Aegis

Aegis uses a closed-loop architecture. Rather than passively alerting human operators or granting unbounded shell execution to LLMs, it connects detection to policy-gated remediation.

Watchman catches container death and tails stdout/stderr logs.

Publishes raw incident payloads into decoupled Kafka KRaft topics.

Generates 384-dim log embeddings and runs FAISS + MLP classification.

Validates confidence, risk level, and blast radius against safety gates.

Executes approved enum actions directly through Dockerode API.

Stores full incident context, embeddings, and execution receipts in MongoDB.

Air-gapped distributed topology running entirely within the host environment. Events flow asynchronously through Kafka KRaft into local neural inference and policy-enforced actuation.

Coordinates Docker Watchman events, Kafka producers/consumers, safety policy gates, and actuator calls.

ZooKeeper-free distributed event log for reliable incident streaming, decoupled buffering, and replay.

Local neural network pipeline: all-MiniLM-L6-v2 embeddings, sub-ms FAISS lookup, and PyTorch MLP classifier.

Persistent incident store, raw log history, embedding vectors, remediation plans, and offline RL replay buffers.

Direct Unix socket client executing enum-only container actions (restart, stop, inspect) without shell scripts.

Terminal execution trace showing the step-by-step progression of an incident from container death to audited recovery.

Docker daemon emits lifecycle death signal across local Unix socket.

Watchman listener filters out intentional stops and flags unexpected termination.

Recent stdout and stderr streams extracted into raw incident diagnostic context.

Asynchronous incident payload committed into partitioned Kafka event backbone.

NestJS orchestrator consumes event and routes diagnostic payload to local AI engine.

Python service returns classification label, confidence score, and suggested action.

Deterministic safety policy validates diagnosis against blast radius rules.

Orchestrator invokes scoped Dockerode restart call directly on target container.

Complete audit record, diagnosis vector, and actuation receipt stored in MongoDB.

Concrete systems guarantees enforced through decoupled streaming, immutable persistence, and deterministic safety boundaries.

Aegis avoids non-deterministic generative LLMs in favor of a fast, local neural pipeline combining semantic embedding, vector similarity, and multi-layer perceptron (MLP) classification.

Preprocesses raw container stderr/stdout logs and converts them into normalized 384-dimensional dense semantic vectors.

Executes sub-millisecond local vector distance search over historical incident embeddings to find nearest prior failures.

Classifies the 384-dim log embedding into supported failure categories (OOM, Timeout, Crash Loop, Port, Perm, Leak).

Emits failure classification, calibrated confidence score, blast radius risk level, and suggested enum remediation action.

Key architectural challenges encountered when designing a self-healing SRE platform operating in sensitive, air-gapped container environments.

Step-by-step engineering reasoning: Problem → Constraint → Engineering Response → Result applied across every critical reliability boundary.

Compact decision records detailing why each core technology was selected to guarantee air-gapped autonomy, strict isolation, and high performance.

Aegis separates diagnosis from actuation. AI recommends an action, but a deterministic policy engine evaluates safety thresholds before a single container is touched.

Neural classification confidence must be strictly ≥ 0.85. Low-confidence ambiguity defaults to safe inaction.

Risk rating must be strictly LOW. Destructive or stateful services are blocked from autonomous intervention.

Any gate failure aborts automated execution, persists full audit telemetry, and escalates to human on-call engineers.

Aegis enforces rigid execution firewalls between the probabilistic AI pipeline, the NestJS control plane, and the Docker container host.

No bash, sh, or exec wrappers. Eliminates command injection vectors completely.

Zero outbound network calls to third-party AI APIs; fully isolated from public internet.

Guaranteed reproducibility with deterministic embeddings and calibrated MLP output.

Hardcoded enum values (RESTART, STOP, IGNORE) bounding actuator scope.

Internal Docker bridge network with no external ports exposed for telemetry.

High-risk, stateful, or uncertain incidents require explicit human sign-off.

Deterministic incident state machine tracking every container transition from failure detection to audited recovery. Hover or click any state to inspect its operational invariant.

NestJS Orchestrator tests confidence >= 0.85, risk == LOW, and action == RESTART.

Aegis is not simply a container restart script. It maintains an immutable, multi-dimensional MongoDB audit history preserving full incident context, neural vectors, and execution logs.

Sequential incident-state-action tuples captured to train offline reinforcement learning policies without production interference.

Point-in-time CPU, RAM, and container network metrics recorded before, during, and after remediation actions.

Transactional outbox pattern guaranteeing that every detected container incident is eventually delivered to Kafka even if brokers temporarily disconnect.

Aegis incorporates an offline Reinforcement Learning (RL) replay pipeline to analyze historical incident episodes and evaluate future remediation heuristics without live cluster risk.

Aegis ships with a built-in chaos engineering suite to intentionally inject container failure modes and verify closed-loop detection, neural classification, and policy gating.

Spawns a memory-greedy worker allocating unconstrained RAM until Linux kernel OOM-killer fires.

Injects deadlocks and hung HTTP keep-alive loops that exceed service health-check deadlines.

Forces unhandled runtime panics and fatal SIGSEGV exit codes to test immediate recovery.

Revokes host volume filesystem permissions to evaluate permission-denied classification.

Attempts binding already-occupied host TCP ports to test address-in-use detection.

Grounded engineering outcomes built directly into the codebase and validated via chaos testing suites rather than speculative marketing numbers.

The complete autonomous lifecycle: capturing Docker failure signals, streaming through Kafka, computing local neural classifications, verifying safety gates, executing remediations, auditing state, and compiling offline learning buffers.

Autonomous systems in production cannot rely on probabilistic model outputs alone. True infrastructure safety demands decoupling recommendations from execution, bounding actuators to fixed APIs, and keeping the entire loop air-gapped.