---
title: Decentralized PlanExe Survivability Network
date: 2026-02-11
status: proposal
author: PlanExe Team
---

# Decentralized PlanExe Survivability Network

**Author:** PlanExe Team  
**Date:** 2026-02-11  
**Status:** Proposal  
**Audience:** Infrastructure Architects, Security Leads, Ecosystem Partners

---

## Pitch
Build a decentralized PlanExe that keeps planning and verification online even if a government shuts down the primary site, disables a datacenter, or blocks a payment processor. Users with local LLM hardware can offer compute and get paid.

## Why
Centralized infrastructure is fragile. A single takedown or payment outage can halt planning, which is unacceptable for cross-border or politically sensitive users. Decentralization improves resiliency and trust.

## Problem

- A single website or cloud region is a single point of failure.
- Model access can be disrupted by datacenter shutdowns.
- Stripe or centralized billing can be blocked or throttled.
- Users with capable hardware cannot currently contribute compute.

## Proposed Solution
Create a **PlanExe Survivability Network** with three layers:

1. **Distributed Execution Mesh**: many independent nodes can run the PlanExe pipeline.
2. **Decentralized Discovery + Routing**: users can find healthy nodes even if the primary domain is down.
3. **Compute Marketplace**: operators provide LLM compute and are paid for verified work.

## Architecture

```text
Client
  -> Node Directory (multiple mirrors)
  -> Execution Mesh (trusted + community nodes)
  -> Result Verification + Audit Log
  -> Payment Settlement (multi-rail)
```

## Key Components

### 1) Execution Mesh

- Nodes run a containerized PlanExe runtime.
- Each node advertises capabilities (models, speed, cost).
- Tasks are routed based on availability, trust, and price.

### 2) Decentralized Discovery

- Multiple directory endpoints (geo-distributed).
- Client can fall back to cached node lists.
- Signed node manifests prevent spoofing.

### 3) Verification Layer

- Outputs are signed by node identity.
- Random re-execution and consensus checks detect bad actors.
- Evidence coverage and confidence thresholds enforced.

### 4) Multi-Rail Payments

Support multiple settlement paths:

- Traditional (credit card or bank transfer)
- Crypto/stablecoin payments
- Voucher or prepaid credit (offline distribution)

## Compute Marketplace

### Node Enrollment

- Operators register node identity and specs.
- Capability tests and benchmarks determine pricing tier.

### Payment Model

- Pay-per-task or pay-per-token.
- Bonus for high reliability and fast turnaround.
- Penalties for failed or unverifiable outputs.

## Output Schema

```json
{
  "node_id": "node_882",
  "capabilities": ["llm", "verification"],
  "price_per_1k_tokens": 0.02,
  "trust_score": 0.91,
  "availability": "high"
}
```

## Security and Governance

- Signed tasks and signed outputs.
- Quarantine low-trust nodes.
- Dispute resolution for payment and output quality.
- Transparent audit logs for all executed tasks.

## Integration Points

- Works with existing PlanExe MCP interface.
- Feeds into evidence ledger and readiness scoring.
- Uses benchmarking harness for node qualification.

## Success Metrics

- % of planning requests that survive primary site outage.
- Median time to route tasks during failure.
- Growth of independent compute nodes.
- Reduction in payment single-point-of-failure incidents.

## Risks

- Malicious or low-quality nodes.
- Fragmentation of standards across nodes.
- Regulatory exposure for cross-border payments.

## Mitigations for Fragmentation and Knowledge Drift

Decentralized nodes can diverge in schemas, prompts, and verification standards. Without coordination, outputs become incompatible and trust collapses. The network should enforce **shared standards** and **shared knowledge sync**.

### 1) Standards Versioning

- Publish a canonical schema bundle with strict versioning (e.g., `planexe-schema@1.4.0`).
- Require nodes to advertise supported versions.
- Reject outputs from incompatible versions unless downgraded to a common target.

### 2) Shared Knowledge Sync (IPFS + Redundancy)

- Publish core artifacts (schemas, benchmark sets, prompt templates, policy rules) to IPFS.
- Use content hashes as immutable identifiers for verification.
- Maintain multiple pinning nodes for redundancy and censorship resistance.

### 3) Consensus on Critical Artifacts

- Require quorum approval for changes to high-impact artifacts (risk gates, verification rules).
- Distribute signed release manifests (multi-sig) to prevent unilateral drift.

### 4) Compatibility Tests

- Nodes periodically run a compatibility test suite.
- Failing nodes are downgraded or quarantined until updated.

### 5) Cached Offline Mirrors

- Clients keep a cached copy of the most recent release manifest.
- If the network is partitioned, nodes can still operate on the last known standard.

## Mitigations for Malicious or Low-Quality Nodes

Decentralized execution requires explicit safeguards against bad actors and unreliable hardware. Mitigations should combine **pre-qualification**, **runtime verification**, and **economic incentives**.

### 1) Pre-Qualification Gates

- Benchmark nodes on standardized test suites before admitting them.
- Require signed attestations for hardware and model versions.
- Assign an initial low trust tier until performance is proven.

### 2) Runtime Verification

- Randomly re-execute a fraction of tasks on a trusted node and compare outputs.
- Cross-check outputs with schema validators and evidence coverage tests.
- Reject results that deviate beyond defined tolerance bands.

### 3) Reputation and Trust Scores

- Track per-node success rates, latency, and error frequency.
- Penalize nodes for invalid outputs or unverified evidence.
- Promote nodes to higher tiers only after sustained accuracy.

### 4) Economic Incentives and Penalties

- Require a stake or deposit for node participation.
- Slash rewards for failed or fraudulent outputs.
- Pay bonuses for high reliability and verified accuracy.

### 5) Quarantine and Revocation

- Auto-quarantine nodes that exceed failure thresholds.
- Allow manual review and appeal for edge cases.
- Publish revocation lists to prevent re-entry under the same identity.

## Future Enhancements

- Peer-to-peer plan replication and caching.
- Federated governance council for node standards.
- Automated multi-provider model routing.

## Detailed Implementation Plan

### Phase A — Survivability Threat Model

1. Define failure scenarios:
   - cloud provider outage
   - network partition
   - key personnel loss
   - service-level legal/regulatory disruption
2. Map critical capabilities and single points of failure.

### Phase B — Decentralized Runtime Strategy

1. Define federated node architecture with regional failover.
2. Replicate critical state using signed append-only logs.
3. Implement degraded-mode operations for partial outages.

### Phase C — Recovery and Continuity Playbooks

1. Add automated failover orchestration and health probes.
2. Add disaster recovery drills and RTO/RPO targets.
3. Publish continuity runbooks and command paths.

### Phase D — Governance and Trust

1. Define cross-node trust and key rotation policies.
2. Add tamper-evident audit synchronization.
3. Add survivability scorecard for quarterly reviews.

### Validation Checklist

- Recovery time objective achievement
- State consistency after failover
- Degraded-mode service availability under stress tests

