The Governance Tuple
Every governed AI decision produces a triple: CONTRACT (what was authorized), DCT (who authorized it, cryptographically signed), and EVIDENCE (what actually happened). This is the atomic record that makes AI governance auditable.
CONTRACT
Permitted operations, resources, caveats, expiry
×
DCT
HMAC-signed delegation binding issuer → subject
×
EVIDENCE
Actual operations, resources accessed, cost, output
governance_tuple.py
from hummbl_governance import DelegationTokenManager, BusWriter, CostGovernor
# CONTRACT: define what the agent may do
contract = {
"ops_allowed": ["read", "summarize"],
"resources": ["docs/*"],
"expiry": "2026-12-31T23:59:59Z"
}
# DCT: cryptographically bind the delegation
dtm = DelegationTokenManager()
token = dtm.issue(
issuer="orchestrator",
subject="summarizer-agent",
operations=contract["ops_allowed"],
resources=contract["resources"]
)
# EVIDENCE: audit what actually happened
bus = BusWriter("governance.jsonl")
bus.append(agent="summarizer-agent", action="read", resource="docs/report.md")
bus.append(agent="summarizer-agent", action="summarize", resource="docs/report.md")
Cognitive Layer: Base120 Mental Models
The reasoning substrate that operates over governance primitives. 120 validated mental models across 6 transformation types.
P
Perspective
Frame and anchor viewpoint. Change how you see the problem.
IN
Inversion
Flip to see what avoiding failure reveals. Think backwards.
CO
Composition
Combine for emergent properties. Build complexity from simplicity.
DE
Decomposition
Break complexity down. Understand the parts.
RE
Recursion
Apply patterns at multiple scales. Iterate and refine.
SY
Systems
Understand governing rules. See the meta-patterns.
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