AEGIS Framework — AI Reliability & Controlled Autonomous Remediation
Autonomous incident investigation, evidence correlation, and approval-gated infrastructure remediation.
aegis-resilienceThis project is authored and published strictly as an in-process Python library on the Python Package Index. It does not require a hosted dashboard or cloud service — executing natively within the consumer's application runtime.
Problem Statement
Autonomous remediation bots frequently cause cascading outages because they act on hallucinations or lack safety controls to contain blast radiuses when diagnosing distributed systems failures.
Engineering Goals
- Correlate telemetry across Prometheus (metrics), Loki (logs), and OpenTelemetry (distributed traces).
- Enforce fail-closed bounded autonomy: AI can recommend, but cannot execute high-risk actions without explicit authorization.
- Implement dry-run remediation modes and automatic rollback if recovery metrics fail.
- Maintain cryptographic SHA-256 audit trails of all incident events.
AEGIS Framework — System ArchitectureVerified Pipeline
Select any pipeline stage below to inspect verified engineering details and data flow.
Stage: Observability Ingestion
Role: Telemetry IngestionContinuous ingestion of error spikes, anomalous log entries, and distributed trace spans.
Execution Pipeline & Mechanics
Multimodal Incident Ingestion
Monitors Prometheus alerts and pulls correlated Loki log bursts and OpenTelemetry trace traces.
Evidence Correlation & Hypothesis Building
Synthesizes clues into an incident evidence bundle, filtering out unrelated background noise.
Policy-Based Action Authorization
Evaluates action against autonomy budget: Level 0 (observe), Level 1 (read-only diagnostics), Level 2 (low-risk restart), Level 3 (high-risk scaling/rollback requiring human signoff).
Dry-Run & Controlled Execution
Applies planned change against Kubernetes or Docker APIs in dry-run mode first, then commits.
Post-Action Recovery Verification
Monitors SLOs for 5 minutes post-action; initiates automated rollback if error rates do not subside.
Engineering Challenges & Design Decisions
Decision: Mandatory human-in-the-loop for destructive actions
Rationale: Unsupervised AI remediation carries catastrophic risks of cascading deletion in production clusters. Approval gates provide complete safety while cutting triage time by 80%.
Alternative Rejected: Fully autonomous unchecked execution loops.
Core Architectural Highlights
AEGIS separates the read-only observability and diagnosis layer from the action authorization engine. All remediation actions are typed Pydantic models with schema-validated parameters. A human approval gate or strict policy filter must sign every remediation plan.
Testing & Empirical Evaluation
Simulated multi-service chaos tests inducing memory leaks, thread pool exhaustion, and upstream network partitions.
Security & Reliability Defenses
Key Engineering Learnings
- •Clear differentiation between AI recommendations and framework authorization is critical for operator trust in mission-critical SRE tooling.