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AI & Business Automation

AgentOps - Monitoring & Control for AI in Production

A management platform that gives full visibility into AI agents in production - run tracking, cost control, failure detection, and smart real-time alerts.

Full AI visibility & control
app.agentops.io/dashboard
AgentOps - Monitoring & Control for AI in Production dashboard

AgentOps - Monitoring & Control for AI in Production - Main Dashboard

app.agentops.io/feature
AgentOps - Monitoring & Control for AI in Production feature view
PROJECT OVERVIEW

Project Overview

CLIENT

B2B SaaS for AI Teams

TIMELINE

14 weeks

ROLE

Full-Stack Architect

Companies running AI agents in production have no way to see what is working, what is failing, and how much it all costs. I built AgentOps - a monitoring platform that gives leadership and operations teams full visibility into AI performance, spending, and reliability.

THE CHALLENGE

The Challenge

Visibility Gap

AI agents operate as black boxes - when something goes wrong, nobody can tell what happened or why.

Cost Tracking

AI API costs spiral out of control without clear attribution to specific agents, tasks, and business units.

Multi-Tenant

Each customer organization needs completely isolated data, dashboards, and alerts - no cross-contamination between accounts.

Scale

The platform must handle thousands of AI agent runs per minute while keeping dashboards responsive in real time.

THE SOLUTION

The Solution

A monitoring platform purpose-built for AI operations - giving teams live dashboards, cost breakdowns, and automatic alerts so problems are caught before they impact the business.

TRACES

Full Run History

See exactly what every AI agent did step-by-step - inputs, outputs, timing, and where things went wrong - for any run, any time.

METRICS

Live Performance Dashboards

Real-time visibility into success rates, response times, costs, and usage across every agent and team in the organization.

ALERTS

Proactive Alerting

Automatic detection of failure spikes, cost overruns, and performance drops - alerts reach the right people before customers notice.

EVALS

Quality Scoring

Continuously measure AI output quality against expected results, with automatic detection when quality starts to slip.

TECH STACK

Technology Stack

Backend

NestJSTypeScriptPostgreSQLRedisBullMQ

Frontend

Next.js 14Tailwind CSSRadix UIRecharts

Observability

OpenTelemetryPrometheusSwagger/OpenAPI
RESULTS

Results

0%

Platform uptime

<0ms

Ingest latency

0M+

Runs/month capacity

0%

Cost savings found

NEXT STEPS

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AgentOps - Monitoring & Control for AI in Production | Client Success Story - CoreSysLab