ACMF Maturity Model
Agent Capability Maturity Framework - Progressive autonomy with automatic progression based on demonstrated performance and automatic regression on failures.
Four Maturity Levels
OBSERVE
New agents, learning baseline patterns
Observe only, no execution. Agent watches human decisions and learns patterns.
LEARNING
Building confidence, validation phase
Can execute but all actions require human review and approval.
ASSISTED
Proven agents handling routine tasks
Full execution capability, but critical operations still require human approval.
AUTONOMOUS
Mature agents with proven track record
Fully autonomous execution with audit trail only. No human approval needed.
Progression Requirements
| Transition | Success Rate | Min Tasks | Consecutive Success | Max Override Rate | Min Confidence |
|---|---|---|---|---|---|
OBSERVELEARNING | 95% | 100 | 20 | 5% | 85% |
LEARNINGASSISTED | 90% | 200 | 30 | 10% | 80% |
ASSISTEDAUTONOMOUS | 95% | 500 | 50 | 2% | 90% |
All Criteria Must Be Met
Progression requires simultaneously meeting all thresholds. Metrics reset when advancing to a new mode to track fresh performance.
Automatic Progression & Regression
Automatic Progression
Agents automatically progress to higher maturity levels when they meet all progression criteria.
Performance Tracking
Continuous monitoring of success rate, confidence, and human override rate
Metrics Reset
Performance counters reset upon progression to track new mode behavior
State Persistence
Maturity state persisted to Redis for recovery across restarts
Automatic Regression
Agents automatically regress to lower maturity levels when they experience too many consecutive failures.
Failure Threshold
5+ consecutive failures trigger automatic regression
Cascade Regression
AUTONOMOUS → ASSISTED → LEARNING → OBSERVE
Detailed Logging
Regressions logged with full context for post-incident analysis
RIGOR Integration
ACMF mode determines how RIGOR executions proceed, particularly around the GENERATE → OPERATIONALIZE transition.
Human Approval Decision Tree
RequiresHumanApproval(agentID, isCritical) → bool ┌───────────────────────────────────────────────────────────┐ │ ACMF Mode │ Operation Type │ Requires Approval? │ ├───────────────────────────────────────────────────────────┤ │ OBSERVE │ Any │ YES (always) │ │ LEARNING │ Any │ YES (always) │ │ ASSISTED │ Critical │ YES │ │ ASSISTED │ Normal │ NO │ │ AUTONOMOUS│ Any │ NO │ └───────────────────────────────────────────────────────────┘ Note: GateHumanApproval (v1.6) operates INDEPENDENTLY of ACMF mode for high-risk operations. Even AUTONOMOUS agents need approval for operations like production DB changes or security policy modifications.
Execution Status by Mode
- OBSERVE:
CanExecute() = false - LEARNING:
Status: awaiting_approval - ASSISTED:
Conditional approval - AUTONOMOUS:
Full execution
State Management
- • Redis key:
acmf:agent_modes - • Auto-recovery on startup
- • Per-agent isolation
- • Metrics tracking: success/failure counts, confidence, override rate
Implementation
ACMF Manager (Go)
// RecordTaskOutcome records task outcome and checks for progression/regression
func (am *ACMFManager) RecordTaskOutcome(
agentID string,
success bool,
confidence float64,
humanOverride bool,
) {
maturity := am.getOrCreateMaturity(agentID)
// Update counters
maturity.TotalTasks++
if success {
maturity.SuccessCount++
maturity.Metrics.ConsecutiveSuccesses++
maturity.Metrics.ConsecutiveFailures = 0
} else {
maturity.FailureCount++
maturity.Metrics.ConsecutiveFailures++
maturity.Metrics.ConsecutiveSuccesses = 0
}
// Update metrics
maturity.Metrics.SuccessRate = float64(maturity.SuccessCount) / float64(maturity.TotalTasks)
maturity.Metrics.AvgConfidence = updateRunningAverage(
maturity.Metrics.AvgConfidence, confidence, maturity.TotalTasks)
// Check for progression
am.checkProgression(maturity)
// Check for regression (5+ consecutive failures)
am.checkRegression(maturity)
}Continue Exploring
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