Skills & Knowledge
How MARIA OS manages agent capabilities, knowledge lifecycle, and skill governance across the operating system.
Skills are fetched or generated on demand
The Skill Requirement Engine matches agent capabilities to task requirements. Missing skills trigger acquisition or generation.
Dynamic Skill Fetch
Requirement resolved from existing Skill Store
Requirement
Skill key, domain, level
Store Search
Query matching capability
Resolve/Create
Generate if not found
Bind to Agent
Phase Gate enforced
Skill / Phase Permission Matrix
Not all skills are available in all phases. The permission matrix governs which skills can be used at each decision stage.
Watch skills flow to Agents
Real-time visualization of skill allocation and consumption across the agent network.
Market Analyst
Strategy
Risk Evaluator
Compliance
Data Processor
Operations
Quality Auditor
Governance
Waiting for refill events...
5
Total Skills Bound
0
Refills Complete
0
Active Transfers
Agents never stop. Skills arrive before they are needed.
Agent execution power never depletes
Skills are continuously replenished. Capacity management ensures agents always have the resources they need.
Capability Mismatch
Agent knows what to do but cannot execute at required level
Quality Degradation
Skill is fatigued or no longer fits current context
Phase Mismatch
Decision weight exceeds skill strength
Environment Change
Skill is correct but premises have shifted
Agents get tired. Environments change. Decisions get heavier.
MARIA OS refills skills, not replaces Agents.
Universe Topology
The structural layout of Universes, Planets, and Zones — the spatial dimension of the operating system.
Skills Measure Design Adherence, Not Just Performance
Skills are observation instruments that verify whether a Universe executes according to its designed judgment structure. Not vanity metrics. Governance truth.
Stop rates are improvement signals, not failures
Targets adapt to risk tier and genesis phase
Skills measure whether the Universe is betraying its design philosophy.
Right Targets, Right Context
One size doesn't fit all. Target profiles adapt to your risk tier and data classification. Stop reasons tell you where to improve.
low-medium
medium-high
high
low
Every stop is a signal. Every profile is a strategy.
Post-Deployment
Execution Flow Circuit
Parallel, serial, and loop paths with HITL checkpoints
Flow Legend
HITL Checkpoints
Trusted Knowledge, Not Just Available
Evidence-backed judgment units
Offline knowledge is not a convenience feature. It is judgment infrastructure. Three guarantees: content is correct, provenance is traceable, time decay is visible.
Package
Evidence
Interpretation
Constraint
Integrity
Past decisions can be replayed with their original knowledge context.
Financial Compliance v2.3
K-2024-001 | basis: 2024-04
142d left
valid
HR Policy Guidelines
K-2024-002 | basis: 2024-01
28d left
warning
Vendor Assessment Criteria
K-2023-015 | basis: 2023-12
14d overdue
expired
Offline knowledge is evidence for judgment, not a reference library. It exists to support decisions and explain them later.
Offline-first for audit resilience. Online for supplementary context.
Knowledge decays. Transparency prevents drift.
Two Sources, One Clear Hierarchy
Never blend. Always distinguish.
Offline is the authority. Online is the supplement. They are never merged — the reference strategy is unified, not the knowledge itself.
Judgment Basis
Primary source for decisions
Audit Resilience
Evidence that persists
Reproducibility
Replay any past decision
Accountability
Explain why this choice
Supplementary Info
Latest developments
External Context
Market conditions
Reference Examples
Similar cases
Advisory Opinion
Not authoritative
Policy v2.3: Max $5,000 without escalation
Industry avg: $4,200 for similar purchases
Approve based on policy (offline primary)
Humans manage the boundary. AI respects it. When offline and online conflict, the system escalates — it never auto-resolves.
Audit-resistant: Municipality. Finance. Education. Healthcare.
Two sources, one clear hierarchy. Always.
Scheduled Knowledge Cleansing
Daily
Real-time conflict check
847
this month
Weekly
Cross-agent consistency
12
this month
Monthly
Full structure review
1
this month
3
Critical
5
Warning
12
Resolved
Cycle-driven knowledge maintenance
Policy Engine
AI decisions require more than correctness—they need consistency, explainability, and accountability. Policy Engine embeds guardrails into every automated judgment.
All constraints satisfied. Proceeding to execution.
Unit tests + Logs
Additional tests + Refactor trace
Human review + Escalation
Multi-party approval + Staged deploy
Policy Engine permits—not produces—AI decisions.
Rules + Values + Evidence = Safe, explainable actions.
Transform Documents into Governance
Your existing policies become executable decision rules
Upload
Policy documents, handbooks, guidelines
Extract
Automatic extraction of decision rules
Integrate
Merge with existing value hierarchy
Sync
Continuous policy synchronization
Supported Document Types
Documents in, governance out. No manual rule writing required.