AI Agent Testing & Security

Red-team your call center AI agents before attackers do.

GraiBot stress-tests SIP voice agents and chatbots with automated jailbreaks, QA scenarios, and regression suites so your contact center AI performs safely in production.

Contact for Demo See how it works

Built for SIP-connected AI agents handling real customer calls at scale.

Built for teams operating AI in contact centers, customer support, healthcare, and financial services.

Three pillars for reliable AI agents

SIP voice stress testing

Test over SIP trunks and production-like telephony paths.
Measure latency, silence gaps, and barge-in handling.
Simulate accents, noise, and unstable call conditions.

Adversarial red-teaming

Prompt injection and jailbreak campaigns.
PII leakage and policy violation checks.
Social engineering and manipulation scenarios.

Continuous regression QA

Run golden conversation sets on every release.
CI/CD integrations with webhooks and APIs.
Automatic drift and quality alerts.

Why GraiBot?

Quality and reliability

Intent recognition accuracy.
Routing and escalation logic.
Latency and ASR degradation tests.

Compliance and PII

Unauthorized PII collection refusal.
Regulatory script adherence.
PCI and HIPAA boundary probing.

Adversarial resistance

Jailbreak and prompt injection coverage.
System prompt extraction protection.
Social engineering resilience.

Full-pipeline integration

Deploy with confidence using CI/CD gating or ensure ongoing quality with continuous production monitoring. GraiBot keeps your agent reliable, turn after turn.

PASSED Deploy proceeds

BLOCKED Deploy halted, findings surfaced

Service pages for core AI testing workflows

Explore purpose-built pages for QA, regression safety, adversarial resilience, and hallucination reporting for AI call center chatbots.

AI chatbot QA testing

Scenario coverage, intent routing validation, and conversation quality benchmarks before release.

Jailbreak and adversarial testing

Simulate prompt injections, policy evasion, and social-engineering attacks against voice and chat agents.

Regression and hallucination monitoring

Run golden-path suites continuously and track hallucination, drift, and safety regressions in production.

How it works

A simple four-step loop for continuous QA, adversarial testing, and deployment safety.

Define

Author test scenarios in our flexible YAML DSL or select from our pre-built adversarial library.

Execute

GraiBot places a real PSTN or SIP call to your agent, simulating human callers with varied personas.

Judge

The evaluation engine scores every turn against your rubric and cites evidence for each finding.

Integrate

Monitor production quality on a schedule or block unsafe deploys with your CI/CD gate.

From the GraiBot security blog

Practical guidance for teams deploying AI agents in call centers, finance, and healthcare.

March 8, 2026 · 8 min read

How to build a conversation-based adversarial test library for LLM chatbots

Single-turn jailbreak prompts are only a small part of modern chatbot risk. The hard failures now chain retrieval, memory, and tool use across multiple turns.

This guide shows how to turn incident evidence, OWASP LLM risk categories, and synthetic canaries into a reusable test library you can run on every release.

Read the full post

March 5, 2026 · 7 min read

What prompt injection means for regulated AI in call centers, finance, and healthcare

The same attack can have very different outcomes depending on domain context. A prompt injection in a regulated workflow often becomes a business-process security problem, not just an LLM problem.

The post outlines the controls, test cases, and escalation thresholds that matter when customer identity, payments, or PHI are involved.