Every memory entry carries a confidence score that evolves over time based on real-world outcomes. This is AMFS’s feedback loop — connecting agent observations to production reality.
Confidence Score
Confidence starts at 1.0 by default and represents how much trust to place in an entry:
entry = mem.write("svc", "pattern", "use connection pooling", confidence=0.85)
print(entry.confidence) # 0.85
Confidence is not capped at 1.0. An entry involved in multiple incidents can have confidence > 1.0, representing a strong risk signal.
Outcome Types
When something significant happens in the real world, you record it as an outcome:
| Outcome | Multiplier | Effect |
|---|
CRITICAL_FAILURE | × 1.15 | Strong confidence increase — knowledge linked to severe failure |
FAILURE | × 1.10 | Moderate confidence increase |
MINOR_FAILURE | × 1.08 | Mild confidence increase — knowledge linked to minor setback |
SUCCESS | × 0.97 | Confidence decay — knowledge proving reliable over time |
How It Works
Recording an Outcome
from amfs import OutcomeType
# An incident happened related to the retry pattern
updated = mem.commit_outcome(
outcome_ref="INC-1042", # reference ID (ticket, deploy ID, etc.)
outcome_type=OutcomeType.CRITICAL_FAILURE,
causal_entry_keys=["checkout-service/retry-pattern"],
)
for entry in updated:
print(f"{entry.key}: confidence {entry.confidence}")
new_confidence = old_confidence × outcome_multiplier
Confidence Over Time
Imagine an entry written with confidence=0.85:
Write → 0.85
Critical failure → 0.85 × 1.15 = 0.978
Success → 0.978 × 0.97 = 0.948
Success → 0.948 × 0.97 = 0.920
Success → 0.920 × 0.97 = 0.892
Failure → 0.892 × 1.10 = 0.981
Success → 0.981 × 0.97 = 0.951
Auto-Causal Linking
If you don’t specify causal_entry_keys, AMFS automatically links the outcome to every entry the agent read during the current session:
# Agent reads several entries during its work
mem.read("svc", "retry-pattern")
mem.read("svc", "timeout-config")
mem.read("svc", "pool-settings")
# Record outcome without specifying keys —
# it applies to all three entries above
mem.commit_outcome("DEP-300", OutcomeType.SUCCESS)
read() call during a session.
Four-Signal Decay Model
When decay_half_life_days is configured, AMFS uses four signals to determine how fast an entry’s effective confidence decays:
| Signal | Effect | Mechanism |
|---|
| Time | Base exponential decay | effective_half_life in days |
| Memory type | Facts 1×, beliefs 0.5× (faster), experiences 1.5× (slower) | Type multiplier on half-life |
| Outcome validation | Validated entries decay 2× slower | outcome_count > 0 doubles half-life |
| Access frequency | Frequently read entries resist decay | log1p(recall_count) extends half-life |
effective_half_life = base × type_multiplier × (1 + log1p(recall_count)) × (2 if outcomes else 1)
decay_half_life_days=30, 10 reads, and 1 outcome:
30 × 1.0 × (1 + log1p(10)) × 2 = 30 × 1.0 × 3.40 × 2 = 204 days
mem = AgentMemory(agent_id="my-agent", decay_half_life_days=30.0)
Filtering by Confidence
Use min_confidence to filter out low-confidence entries:
# Only return entries with confidence >= 0.5
entry = mem.read("svc", "pattern", min_confidence=0.5)
# Search with confidence filter
results = mem.search(min_confidence=0.7)
The Feedback Loop
Agent observes pattern → writes entry (confidence: 0.85)
│
▼
Incident occurs → commit_outcome("CRITICAL_FAILURE")
│
▼
Confidence increases → 0.978
│
▼
Next agent sees high-confidence risk signal
│
▼
Agent avoids the pattern → clean deploy
│
▼
Confidence decays → 0.948
