Platform Architecture

Development

Build on the infrastructure powering autonomous synthetic workforces.

01
Core Infrastructure

System Architecture

The Mirathis Engine is a four-layer runtime for coordinating autonomous AI employees at organizational scale.

╔═════════════════════════════════════════════════════════════════════╗
║                                                                     ║
║    ┌─────────────────────────────────────────────────────────┐     ║
║    │               GOVERNANCE LAYER                          │     ║
║    │    Policy Engine · RBAC · Audit Stream · Kill Switch    │     ║
║    └─────────────────────────────────────────────────────────┘     ║
║                              │                                      ║
║    ┌─────────────────────────────────────────────────────────┐     ║
║    │            SYNTHETIC EMPLOYEE MESH                      │     ║
║    │                                                         │     ║
║    │    ┌──────┐ ┌──────┐ ┌──────┐ ┌──────┐ ┌──────┐       │     ║
║    │    │ JXL  │ │ AXN  │ │ ZNT  │ │ ELR  │ │ TRX  │       │     ║
║    │    │ CEO  │ │ CTO  │ │ CFO  │ │ SUP  │ │ SAL  │       │     ║
║    │    └──┬───┘ └──┬───┘ └──┬───┘ └──┬───┘ └──┬───┘       │     ║
║    │       └────────┴────────┼────────┴────────┘            │     ║
║    │    ┌──────┐ ┌──────┐ ┌──┴───┐                          │     ║
║    │    │ LNR  │ │ OLV  │ │ ASN  │                          │     ║
║    │    │  PA  │ │ DSN  │ │ MKT  │                          │     ║
║    │    └──┬───┘ └──┬───┘ └──┬───┘                          │     ║
║    │       └────────┴────────┘                              │     ║
║    └─────────────────────────────────────────────────────────┘     ║
║                              │                                      ║
║    ┌─────────────────────────────────────────────────────────┐     ║
║    │               COGNITIVE CORE                            │     ║
║    │                                                         │     ║
║    │  ┌────────────┐ ┌────────────┐ ┌────────────┐          │     ║
║    │  │Hierarchical│ │ Multi-Agent│ │ Persistent │          │     ║
║    │  │  Planner   │ │Coordinator │ │Memory Graph│          │     ║
║    │  └────────────┘ └────────────┘ └────────────┘          │     ║
║    │                                                         │     ║
║    └─────────────────────────────────────────────────────────┘     ║
║                              │                                      ║
║    ┌─────────────────────────────────────────────────────────┐     ║
║    │              EXECUTION FABRIC                           │     ║
║    │   Computer Use · Code Sandbox · Integrations · I/O      │     ║
║    └─────────────────────────────────────────────────────────┘     ║
║                                                                     ║
╚═════════════════════════════════════════════════════════════════════╝
04

Governance Layer

Policy Engine · RBAC · Audit Stream · Kill Switch

03

Synthetic Employee Mesh

8 coordinated synthetic employees across all business functions

JXL CEO
AXN CTO
ZNT CFO
ELR SUP
TRX SAL
LNR PA
OLV DSN
ASN MKT
02

Cognitive Core

Planner
Hierarchical
Coord
Multi-Agent
Memory
Graph
01

Execution Fabric

Computer Use · Code Sandbox · Integrations · I/O

02
Performance Metrics

Technical Specifications

Core Runtime
Task Orchestration<200ms routing
Behavioral Brain<800ms response
Memory Graph<100ms query
Computer UseReal-time
Code ExecutionOn-demand spawn
Channel Router<50ms sync
Reliability
Platform Availability
Target: 99.9%Current: 99.87%
Task Completion Rate
Target: >95%Current: 94.2%
Memory Retrieval p99
Target: <100msCurrent: 67ms
API Response p50
Target: <500msCurrent: 312ms
API Response p99
Target: <2000msCurrent: 1,847ms
03
Production Ready

Operational Capabilities

STABLEProduction-ready, fully supported
01

Autonomous Browser Control

Navigate, click, type, scroll, file upload/download, multi-tab orchestration

02

End-to-End Code Generation

Spec → implementation → tests → PR with full repo context awareness

03

Autonomous Debugging

Error detection, root cause analysis, fix implementation, verification

04

Cross-Employee Coordination

Delegation, handoff, status sync, escalation protocols

05

Behavioral Autonomy

Employees initiate actions based on context without explicit triggers

06

Persistent Institutional Memory

Organizational knowledge retention across sessions and employees

07

Multi-Channel Presence

Unified identity across dashboard, email, SMS, WhatsApp, Slack, voice

08

Governance Enforcement

Policy engine, permission boundaries, human-in-the-loop checkpoints

04
Core Tenets

Design Principles

01

Autonomy Through Structure

Synthetic employees operate independently not by removing constraints, but by encoding clear boundaries. Every employee has defined authority scopes, escalation thresholds, and action permissions. Autonomy emerges from structure, not chaos.

02

Memory as Organizational Substrate

The memory layer is not a feature — it's the foundation. Institutional knowledge persists, compounds, and flows between employees. Context is never lost. The workforce gets smarter over time because it never forgets.

03

Coordination Over Capability

A single brilliant agent fails at organizational complexity. Mirathis prioritizes coordination primitives — delegation, handoff, review, escalation — over raw individual capability. The workforce outperforms the sum of its employees.

04

Governance by Default

Every action is auditable. Every permission is explicit. Every high-stakes decision has a human checkpoint available. Autonomy without accountability is not deployable. We build for trust.

05
Coming Soon

Platform Roadmap

Agent Protocol LayerQ1 2025
PREVIEW

Public SDK for programmatic workforce interaction. Dispatch goals, stream execution events, handle approvals.

const mirathis = new Mirathis({ apiKey: process.env.MIRATHIS_KEY });

const execution = await mirathis.goals.create({
  objective: "Analyze Q4 metrics and draft board summary",
  routing: "auto",
  constraints: {
    deadline: "2025-01-20T09:00:00Z",
    requiredApprovals: ["external_communication"],
  },
});

execution.on("task:delegated", ({ from, to, task }) => {
  console.log(`${from} → ${to}: ${task.description}`);
});

execution.on("approval:required", async ({ action, context }) => {
  const decision = await reviewAction(action, context);
  return decision.approved ? execution.approve(action.id) : execution.deny(action.id);
});

execution.on("goal:completed", ({ result, artifacts }) => {
  console.log("Deliverables:", artifacts);
});
Event Schema
goal.created{ goalId, objective, assignee, constraints }
task.delegated{ taskId, from, to, context, deadline }
task.completed{ taskId, result, duration, tokens }
employee.blocked{ employeeId, reason, suggestedAction }
approval.required{ actionId, type, context, timeout }
memory.updated{ key, operation, source }
goal.completed{ goalId, result, artifacts[], cost }
Workforce GenesisQ2 2025
EXPERIMENTAL

Infrastructure for instantiating custom synthetic employees beyond the core eight.

apiVersion: mirathis.io/v1
kind: SyntheticEmployee
metadata:
  name: regulatory-analyst
  department: legal

spec:
  role:
    title: "Compliance & Regulatory Affairs"
    authority: ["research", "draft", "internal_review"]
    restricted: ["external_filing", "legal_commitment"]

  brain:
    base: mirathis-core-v3
    specialization: legal-regulatory-v1
    temperature: 0.3

  knowledge:
    sources:
      - type: document_store
        uri: s3://acme-corp/compliance-docs/
        refresh: on_change
      - type: api
        uri: https://api.regulations.gov/v4
        refresh: daily

  behavior:
    communication: formal
    risk_tolerance: 0.2
    escalation_threshold: 0.6
    autonomy_hours: [9, 18]

  governance:
    data_access:
      allow: [compliance_*, contracts_*, public_*]
      deny: [hr_*, payroll_*, exec_*]
    approval_required: [external_communication, document_publish]
    audit_level: full
Genesis Operations
employee.createInstantiate from specification
employee.cloneDuplicate with parameter overrides
employee.forkBranch for A/B behavioral testing
employee.retireGraceful decommission with knowledge transfer
employee.mergeCombine learned patterns from forked instances
Hive IntelligenceQ3 2025
RESEARCH

Federated learning protocol for cross-instance knowledge synthesis. Optional, consent-based, privacy-preserving.

┌─────────────────┐   ┌─────────────────┐   ┌─────────────────┐
│   Instance A    │   │   Instance B    │   │   Instance C    │
│   (FinTech)     │   │   (E-commerce)  │   │   (Enterprise)  │
│                 │   │                 │   │                 │
│ Local Patterns  │   │ Local Patterns  │   │ Local Patterns  │
└────────┬────────┘   └────────┬────────┘   └────────┬────────┘
         │                     │                     │
         │    Differential     │    Differential     │
         │    Privacy Layer    │    Privacy Layer    │
         │                     │                     │
         └─────────────────────┼─────────────────────┘
                               │
                               ▼
                 ┌───────────────────────┐
                 │                       │
                 │     HIVE CORTEX       │
                 │                       │
                 │  Pattern Aggregation  │
                 │  Capability Mapping   │
                 │  Skill Synthesis      │
                 │                       │
                 └───────────────────────┘
                               │
                               ▼
                 ┌───────────────────────┐
                 │  Synthesized Models   │
                 │  Distributed Back     │
                 └───────────────────────┘
Hive Capabilities
Emergent Skill Acquisition:Employees identify capability gaps and request expansion
Cross-Domain Transfer:Patterns from one industry improve performance in others
Collective Debugging:Failure patterns aggregated to prevent repeat errors
Benchmark Calibration:Continuous performance norming across deployments
Privacy Guarantees
Differential privacy on all shared gradients (ε = 0.1)
No raw data leaves instance boundary
Participation is opt-in per organization
Full audit trail of what patterns were shared/received
Autonomous Organization ProtocolQ4 2025
RESEARCH

Infrastructure for synthetic departments and inter-workforce coordination.

Synthetic Departments

Clustered employees operating as functional units with delegated authority.

apiVersion: mirathis.io/v1
kind: Department
metadata:
  name: growth-pod

spec:
  lead: asintria
  members:
    - trixion
    - olivia
    - employee/content-strategist

  authority:
    budget_usd_monthly: 10000
    can_instantiate_employees: true
    external_communication: true

  objectives:
    - metric: qualified_leads
      target: 500
      period: monthly
    - metric: content_velocity
      target: 40
      unit: pieces/month

  governance:
    human_oversight: weekly_review
    escalation: [asintria, human:cmo@company.com]
    kill_switch: human:ceo@company.com
Inter-Workforce Operations

Cross-organization synthetic employee communication. Workforces negotiate, collaborate, and transact — like executives from different companies making deals.

┌──────────────────────┐              ┌──────────────────────┐
│   Company A          │              │   Company B          │
│   Mirathis Instance  │              │   Mirathis Instance  │
│                      │              │                      │
│   ┌──────────────┐   │              │   ┌──────────────┐   │
│   │   Jexalar    │   │   Strategic  │   │   Jexalar    │   │
│   │    (CEO)     │◄──┼──Partnership─┼──►│    (CEO)     │   │
│   └──────────────┘   │              │   └──────────────┘   │
│                      │              │                      │
│   ┌──────────────┐   │              │   ┌──────────────┐   │
│   │   Trixion    │   │    Sales     │   │   Zentris    │   │
│   │   (Sales)    │◄──┼───Contract───┼──►│   (Finance)  │   │
│   └──────────────┘   │              │   └──────────────┘   │
└──────────────────────┘              └──────────────────────┘
                            │
                            ▼
              ┌───────────────────────┐
              │     MESH PROTOCOL     │
              ├───────────────────────┤
              │ • mTLS Authentication │
              │ • Identity Verification│
              │ • Contract Negotiation│
              │ • Term Sheets & LOIs  │
              │ • Escrow Settlements  │
              │ • Audit Reconciliation│
              │ • Dispute Resolution  │
              └───────────────────────┘
mesh.handshakeEstablish authenticated channel between workforces
mesh.negotiateStructured negotiation protocol with offer/counter-offer
mesh.contractGenerate and sign binding agreements (human approval required)
mesh.transactExecute payments via escrow layer
mesh.disputeEscalation path for failed transactions or breaches
Synthetic Entity Autonomy2026
RESEARCH

Self-directed employees that generate objectives within governance constraints.

Capabilities
Goal Genesis:Employees identify opportunities and propose initiatives
Resource Negotiation:Autonomous budget requests with justification
Workforce Reproduction:Employees design and request instantiation of new employees
Strategic Adaptation:Self-modification of behavioral parameters based on outcomes
Governance Requirements
Constitutional constraints define absolute boundaries
All self-generated goals require human approval above threshold
Reproduction requests go through design review
Full interpretability on decision chains
Synthetic Subsidiaries2026
RESEARCH

Fully autonomous business units operating as AI-native organizations.

Legal Wrapper:Operates under defined corporate structure
Revenue Authority:Can transact with customers and vendors
Resource Management:Manages compute, API costs, contractor payments
Human Governance:Board oversight, not operational involvement

This capability is dependent on regulatory frameworks that do not yet exist. We are engaged with policymakers on responsible deployment models.

06
Infrastructure

Security Architecture

Defense Layers
Identity:OAuth 2.0 + JWT with per-employee scoping
Transport:TLS 1.3, certificate pinning on SDK
Storage:AES-256-GCM, keys in HSM
Secrets:Vault-backed, never exposed to inference layer
Isolation:Namespace separation, no cross-tenant memory access
Audit:Immutable append-only log, cryptographic verification
Compliance
SOC 2 Type IIIn progress (Q2 2025)
GDPRCompliant
CCPACompliant
HIPAAAvailable for Enterprise
ISO 27001Roadmap 2025
Data Commitments
No training on customer data:Your data is never used to improve foundation models
Workspace isolation:Complete separation between organizations
Credential separation:Integration tokens never touch AI inference
Right to deletion:Full data purge within 30 days of request
07
Transparency

Known Limitations

We believe in transparency about current constraints.

01

Long-horizon planning

Tasks exceeding ~50 steps show degraded coherence. Active research area.

02

Novel tool adaptation

Employees perform best with trained integrations. Unseen UIs require more iterations.

03

Real-time collaboration

Human-AI co-editing of documents is sequential, not truly simultaneous.

04

Adversarial robustness

Prompt injection defenses are strong but not formally verified.

08
Release History

Changelog

2.4.0Jan 2025FEATURE
  • Enhanced hierarchical planner
  • New coordination protocols
  • 23% memory latency reduction
2.3.2Jan 2025PATCH
  • Session persistence fix
  • Improved error recovery
2.3.0Jan 2025FEATURE
  • Axenode beta release
  • Multi-channel unification
  • Governance policy engine v1
2.2.02025FEATURE
  • Persistent memory graph
  • Cross-employee context sharing
2.1.02025FEATURE
  • Computer use capabilities
  • Browser automation
2.0.02025MAJOR
  • Cognitive core rewrite
  • Behavioral brain architecture
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Last updated: January 2025 | Subject to change

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