Confidence & Outcomes

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}")

Confidence Formula

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

Over many successes, confidence trends toward zero — the risk signal fades. A single failure spikes it back up.

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)

This is powered by the ReadTracker, which logs every 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

The effective half-life formula:

effective_half_life = base × type_multiplier × (1 + log1p(recall_count)) × (2 if outcomes else 1)

For example, a fact with 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

This means actively used, production-validated knowledge persists far longer than cold, unvalidated beliefs.

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

This creates a self-correcting system: risky patterns get flagged, safe patterns fade, and agents inherit the accumulated wisdom of past sessions.

Product

Getting Started

Guides

Core Concepts

Pro Features

Confidence & Outcomes

Confidence Score

Outcome Types

How It Works

Recording an Outcome

Confidence Formula

Confidence Over Time

Auto-Causal Linking

Four-Signal Decay Model

Filtering by Confidence

The Feedback Loop

​Confidence Score

​Outcome Types

​How It Works

​Recording an Outcome

​Confidence Formula

​Confidence Over Time

​Auto-Causal Linking

​Four-Signal Decay Model

​Filtering by Confidence

​The Feedback Loop

Confidence Score

Outcome Types

How It Works

Recording an Outcome

Confidence Formula

Confidence Over Time

Auto-Causal Linking

Four-Signal Decay Model

Filtering by Confidence

The Feedback Loop