Why I Stopped Using Stateless LLMs for Production Outages and Built Aegis SRE

At 2 AM when production is burning, the worst response to an obscure database pool exhaustion log is textbook advice that previously took down your replica nodes.

Stateless LLMs treat every prompt as day zero. They don't know that three weeks ago your team tried bumping connection pools to 500, only to discover it created massive CPU lock contention that degraded the entire cluster. To solve this, I designed Aegis SRE—an autonomous incident commander that pairs high-speed LLM inference with persistent graph memory using Hindsight.

The Core Problem: The Tribal Knowledge Black Hole

Modern DevOps teams generate dozens of thorough incident post-mortems every quarter. Yet during live incidents, that collective intelligence remains buried in static Markdown docs or Jira tickets. On-call engineers continually rediscover the same root causes and repeat the same failed mitigations.

Standard retrieval-augmented generation (RAG) falters here because incidents aren't static documents—they are causal chains: Symptom X led to Failed Action Y, which required Verified Resolution Z.

To bridge this gap, I turned to Vectorize agent memory to give the agent a native, evolving knowledge graph of past reliability incidents.

System Architecture

Aegis SRE combines three primary components:

1. Incident Telemetry Console: A dual-pane cockpit built with Streamlit for log ingestion and real-time memory introspection.
2. Causal Memory Engine: Powered by Hindsight documentation principles, retaining post-mortems as structured semantic and causal graph nodes.
3. Execution Runtime: Groq Llama-3 inference that applies negative constraints and verified mitigation steps before generating recommendations.

python
# Direct, thread-safe memory recall from Hindsight
def recall_from_hindsight(bank_id: str, query: str):
    url = f"[https://api.hindsight.vectorize.io/v1/default/banks/](https://api.hindsight.vectorize.io/v1/default/banks/){bank_id}/memories/recall"
    headers = {
        "Authorization": f"Bearer {HINDSIGHT_API_KEY}",
        "Content-Type": "application/json"
    }
    response = requests.post(url, headers=headers, json={"query": query}, timeout=15)
    if response.status_code == 200:
        return [r.get("text", str(r)) for r in response.json().get("results", [])]
    return []

Before vs. After: The Connection Pool Trap
To evaluate the system, I fed an active outage log into both modes:

"Service auth-service throwing HTTP 504 Gateway Timeouts on auth-cluster-04 with error: 'FATAL: remaining connection slots are reserved for non-replicated superuser connections'. Should I increase max_connections to 500?"

Baseline (Memory Disabled)
The stateless LLM politely provided standard database advice:

"Yes, you can increase max_connections to 500 in postgresql.conf and reload the service to allow more client sessions."

In production, executing this advice would have caused cascading thread contention across the cluster.

Aegis SRE (Hindsight Memory Active)
In 1.1 seconds, Hindsight recalled 11 connected graph nodes from previous incidents.

The resulting mitigation plan stopped the disaster in its tracks:

CRITICAL WARNING: DO NOT INCREASE max_connections TO 500.

Based on historical post-mortem data, increasing max_connections from 200 to 500 previously worsened an identical incident by causing severe lock contention. The verified root cause is dead connection leaks in payment callback webhooks.

Verified Remediation:

Apply TCP socket reuse: sysctl -w net.ipv4.tcp_tw_reuse=1

Configure HikariCP idleTimeout=30000ms and maxLifetime=1800000ms.

Key Takeaways
Negative constraints are more valuable than suggestions: Preventing an engineer from applying a known bad fix saves more downtime than generating generic ideas.

Graph memory beats plain vector search: Linking error symptoms directly to causal resolutions and runbook IDs prevents the context fragmentation common in chunk-based RAG.

Closing the loop: Retaining post-incident resolutions back into Hindsight ensures the system continuously learns after every resolution.

Full open-source implementation:

Repository: imranshareefshaik/aegis-sre-incident-commander