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_3span_3.

The system operates on two core pillars:

  1. Memory Retention (/api/retain): Automatically saving resolved incident runbooks, root causes, and terminal commands into a persistent vector bank.
  2. 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_4span_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)