AI agent deployment represents a fundamental shift in how customer service operations function, yet many enterprises continue to treat implementation as a finite IT project rather than an ongoing operational discipline. Csaba Tamas, Chief Product Officer at Parloa, argues that this mindset creates substantial risk through "agent drift"—the gradual misalignment between agent behaviour and evolving business conditions. When business policies, product definitions, and operational contexts change, agents built months or years prior continue operating against outdated parameters. The problem compounds as organisations scale: success with a single agent typically triggers demand for dozens more across different processes, multiplying the management burden. This raises a critical question for teams already running Agentforce or similar platforms: are your governance structures designed for continuous optimisation, or are they still configured around the deployment milestone?
The operational reality diverges sharply from how many teams currently measure success. Containment rates—the percentage of conversations handled without human escalation—have become the dominant metric, yet they obscure poor customer experiences and can be artificially inflated by simply refusing to escalate. Post-launch, the first three to six months prove critical for performance improvement, but organisations often lack the infrastructure to react quickly to emerging issues. Each new edge case discovered in live conversations requires a decision: secure fresh budget and reassemble project teams for minor modifications, or implement a self-service model where subject matter experts can adjust agent configurations directly. Testing discipline also transforms fundamentally; AI agents are non-deterministic, requiring hundreds or thousands of test iterations rather than single validation runs. Teams must shift from pre-deployment testing to continuous observability across customer conversations, monitoring containment alongside customer frustration, sentiment, anomalies, and escalation patterns. Should smaller vendors worry about this operational complexity becoming a competitive moat for larger platforms with built-in governance and observability tools?
The strategic implication is clear: AI agent ownership requires active operators embedded within customer service teams, not distant IT project managers. These operators need visibility into performance anomalies, authority to make rapid adjustments, and accountability for keeping agents aligned with business evolution. This represents a fundamental restructuring of how CX teams allocate resources—shifting investment from one-time builds toward continuous management, faster feedback loops, and sharper testing protocols. Organisations that treat agent deployment as project completion rather than operational inception will find themselves managing increasingly misaligned systems, whilst competitors who embed operators and establish rapid improvement cycles will maintain agent performance and customer satisfaction over time.
AI Agents Need Active Management, Not a One-Time Build CX Today