocator cryptographically signs every AllocationPlan.
  • GNOSTRA Qt Client (M2.3): The complete HMI, featuring the Bottleneck Map, Pillar Gauges, Plan Explainer, and Data Submission forms.

M3: The Learning Loop

COMPLETE
  • Goal: Make the system intelligent and autonomous.
  • Status: COMPLETE.

Key Features:

  • Simulator (M3.1): The "Digital Twin" that generates synthetic crisis scenarios.
  • Learner (M3.2): The Epsilon-Greedy (MAB) agent that observes simulations and learns optimal policies.
  • Evaluator (M3.3): The audit layer that runs counterfactual simulations to calculate "regret" and verify the Learner's intelligence.

M4: Post-Beta & Future Operations

IN PROGRESS
  • Goal: Harden the network for production and expand the learning model.
  • Status: IN PROGRESS.

Next Steps:

  • Deployment Packaging: Finalize the docker-compose environment for testers.
  • Security Hardening: Implement full gRPC authentication and rotate all development keys.
  • M3.3 Integration: Feed the "Regret Metric" from the Evaluator back into the Learner's decision model.
  • Policy Expansion: Expand the Learner's "policy knobs" from one (w_strictness) to dozens (e.g., cost, speed, priority weights).