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AIRA — Autonomous Incident Response Agent

Inspiration

On-call rotations are burning out engineers. Every outage triggers the same painful cycle: alert → page → wake up → diagnose → fix → repeat. We saw teammates spending 60% of their time on repetitive incidents — restarts, scaling, config typos — while critical system failures waited 2-4 hours for human intervention.

Then we did the math: at $5,600/hour downtime cost, a single slow incident pays for an entire automation platform. What if an AI agent could handle the 80% of incidents that follow predictable patterns, and only wake humans for the novel 20%?

That's when we built AIRA.

What it does

AIRA is an autonomous incident response agent that goes from alert to pull request in under 30 seconds — with zero human handoff when confidence exceeds 85%.

• Instant triage → Classifies P0-P3 severity in <5 seconds
• Smart diagnosis → Reads code context + historical incident data
• Auto-remediation → Generates patches with confidence scoring
• Selective escalation → Human review only when confidence < 85%
• Continuous learning → Improves accuracy from every incident

It blocks dangerous paths (auth.py, secrets.yml), maintains a full audit trail, and shows everything on a real-time WebSocket dashboard. Engineers finally sleep through the night.

How we built it

The 5-phase autonomous pipeline: Alert Ingestion → Triage (<5s) → Diagnosis → Remediation → PR ↓ ↓ ↓ ↓ P0-P3 labels Code context >85%? Auto-merge

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Every action is gated by confidence thresholds, blocked-path rules, and a complete audit trail.

Challenges we ran into

• The confidence problem → How do we know when to trust the AI? We solved this with hybrid scoring: code similarity (Redis), historical success rate, and LLM self-assessment. Below 85% → human review. Never false-auto-merge.

• Latency vs. accuracy → Groq is blazing fast (~10-20x slower models), but complex diagnosis needed more tokens. We optimized prompts to balance speed and depth, hitting <30s total resolution.

• Security paranoia → We couldn't let AI touch auth.py or secrets.yml. Built a blocked-paths system that scans every remediation before execution. Also added full SQLite logging — every action is replayable.

• Context window limits → Codebases are huge. We implemented smart context extraction: only relevant files + recent incidents, not the entire repo.

Accomplishments we're proud of

• ⚡ <30 seconds from alert to pull request (80% faster than industry average)
• 🎯 95% severity accuracy across P0-P3 incidents
• 💰 $4K saved per hour of avoided downtime
• 🔒 Zero security breaches in 500+ simulated incidents
• 📊 Full transparency — every decision visible in real-time dashboard
• 🚀 5-minute setup from clone to first incident

We proved that autonomous remediation isn't science fiction — it's deployable today.

What we learned

• Confidence thresholds matter more than accuracy → A 99% accurate model that's wrong 1% of the time will still break production. The 85% gating + human review pattern is the real innovation.

• Speed is a feature → Engineers won't wait 2 minutes for an AI. Groq's inference speed made <30s possible, which made people actually want to use AIRA.

• Audit trails build trust → The SQLite log + WebSocket feed turned "scary AI" into "transparent assistant." Teams need to see why decisions were made.

• Simple > complex → We started with a massive architecture diagram. Ended with 5 phases, 3 confidence levels, 2 outcomes (auto-PR or human review). Simplicity wins.

What's next for AIRA

We're also exploring fine-tuned smaller models for on-prem deployments where cloud LLMs aren't allowed.

Try it today: git clone https://github.com/piyush080205/aira.git

MIT Licensed · Built for hackathons, ready for production

Built With

• fastapi
• groq
• langchain
• postgresql
• python
• react
• supabase