Inside the Coralogix AI Center: Solving AI’s Silent Failure Crisis
Observability has always answered one core question: Is it running?
But in the era of LLMs, autonomous agents, and AI-powered workflows, that’s no longer enough. We need to ask a harder, scarier question: Is it right?
And right now, most teams can’t answer that.
Let’s fix it.
In our last post, “The AI Monitoring Crisis No One’s Talking About,” we outlined why prompt injection, hallucinations, and context drift create invisible failures. Failures that traditional observability simply misses.
This time, we’re moving past diagnosis and showing how teams are solving the problem today with the Coralogix AI Center.
Coralogix’s AI Center is one of the first observability solutions purpose-built for production AI systems, combining real-time evaluation, security visibility, and cost intelligence in one native experience.
During our recent webinar “Green Lights, Wrong Answers,” we took teams inside the AI Center and demoed live detection of failure modes, hallucination, prompt injection, and drift using real telemetry and real production logic. The response?
“This is the first AI monitoring solution that actually feels like it gets our pain.”
Watch the full webinar replay here to see it for yourself:
If you want a quick, concise summary, here’s what was shown, and how teams are finally getting real visibility into their AI systems in production.
Stick around for the bonus MCP Server demo that really got the audience going.
Meet the Coralogix AI Center
This isn’t just another dashboard. It’s a unified control plane for AI observability. Evaluation. Tracing. Security. Cost control. All in one place, all in real time. Built on OpenTelemetry.
AI Overview Dashboard: ROI at a Glance
Most monitoring tools ask: Is the server responding?
The AI Center asks: Is this AI doing what it’s supposed to?
The overview dashboard answers a fundamentally new set of questions:
- Is the model output correct?
- How are my users behaving?
- Is this AI investment delivering ROI?
- Can we correlate this with real business metrics?
AI-native requires new types of metrics which include:
- Quality scores per model and application
- Token usage trends and cost breakdowns
- Response time correlation with user experience
- Flagged interactions for hallucinations, PII leaks, or drift
This is where ops, security, and product teams converge around shared AI visibility – no more silos between infrastructure health and AI behavior.
You might be asking yourself how do we flag for hallucinations, sensitive data leaks, model drift etc etc. See below.
AI Evaluation Engine: Real-Time Model Intelligence
This is the brains of the AI Center.
Coralogix Evaluators are compact transformer-based SLMs (small language models) purpose-trained to evaluate production LLM output.
These aren’t generic models. They’re:
- Domain-specific, trained on your use cases and policy constraints.
- Independently validated, running outside your application model to avoid bias.
- Performance-proven, with 90% better detection accuracy for hallucination and off-topic content compared to baseline OpenAI models.
Since launch, Coralogix customers have identified over 2,500 prompt injection attempts with zero false positives, using our Topic Enforcement Evaluator alone.
Evaluators run live on every interaction. Think of them as smart CI tests for LLMs in real time, with real context.
Out-of-the-box evaluators include:
- Prompt Injection Detection
- Topic Enforcement
- Hallucination Detection
- Cost Abuse Protection
- PII and Security Checks
- And many more
Need something specific? Our team can deliver custom evaluators trained for your domain.
All evaluator scores are queryable In Coralogix via DataPrime, enabling precise alerts and historical trend analysis.
Full-Stack AI Observability with OpenTelemetry
Every interaction is fully instrumented with:
- Full prompt history (system and user)
- Model version, parameters, temperature
- Token count and latency
- Evaluator verdicts and reason codes
- Linked spans for downstream API calls or vector DB access
This is powered by LLM_Tracekit, part of the same OpenTelemetry Open Source standard that we know and love at Coralogix.
It ensures that you have:
- Zero vendor lock-in
- Unified telemetry across infra, app, and AI layers
- Full trace correlation in your Coralogix pipeline
For example: A single trace reveals the exact user prompt, model output, rejection reason from the evaluator, plus a downstream timeout on a semantic search call.
AI Security & Posture Dashboard
Security for AI isn’t just about IAM roles or HTTPS. It’s about understanding how your models behave under pressure and when users try to exploit them.
The AI Center’s security layer includes:
- AI posture scoring across all applications (AI-SPM)
- Live threat detection for prompt injection, prompt chaining, and cost abuse
- Shadow AI discovery from GitHub and API logs
Track and trend:
- Evaluator-triggered anomalies
- Rising attack patterns
- Severity-weighted risk scores
- Model behavior deviation over time
Risk scoring combines evaluator frequency, input type, and cost anomalies to produce a single metric for security posture per app.
Live Demo: Evaluators in Action
For the demo, I spun up a quick Flask app using OpenAI under the hood. The app translates natural language into DataPrime queries. Not the prettiest UI, I know! but it made the point.
Now for the test scenario: Say a user asks “Show me the McDonald’s menu” which is clearly an off-topic prompt for a query generator and quite frankly a waste of my tokens.
To stop this type of situation occurring we go to Coralogix’s AI Center, apply the allowed topics evaluator, set it to be that of ‘Dataprime’ and voila!
The topic enforcement evaluator flagged this as off-domain content and prevented the system from wasting tokens on irrelevant processing. In production, this protects against both malicious attacks and accidental misuse.
But we didn’t stop there.
Developer Integration with MCP Server
This is where we truly blew minds.
With our Model Context Protocol (MCP) Server, I opened up the Cursor IDE, and asked the built-in Assistant.
“Using the Coralogix MCP server show me the recent spans from the Dataprime assistant app”
And it worked – pulling live production data directly into my development environment.
You get:
- Live telemetry from production inside your IDE
- Evaluator verdicts and traces without leaving your editor
- Faster debugging with context-aware queries
Why this matters: Contextual telemetry inside your IDE collapses the feedback loop from hours to seconds. This is what ‘developer-first observability’ really looks like.
Imagine the developer workflow:
- A dev pushes a change
- Opens Cursor
- Says “Show me frontend eval score anomalies from yesterday”
- MCP pipes this to Coralogix
- You see exactly what changed – no dashboards required
- Results appear directly in the editor – no dashboard switching required
This isn’t just convenience, its production context delivered where developers actually live and work.
MCP integration is available now, connect your IDE and debug like never before. I will cover this in more detail in my next blog.
Don’t Just Monitor. Understand
Traditional monitoring tells you what ran.
AI observability tells you what went wrong, why it happened, and how to fix it.
With Coralogix’s AI Center, you can:
- Detect silent failures before they damage user trust
- Block exploit attempts in real-time with intelligent evaluation
- Trace AI behavior in complete production context
- Quantify ROI from your AI investments with business correlation
The future of observability is AI-aware. And it starts here.
You can’t improve what you can’t see.
Now you can.
Ready to get started?
- Watch the Webinar Replay – See every demo including live prompt injection detection
- Try the AI Center – Deploy your first evaluator in minutes
- Coralogix MCP Server – Connect your development workflow to production AI data
