BIP AGENT GUARD

guardrail, testing, and monitoring platform that secures Generative AI

applications end‑to‑end. It combines rule‑based filters, ML detectors, LLM

reasoning agents, and user‑journey simulations to prevent unsafe outputs,

detect vulnerabilities, optimize UX, and ensure regulatory compliance.

Target Users

AI engineering teams, UX/product teams, compliance‑driven industries (finance,

healthcare, public sector), and any enterprise scaling LLM applications.

Customer Pain Solved

It addresses the inability of traditional testing to detect real‑world

failures, reduces UAT and maintenance costs, prevents hallucinations and

compliance breaches, identifies UX issues before launch, and provides

continuous protection and monitoring throughout the AI product lifecycle.

Detail:

• Description of the Offer

Agent Guard SaaS is a multi‑agent

guardrail and evaluation platform designed to secure, test, and

continuously monitor Generative AI applications in real‑world conditions. It

integrates several categories of AI agents—including guard, supervisor,

adversarial, monitoring, explainability, and optimization agents—to protect AI

products and improve their performance across safety, compliance, UX

quality, and operational reliability.

• Type of User That Benefits from This Offer

A) AI Product Teams & Engineering Organizations: Teams

responsible for designing, building, deploying, and maintaining LLM‑based

systems benefit from continuous testing, guardrails, and automated

troubleshooting;

B) Companies operating in regulated or high‑risk sectors:

Healthcare, finance, public sector, and any domain where compliance and

misinformation risk is high that typically require Fact‑checking, PII

protection, Copyright and compliance enforcement, Multi‑modal moderation;

C) UX, Product & Customer Experience Teams: Because the

system simulates real users with real emotions, needs, and behaviors, it

directly benefits Product owners, UX researchers, Service designers, Customer

support quality teams;

D) Enterprises scaling LLM usage: Companies expecting

widespread internal or external adoption of AI agents and copilots benefit from

the consumption‑based, scalable model described in the deck.

• Customer needs & pain points addressed

A) Traditional GenAI evaluations fail to capture real user

behavior: current evaluation methods focus on correctness, not on user

journeys, leading to AI systems that seem fine in testing but fail in the wild:

Missed implicit user needs, Inability to keep users engaged, Incorrect

assumptions about user intent, Failed task completion even when answers seem

“correct”.

Agent Guard solves this by simulating diverse

personas—motivated, unmotivated, confused, expert, novice—to detect where the

AI fails to support real humans.

B) Need for continuous monitoring and defense in production:

Offline testing alone is insufficient and real‑time user behavior must be

observed to avoid failures in production environments. The solution monitors:

Model drift, Unsafe outputs, Regulatory risks, Hidden vulnerabilities, Latency

or token inefficiency issues, Reputational damage from hallucinations or

harmful content.

C) High cost and complexity of manual UAT and AI safety

testing: The solution provides automated multi‑agent testing, Persona × goal ×

channel matrix coverage, Insights, warnings, optimization suggestions, Cost‑per‑token

optimized testing and guardrails, Predictable consumption model for budgeting.

D) Enterprises need to comply with laws (GDPR, AI Act,

etc.): The solution evaluates if your agent respect compliance requirements,

the risks of PII exposure, need to prevent copyright violations, need for

governance over AI system behavior.