How I Built an Autonomous SRE Agent That Actually Remembers Past Incidents Using Hindsight and FastAPI

Pranitha U P — Sat, 07 Nov 2026 13:28:51 +0000

When production goes down, every minute counts. Traditional monitoring tools alert you instantly, but they don't remember why a similar connection pool exhaustion or memory leak happened three months ago. Your SRE team ends up reinventing the wheel under pressure.

I wanted to change that. I set out to build an Autonomous Site Reliability Engineering (SRE) Incident Response Agent that doesn't just process stateless errors, but maintains long-term, persistent operational memory to diagnose and resolve production failures instantly.

Here is how I architected and implemented it using FastAPI, Google Gemini, and Hindsight.

The Architecture: Why Stateless LLMs Fail at Ops

Most AI troubleshooting scripts fail because LLMs are stateless. When a database error hits, a standard LLM chatbot sees the error in isolation. It has no context of your infrastructure's history, past post-mortems, or previous configuration tweaks.

To build a true agentic system, we need a dedicated memory layer. I integrated Hindsight, a specialized memory system developed by Vectorize, which allows AI agents to store, semantic-recall, and evolve their operational knowledge over timespan_3 span_3.

The system operates on two core pillars:

Memory Retention (/api/retain): Automatically saving resolved incident runbooks, root causes, and terminal commands into a persistent vector bank.
Contextual Retrieval & Diagnosis (/api/diagnose): Querying historical memory when a new error occurs, injecting relevant past runbooks into the LLM context, and generating a precise SRE resolution planspan_4 span_4.

Code Implementation: Building the Backend Agent

Here is the core backend implementation using FastAPI, the modern google-genai SDK, and the Hindsight client.


python
import os
from fastapi import FastAPI
from pydantic import BaseModel
from hindsight_client import Hindsight
from google import genai

app = FastAPI(title="Autonomous AI Incident Response Agent")

# Environment & Clients Setup
HINDSIGHT_API_KEY = "hsk_a0f29fd7808a64542cf1dec59f70aab1_9b3f87fec8505c17"
HINDSIGHT_ENDPOINT = "[https://api.hindsight.vectorize.io](https://api.hindsight.vectorize.io)"
GEMINI_API_KEY = "AQ.Ab8RN6IXMyfNuX1LDejSxSjs5alio0Tk08hG5jkGikqHVC_t2A"

hindsight_client = Hindsight(base_url=HINDSIGHT_ENDPOINT, api_key=HINDSIGHT_API_KEY)
gemini_client = genai.Client(api_key=GEMINI_API_KEY.strip())
BANK_ID = "incident-logs-bank"

class DiagnoseRequest(BaseModel):
    error_log: str

def recall_and_diagnose(new_error_log: str):
    # Step 1: Recall similar past incidents from Hindsight memory
    recall_res = hindsight_client.recall(bank_id=BANK_ID, query=new_error_log)
    memories = [m.text for m in recall_res.results]
    recalled_context = "\n".join(memories) if memories else "No past memories found."

    # Step 2: Formulate SRE prompt with historical context
    prompt = f"""
    You are an Autonomous SRE Incident Response AI Agent.
    Current Server Error: {new_error_log}
    Historical Memory Runbooks: {recalled_context}
    Diagnose the root cause and provide step-by-step resolution.
    """

    # Step 3: Generate diagnostic plan via Gemini
    response = gemini_client.models.generate_content(
        model="gemini-2.5-flash",
        contents=prompt
    )
    return {"diagnosis": response.text, "recalled_memory": recalled_context}

@app.post("/api/diagnose")
def api_diagnose(data: DiagnoseRequest):
    return recall_and_diagnose(data.error_log)

DEV Community: Pranitha U P

How I Built an Autonomous SRE Agent That Actually Remembers Past Incidents Using Hindsight and FastAPI

The Architecture: Why Stateless LLMs Fail at Ops

Code Implementation: Building the Backend Agent