Production AI Reliability: How Detective, Diagnostician, and Predictive Agents Work Together

Juan Petter — Mon, 08 Dec 2025 16:37:15 +0000

Over the past few weeks I've been building an agentic reliability engine designed to do what traditional monitoring tools rarely accomplish:

Detect failures early, understand why they're happening, predict the blast radius—and self-heal automatically.

Below is the full architecture and real screenshots from the working demo.

🏗️ System Architecture

The pipeline uses a multi-agent system:

🕵️ Detective Agent — Anomaly Detection

Continuously monitors telemetry (latency, errors, memory, CPU, throughput) and flags deviations with confidence scoring.

🔍 Diagnostic Agent — Root Cause Analysis

Builds a causal snapshot using FAISS memory, recent deployment diffs, dependency health, and incident similarities.

🔮 Predictive Agent — 15-Minute Failure Forecasting

Estimates time-to-crash, risk level, and expected business impact.

⚖️ Policy Engine — Thread-Safe Circuit Evaluation

Checks reliability rules, budget constraints, SLA thresholds, and determines whether to trigger auto-healing.

🤖 Automated Healing Actions

If risk exceeds policy limits, the framework triggers:

🔄 Restart
↩️ Rollback
📈 Scale Up
🛑 Circuit Break

All actions are tracked and fed back into a FAISS memory layer for model improvement and ROI calculations.

📊 Real-Time Demo — Business Impact Dashboard

The dashboard displays:

🟣 Total Incidents Analyzed
🛠️ Auto-Healed Incidents
⚡ Time Improvement vs Industry
💰 Revenue Saved
⏱️ Detection Time
📉 Response Benchmarks

Example from a recent run:

Industry Avg Response: 14 minutes
ARF Response: 2.3 minutes
Result: ~6× faster incident resolution

🧪 Example Scenario — Memory Leak Time Bomb

Telemetry:

Memory climbing 2%/hr
Current: 94%
Time to crash: ~18 minutes

Agent Verdict:

Confidence: 89.5%
Insights: latency spikes, error-rate jump, suspect recent deployments
Business Impact: \$119.17 / 6710 users at risk
Auto-Actions: restart, rollback, alert team, circuit break

📈 Early Traction

The public demo is already seeing organic traffic:

All-time visits: 279
Last month: 255
Last week: 91

And that’s before any formal announcement.

🚀 What’s Next?

Adding LLM-powered incident postmortems
Integrating OpenTelemetry ingestion
Deploying a Kubernetes operator version
Extending the predictive engine to multi-service cascades

If you're interested in reliability automation, agentic systems, or want to collaborate, I’d love to connect.

GitHub Repo:

https://github.com/petter2025/agentic-reliability-framework

LIVE DEMO: https://huggingface.co/spaces/petter2025/agentic-reliability-framework

DEV Community: Juan Petter