Independent evidence from live systems on why a good score is not a ship decision
Most enterprises decide whether an AI agent can face customers based on three things: a single aggregate quality score, a hand-read sample of conversations, and the judgement of the team that built it.
However, a high score can hide the failures that matter most. An agent can rank well on answer quality and still give in to a prompt injection, skip a required safety escalation, or act outside its permissions.
Across seven independent assessments of live production agents, six were blocked from deployment, and most of them scored above 0.80.
This whitepaper gives AI leaders and risk owners the evidence and the clearance approach they need to decide which agents are ready to ship and which only look ready. QKTech’s GATES framework brings this discipline to enterprise AI. Our techniqk platforms, such as Nimbus, evaluate GenAI agents through API, live or offline inputs.
What’s Inside
- Why aggregate quality scores can rank agents correctly but still can’t decide whether to ship them
- Findings from seven assessments of live agents across airlines, ride-hailing, education, enterprise analytics and financial services
- The three failure classes behind every blocked deployment: adversarial resistance, task integrity and grounding
- A case study of an assistant that failed on every retrieval call and still passed every check its operator ran
- Five requirements for deployment clearance, and the eight disclosures every clearance should carry
Who Should Read This
- AI and ML leaders who sign off on agent deployments
- CIOs and CTOs accountable for customer-facing AI
- Risk, compliance and audit teams in regulated industries (BFSI, insurance, healthcare)
- AI assurance and quality engineering leaders building evaluation programmes
Download the whitepaper to access the full assessment results, the five clearance requirements, and the seven-stage assessment pipeline that turns a quality score into a defensible ship decision.
