Every team has had this moment: prod breaks, someone gets paged, and 45 minutes in someone says "wait, didn't we hit this exact error six months ago?" Someone digs up an old thread, finds the fix, incident wraps in 5 more minutes. The knowledge existed the whole time — it just wasn't in front of the on-call engineer the moment it started.

Built IncidentIQ to close that gap. It's an AI incident-response agent with one job: remember. Every time an incident gets resolved, it retains the root cause, the fix, and the lessons learned. Next time something similar happens, it recalls that record automatically and surfaces it — before anyone spends an hour rediscovering it themselves.

One-line pitch: every incident should make the next one faster to solve.

\- Backend: FastAPI, with a retain/recall interface at its core (hindsight\_memory.py). Right now it's a lightweight keyword/term-overlap scoring layer persisted to a local JSON file — zero external dependencies, no API keys needed.

\- Frontend: plain HTML/CSS/JS. No build step, no framework overhead.

The centerpiece of the demo is a side-by-side: Without Hindsight vs. With Hindsight. Left panel is generic rule-based triage (a system with no memory). Right panel is the same incident matched against something IncidentIQ has seen before — past root cause, the fix that worked, and a match score. Seeing both side by side makes the value of institutional memory concrete instead of abstract.

1. Report — incident comes in (title, error message, affected service)
2. Analyze — baseline triage runs, memory gets searched for similar past incidents
3. Compare — generic analysis shown next to hindsight-informed analysis
4. Resolve — responder feeds back root cause, solution, lessons learned
5. Retain — that record becomes permanent memory for next time

The whole memory implementation lives behind four functions — retain, recall, all\_records, stats — in a single file. The mock version uses term-overlap scoring against local JSON, which keeps it fully runnable offline for a hackathon demo. But nothing else in the app (agent, routes, frontend) knows or cares that the memory layer is mocked. Swapping in a real semantic backend — vector store, embeddings — means touching exactly one file.

\- Real embeddings for semantic recall instead of keyword matching

\- Wire the analysis step to a live LLM call (hook already exists in agent.py)

\- Feedback loop so responders can rate how useful a recalled match was, feeding back into future recall

git clone [https://github.com/bhoomikacsnc/AI-Agentt.git](https://github.com/bhoomikacsnc/AI-Agentt.git)

cd IncidentIQ

pip install -r requirements.txt

cd backend

Incident response shouldn't start from zero every time. IncidentIQ is a small step toward making sure it doesn't have to.

IncidentIQ — an incident-response agent that remembers past outages so on-call doesn't start from zero