How I Built an AI Agent That Never Forgets a Production Outage

Yashwanth Prabhu — Sun, 12 Apr 2026 17:31:39 +0000

The 2 AM Problem
It's 2 AM. Your phone screams. Production is down.
You've never seen this error before. You frantically dig through Slack, old Jira tickets, Confluence docs. Forty-five minutes later, you find it — a colleague fixed this exact same issue six months ago. Total downtime: two hours. Total cost: thousands of dollars.
The bug wasn't the real problem. Organizational amnesia was.

What I Built
The Incident Response Agent is an AI-powered SRE that never forgets. Every time an incident happens, it remembers the symptoms, root cause, and resolution. The next time something similar breaks, it instantly surfaces that memory and gives your on-call engineer a targeted diagnosis — not a generic "have you tried restarting it?"
Built for the Hindsight Hackathon at HackWithChennai 2026, this agent uses three core memory operations — retain, recall, and reflect — to build a living, compounding knowledge base of your infrastructure's failure history.

The Before vs After
Here's the clearest way to explain why this matters:
Without memory (generic LLM):

"CrashLoopBackOff usually means your container is crashing repeatedly. Check your logs with kubectl logs. Common causes include misconfiguration, missing dependencies..."

You already knew that. It's useless at 2 AM.
With Hindsight Memory (our agent):

"MATCH FOUND — INC004 (resolved in 18 minutes): Payment service pods entered CrashLoopBackOff after a ConfigMap update. Root cause: missing PAYMENT_API_KEY environment variable.
Immediate steps: (1) kubectl get configmap -o yaml and check for PAYMENT_API_KEY, (2) kubectl describe pod to confirm env var error, (3) kubectl rollout restart after fix. Estimated resolution: ~18 minutes."

That's the difference between organizational amnesia and institutional intelligence.

How Hindsight Memory Works
Hindsight by Vectorize gives AI agents persistent, semantic memory through three operations:
retain() — stores any information as a retrievable memory. We call this after every incident is reported or resolved, saving the description, root cause, resolution steps, and time-to-fix.
recall() — semantically searches stored memories by similarity. When a new incident comes in, we search for the 5 most similar past incidents before passing anything to the LLM. This means the diagnosis is grounded in your actual history, not generic internet knowledge.
reflect() — synthesizes patterns across all stored memories. We use this for weekly ops reviews: "Database incidents spike every Friday after the 5 PM deployment." That's proactive prevention.
The flow looks like this:
New Incident Reported
↓
recall() → Top 5 similar past incidents
↓
LLM Diagnosis → Root cause + actions + timeline
↓
retain() → Stored for future recall
↓
reflect() → "DB issues spike every Friday after deploy"

Tech Stack

Memory: Hindsight Cloud by Vectorize
LLM: Groq — llama-3.3-70b-versatile (fast, free tier)
Backend: FastAPI (Python)
Frontend: Vanilla HTML/CSS/JS dashboard
Agent: Python 3.10+

The architecture is intentionally simple. The intelligence comes from memory, not complexity.

What I Learned

Memory changes everything about AI agents. A stateless LLM is a knowledgeable stranger. An LLM with memory is a colleague who was there last time. The quality of the diagnosis didn't just improve — it became actionable.
The real value compounds over time. The agent gets smarter with every incident. After 10 incidents it's helpful. After 100 it's indispensable. After a year it knows your infrastructure's failure patterns better than any human who's changed teams.
reflect() is underrated. Most people think of AI memory as "store and retrieve." But the reflect operation — synthesizing patterns across everything stored — is where the real insight lives. It's the difference between a log file and an ops review.
Grounding LLMs in real history eliminates hallucination. When the LLM has actual past incident data to work from, it stops guessing. The diagnosis becomes specific because the context is specific.

Try It Yourself
The full code is open source:
GitHub: https://github.com/yashwanthprabhu07/incident-response-agent
To run it locally:
bashgit clone https://github.com/yashwanthprabhu07/incident-response-agent.git
cd incident-response-agent
pip install -r requirements.txt

Add your .env with GROQ_API_KEY and Hindsight credentials

cd agent && python incident_agent.py
You'll need a free Groq API key and a free Hindsight Cloud account.

What's Next

Slack/PagerDuty integration — so the agent responds automatically when an alert fires
Auto-runbook generation — use reflect() to generate preventive runbooks from patterns
Multi-team memory banks — separate memory contexts per service or team
Confidence scoring — show how closely a past incident matches the current one

Final Thought
Every company with servers has had the same incident twice. The Incident Response Agent makes sure that never happens again.
If your production system has ever gone down for the same reason twice — this agent is for you.

Built at HackWithChennai 2026 — Hindsight Hackathon | [YOUR NAME] | [TEAM NAME]

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How I Built an AI Agent That Never Forgets a Production Outage
By Yashwanth Prabhu R | @codex | HackWithChennai 2026

The Before vs After
Here's the clearest way to explain why this matters:
Without memory (generic LLM):

"CrashLoopBackOff usually means your container is crashing repeatedly. Check your logs with kubectl logs. Common causes include misconfiguration, missing dependencies..."

You already knew that. It's useless at 2 AM.
With Hindsight Memory (our agent):

That's the difference between organizational amnesia and institutional intelligence.

Tech Stack
LayerTechnologyMemoryHindsight Cloud by VectorizeLLMGroq — llama-3.3-70b-versatileBackendFastAPI (Python)FrontendVanilla HTML/CSS/JS dashboardAgentPython 3.10+
The architecture is intentionally simple. The intelligence comes from memory, not complexity.

What I Learned

Memory changes everything about AI agents. A stateless LLM is a knowledgeable stranger. An LLM with memory is a colleague who was there last time. The quality of the diagnosis didn't just improve — it became actionable.
The real value compounds over time. The agent gets smarter with every incident. After 10 incidents it's helpful. After 100 it's indispensable. After a year it knows your infrastructure's failure patterns better than any human who's changed teams.
reflect() is underrated. Most people think of AI memory as "store and retrieve." But the reflect operation — synthesizing patterns across everything stored — is where the real insight lives. It's the difference between a log file and an ops review.
Grounding LLMs in real history eliminates hallucination. When the LLM has actual past incident data to work from, it stops guessing. The diagnosis becomes specific because the context is specific.

Try It Yourself
The full code is open source:
GitHub: https://github.com/yashwanthprabhu07/incident-response-agent

bashgit clone https://github.com/yashwanthprabhu07/incident-response-agent.git
cd incident-response-agent
pip install -r requirements.txt

Add your .env with GROQ_API_KEY and Hindsight credentials

cd agent && python incident_agent.py
You'll need a free Groq API key and a free Hindsight Cloud account.

Built at HackWithChennai 2026 — Hindsight Hackathon | Yashwanth Prabhu R | @codex

DEV Community: Yashwanth Prabhu

How I Built an AI Agent That Never Forgets a Production Outage

Add your .env with GROQ_API_KEY and Hindsight credentials

Add your .env with GROQ_API_KEY and Hindsight credentials