How We Built an Autonomous Incident Response Agent with Persistent Memory Using Hindsight

When a production database crashes at 2 AM, the last thing an on-call Site Reliability Engineer (SRE) wants to do is comb through months of scattered post-mortems across Notion and Slack threads.

Stateless LLMs fail in real-world DevOps because they lack memory. Ask a standard AI assistant why your database connections are exhausted, and it returns a generic textbook tutorial on tuning connection pools. It has no institutional memory: it doesn't know that your payment microservice has a known connection leak under peak traffic, or that cycling PgBouncer is your team's proven first-response playbook.

To bridge this gap, we built AutoOps SRE—an incident remediation agent powered by Vectorize agent memory and Hindsight.

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The Architecture: Why Simple RAG Isn't Enough

Standard Retrieval-Augmented Generation (RAG) treats knowledge as static files. But production infrastructure knowledge is dynamic: runbooks evolve, incidents recur with subtle syntax variations, and resolutions must compound over time.

We structured the remediation engine around the Hindsight documentation architecture into an active SRE operational loop:

1. The Retain Phase: Whenever senior engineers resolve a production outage, the post-mortem summary and bash runbooks are ingested into Hindsight using local vector extraction.
2. The Semantic Recall Phase: When a raw alert fires, the agent queries Hindsight to extract historical precedents based on semantic meaning, bypassing syntactic differences.
3. The Reasoning Phase: The LLM receives the recalled institutional context alongside real-time alert logs to produce an exact remediation runbook in seconds.

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Code Implementation

Here is how Hindsight's embedded engine orchestrates the recall-and-diagnose loop in Python:

python
import os
from hindsight import HindsightEmbedded
from google import genai

# Initialize embedded local Hindsight memory
memory = HindsightEmbedded(
    profile="incident-agent-gemini",
    llm_provider="gemini",
    llm_model="gemini-2.5-flash",
    llm_api_key=os.environ["GEMINI_API_KEY"],
)

# 1. Store institutional runbook into persistent memory
memory.retain(
    bank_id="production-incidents",
    content="INCIDENT: PostgreSQL exhaustion 'FATAL: remaining connection slots reserved' | FIX: sudo systemctl restart pgbouncer"
)

# 2. Recalling contextual solutions for new, vaguely worded alerts
past_memories = memory.recall(
    bank_id="production-incidents", 
    query="Postgres database rejecting connections, error says slots are fully occupied!"
)