— The definition AI Agent Governance is the discipline of proving that your AI agent meets your own definition of good — your standards, your policies, your guardrails — continuously, and to whoever needs to be satisfied: your board, your regulator, or your customer. It’s the difference between a policy you’ve written down and evidence…

— The definition AI Agent Testing is the discipline of proving, before a single customer is affected, that your agent behaves correctly across the scenarios that actually matter. It starts with deciding what to automate in the first place, and it’s grounded in how your customers really talk, not just the neat paths you imagined…

— The definition AI Agent Monitoring is the discipline of continuously seeing what your AI agent says to real customers — and how those customers feel about it — so you catch drift, errors, and poor experiences while they’re still small, instead of finding out from a complaint, a regulator, or a viral screenshot. You…

Your AI agent may already be resolving more customer conversations than your human team, but as the volume increases, so does the need for confidence. If the resolutions aren’t consistent and correct, your AI agent risks making incorrect decisions at scale. Containment data can tell you how often your AI agent handled a conversation without…

Every contact center leader we talk to who’s evaluating a new QA platform eventually asks the same question: how long is this actually going to take to set up? It’s a good question. Building an accurate scorecard from scratch, writing prompts for every line item, testing them against real conversations, and tweaking the wording until…

No two customers are the same. Some may have a straightforward query that you’re able to solve in a matter of moments. Others, meanwhile, may need extra care for any number of reasons – and recognizing these vulnerabilities is not just a matter of good service; it’s a necessity. In this blog, we’ll explore what…

With AI, automation, and agentic technologies becoming more widespread, contact centres are recognising the potential to do more with their QA data. There’s a growing appetite for change in how quality is measured and acted upon. With that in mind, we’ve collated the three big shifts every contact centre leader – and especially those in…

Many businesses are rushing to deploy AI – in fact, we’ve seen it already with Klarna and Duolingo missteps. The problem is, they’re often in a race to be the first to make efficiency (and cost) gains, while overlooking fundamental prerequisites. This approach risks implementing sophisticated technology on top of unresolved operational issues. And perhaps,…

We’ve said it before, and we’ll say it again: AI is an augmenter, not (yet) a human replacement. Recently, there has been a surge of bold declarations from high-profile companies around artificial intelligence. Some have announced sweeping changes to how they work – halting new hires, or restructuring entire teams – all in the name…

AI in the contact center is no new concept, but as the technology (and competition) has evolved, the pace of its development has sky-rocketed. But now that you can choose a variety of ways to interact with AI – and more specifically, Large Language Models (LLMs) – how do you go about it, and what…