A Different Way to Build: My Experience with Kiro + IncidentOps

reema raghava — Fri, 05 Dec 2025 20:41:40 +0000

After 25 years in IT operations, I’ve learned that the biggest incidents rarely come from a single dramatic failure. They usually begin as small, recurring signals that sit quietly at the edges of a production environment — buried in logs, overlooked in dashboards, or lost inside noisy alerts.

For this year’s Kiro Hackathon, I wanted to build something that made those signals easier to see. The result was IncidentOps: a multi-agent incident pipeline that detects anomalies, summarizes them, assigns deterministic severity, generates remediation suggestions, performs governance checks, and surfaces recurring patterns across runs. Everything is persisted in SQLite and visualized through a multipage Streamlit UI.

Architecture Overview

Why Kiro?

What surprised me most about this project was how different Kiro felt from typical AI-assisted coding. Instead of trying to “guess” what I wanted, Kiro encouraged me to define the system clearly through spec-driven development.

Writing the specs felt natural — almost like capturing my thought process. Once the specs were ready, the Start Task workflow made execution predictable and efficient. I could ask Kiro to build one component at a time, review it, refine it, and move forward.

Tasks that normally take weeks across engineering and operations teams were completed in hours with human review.

What I Built

IncidentOps is a sequential pipeline:

MonitorAgent → LLMAlertSummaryAgent → TriageAgent →

LLMResolutionAgent → OpsLogAgent → LLMGovernanceAgent →

LLMGovernanceInsightsAgent → NotificationAgent

Each agent has a focused responsibility:

Detect anomalies
Produce human-friendly summaries
Assign severity and category deterministically
Suggest remediation steps
Write factual audit logs (no interpretation)
Score risk, escalation, and compliance
Analyze historical patterns using DB aggregations
Send notifications

The system uses:

Python for orchestration
SQLite for persistence
Streamlit for the UI
LLMs for summarization, remediation, and insights
Kiro’s spec-driven workflow for structure and iteration

What I Learned

Clear specs dramatically speed up development
AI-generated code still requires human validation
Small, well-defined units reduce complexity and drift
Combining deterministic logic with LLM reasoning gives both reliability and adaptability
A structured workflow makes even complex systems manageable under tight timelines

Closing Thoughts

Good tools don’t just accelerate development — they improve clarity of thought. For incident management, where hidden patterns matter as much as visible errors, that clarity is essential.