Summary
Hindsight provides agent memory that works like human memory — with importance-based retention, forgetting curves, and contextual recall. TealTiger governance decisions are a natural fit for Hindsight's memory system: governance events have varying importance, should decay over time, and need to be recallable during future decisions.
Why this integration?
Currently, TealTiger governance decisions are either in-memory (lost on restart) or persisted in flat storage. Hindsight adds intelligence to governance memory:
- Importance-based retention — DENY decisions (high risk) persist longer than routine ALLOWs
- Contextual recall — "What governance decisions were made for this agent in similar contexts?"
- Forgetting curves — Old, low-importance approvals naturally age out
- Pattern detection — Recall clusters of denials to detect systematic policy violations
Proposed approach
HindsightGovernanceMemory — stores TealTiger decisions in Hindsight with importance scoring
- Decision importance mapping: DENY=0.95, ALLOW=0.60, MONITOR=0.70 (mirrors Dakera pattern)
- Contextual governance recall — before making a new decision, recall similar past decisions
- Anomaly detection — flag when an agent's denial rate spikes vs. historical baseline
Example usage
from hindsight import HindsightClient
from tealtiger.integrations.hindsight import HindsightGovernanceMemory
hindsight = HindsightClient(api_key="your-key")
governance_memory = HindsightGovernanceMemory(
client=hindsight,
importance_map={
"deny": 0.95,
"allow": 0.60,
"monitor": 0.70,
},
)
# Every governance decision is stored with importance-weighted retention
client = TealOpenAI(
api_key=os.getenv("OPENAI_API_KEY"),
guardrails={"pii_detection": True},
on_decision=governance_memory.store,
)
# Later: recall governance history for context
history = governance_memory.recall(
agent_id="research-agent",
context="tool call to web_search",
limit=5,
)
Deliverables
Links
Summary
Hindsight provides agent memory that works like human memory — with importance-based retention, forgetting curves, and contextual recall. TealTiger governance decisions are a natural fit for Hindsight's memory system: governance events have varying importance, should decay over time, and need to be recallable during future decisions.
Why this integration?
Currently, TealTiger governance decisions are either in-memory (lost on restart) or persisted in flat storage. Hindsight adds intelligence to governance memory:
Proposed approach
HindsightGovernanceMemory— stores TealTiger decisions in Hindsight with importance scoringExample usage
Deliverables
tealtiger.integrations.hindsightmoduleHindsightGovernanceMemoryclass implementing importance-weighted storageLinks