← Back to Blog
AI OBSERVABILITY

AI Observability: Your CCTV for Every Agent Decision

How RagLeap built a 3-layer observability system to monitor every LLM call, detect hallucinations, and audit every business action in real-time.

By RagLeap Engineering • May 2026 • 8 min read

When you deploy an AI agent to handle real business operations — sending emails, updating CRMs, scheduling calls — you need to know exactly what it's doing and why. Without observability, you're flying blind.

At RagLeap, we built a 3-layer observability system that gives you complete visibility into every decision your AI agent makes.

The Problem: Black Box AI

Most AI platforms give you a chat interface and a response. What happens in between is invisible. Did the AI hallucinate? Did it call the right tool? Did it send that email you asked it not to send?

For consumer apps, this is acceptable. For business operations, it's dangerous.

Real example: An AI agent told a customer "I've sent your invoice" — but the email failed silently. Without observability, no one knew. The customer waited 3 days.

Layer 1 — LLM Call Tracing

Every call to Claude, GPT, or Gemini is logged to our AgentTrace model:

AgentTrace(
    workspace=workspace,
    agent_type="email_specialist",
    input_tokens=847,
    output_tokens=312,
    model="claude-sonnet-4",
    latency_ms=1240,
    tool_calls=["send_email", "log_lead"],
    hallucination_detected=False,
    created_at=now()
)

This gives you a complete audit trail. Every token, every tool call, every millisecond.

Layer 2 — Hallucination Detection

Our guardrails system scans every AI output before it reaches the user or triggers an action. It detects:

  • Phantom confirmations ("I've sent the email" when no email was sent)
  • Future tense claims ("will be processed" used as confirmation)
  • Tool invocation claims without actual execution
  • Confident assertions about uncertain information
False positive handling: We explicitly exclude legitimate execution patterns like [EXECUTE_ACTION:] from hallucination detection — so real actions are never blocked.

Layer 3 — Real-Time Dashboard

The observability dashboard at /observability/ shows:

194
Actions Registered
Live
JSON Endpoint
3
Retry Attempts

The Adaptation Loop

When an action fails, RagLeap doesn't give up. The adaptation loop:

act() -> verify()
  if fail:
    heal() -> re-plan(error_context) -> retry()
    max 3 attempts per action

Every attempt is traced. Every failure is logged. Every recovery is auditable.

Why This Matters for Enterprise

Hospitals, banks, and law firms can't deploy AI without audit trails. Our 3-layer observability system provides:

  • Compliance: Every action logged with timestamp, user, and context
  • Debugging: Trace any issue back to the exact LLM call
  • Trust: Show clients exactly what the AI did and why
  • Control: Catch hallucinations before they cause damage

See It Live

Try RagLeap free — full observability dashboard included on all plans.

Start Free Trial