Containment Rate: The metric that reveals your AI's performance
Moveo AI Team
February 27, 2026
in
🤖 AI automation
High containment rate. For many operations leaders, that number feels like a win. When 80% of interactions are resolved by AI without routing to a human agent, the technology seems to be working. The dashboard looks great. The executive report, too.
But there's a quiet problem buried in that logic: a customer who tried to resolve a debt didn't understand the AI's response, and simply gave up, also "contained" that conversation. No human agent was triggered. The rate went up. Recovery did not.
According to Gartner, agentic AI will autonomously resolve 80% of common customer service issues without human intervention by 2029, leading to a 30% reduction in operational costs. That's a powerful projection, but it hinges on a critical assumption many teams still overlook: containment and resolution are not the same thing.
This article explains what the containment rate is, how to calculate and interpret it correctly, where it fails when analyzed in isolation, and what separates operations that truly perform from those that only appear to.
What is containment rate (and where the definition stops short)
Containment rate measures the percentage of interactions initiated with an AI system that are completed without needing to transfer to a human agent. The formula is straightforward:
Containment Rate (%) = (Interactions Resolved by AI ÷ Total Interactions Initiated) × 100
Example: if your AI handles 1,000 conversations and 750 end without escalating to a human, your containment rate is 75%.
That much is clear. The problem starts when teams treat this number as the primary indicator of AI success, because containment rate measures the absence of escalation, not the presence of resolution.
There is a fundamental distinction every operations leader needs to internalize:
Every resolution contains. But not every containment resolves.
An AI that delivers a vague, incomplete, or irrelevant answer, but the customer walks away without requesting a human, technically contains that interaction. This is what the market calls bad containment: high rate, low quality, frustrated customers who don't come back, or who come back with the same problem.
In collections operations, this effect is even more critical. An AI with a high containment rate that fails to confirm payment arrangements, handle debtor objections, or accurately log negotiation status creates an illusion of efficiency that directly undermines recovery rates.
For a deeper look at how AI architecture affects collection operations, see Which Collection Strategy Works Best?, an article that addresses exactly this tension between scale and quality.
How to calculate and interpret containment rate correctly
Calculating the containment rate is simple. Interpreting it correctly requires context.
Step-by-step measurement guide:
Define what counts as an interaction: user-initiated conversations, not system pings or background sessions.
Identify what counts as an escalation: transfer to a human agent, explicit request for human support, or interactions flagged as unresolved by the AI itself.
Divide the number of contained interactions by total interactions and multiply by 100.
Segment by intent: the aggregate rate hides a lot. An AI may achieve 90% containment for balance inquiries and 40% for debt renegotiation. The consolidated number doesn't tell that story.
Performance benchmarks by maturity level:
Operation Maturity | Typical Containment Rate | Profile |
Basic bots (rule-based) | 20–40% | Limited intent coverage |
Mature CS platforms | 65–75% | Structured NLP, updated knowledge base |
Leaders in e-commerce/fintech | 80–90% | AI with context, personalization, and memory |
Gartner target (autonomous agents) | up to 80% (2029) | Autonomous resolution without human intervention |
Sources: Botpress, Alhena AI, Quiq Benchmarking Report, Gartner (March 2025)
None of these numbers has meaning in isolation. Containment rate must be cross-referenced with at least two other indicators to reveal whether the operation is actually performing:
CSAT (Customer Satisfaction Score): If containment rises but CSAT falls, the AI is deflecting customers, not resolving their issues.
Recontact rate: customers who return with the same issue within 24 to 72 hours are the clearest proof that the previous interaction went unresolved, even if the containment rate says otherwise.
The real KPI isn't how many customers you prevented from reaching an agent. It's how many actually resolved the problem they came with.
The danger of false containment in collections and customer service
In general customer service operations, false containment creates dissatisfaction. In collections, it creates direct revenue loss.
Picture a debtor who reaches out through a digital channel, questions their debt balance, receives a generic AI response that fails to access the correct contract history, and closes the conversation without an agreement. For the system, that interaction was contained. For the operation, it was a lost recovery opportunity.
Industry data makes this clear: according to Sedric, next-generation voicebots are already handling balance inquiries, promise-to-pay (PTP) confirmations, and dispute logging with high containment rates, but only when designed with a focus on contextual resolution, not just volume deflection.
The gap between leaders and followers in this context isn't the containment rate itself. It's the architecture behind it. Companies that track only containment fall behind those that track resolution quality in context.
A complementary indicator that helps expose this gap is the Automated Resolution Rate (ARR): the percentage of conversations that were both contained and satisfactorily resolved. To calculate it, you review a sample of transcripts, classify each as resolved or not, and apply that percentage to the total conversation volume. This transforms the containment rate from a volume metric into a quality metric.
Containment with memory: what separates the leaders
There is a clear pattern among operations that consistently outperform containment and resolution benchmarks alike: they use AI with contextual memory.
An AI without memory treats every interaction as if it were the first. Even if the customer negotiated a payment plan last week or logged a complaint last month, the agent starts from scratch. The result is frustrating for the customer and inefficient for the operation.
An AI with memory, on the other hand, knows the customer's delinquency history, preferred communication channel, prior contact attempts, and current negotiation stage. As we explored in What is a Debt Collection AI Agent?, agents that learn from past interactions and anticipate future needs set a new standard in customer engagement. Every new interaction is a continuation, not a reset.
This is exactly the principle behind Moveo.AI's approach.
The platform's agents with memory operate through what we call the Memory Layer: an intelligence layer that accumulates and connects each customer's history across all interactions, on all channels. As a result, containment stops being a deflection metric and becomes an indicator of compounded resolution, as detailed in The Champion Strategy for Debt Collection.
The concept of Compounding Intelligence applies directly here: with each successful interaction, the system learns. Resolution rates grow cumulatively. When the AI also has access to behavioral data and payment history, it can assess each debtor's propensity to pay and adjust the approach before the conversation even begins.
A concrete example of this in action: in a large-scale Latin American telecommunications operation, Mobi2Buy leveraged Moveo.AI's Memory Layer to manage 200,000 monthly conversations with a 76% resolution rate and 51,000 debt settlements per month, while performing 2x more efficiently than traditional chatbots.
High containment is only a win when paired with high resolution. Without contextual memory, you're measuring deflection, not performance.
How to optimize your containment rate: 5 practical actions
For leaders who want to turn containment into a genuine resolution, here are five actions with direct impact on operational quality:
1. Audit escalations by reason, not just by volume
Knowing that 25% of interactions escalated isn't enough. You need to understand why. Missing intent coverage? Incorrect AI response? Explicit user request? Each root cause has a different fix.
2. Update the knowledge base with real interaction data
Rule-based models go stale quickly. The knowledge base needs to be continuously fed with the actual questions, objections, and language patterns real customers use, not idealized FAQs.
3. Segment containment rate by intent
An aggregate rate of 70% may hide 95% performance on simple queries and 40% on renegotiation cases. Breaking down the analysis by interaction type reveals exactly where the AI needs reinforcement.
4. Cross-reference containment with CSAT and recontact rate
These three indicators together form the automation quality triangle. If containment climbs while CSAT falls and recontacts increase, the system is deflecting customers, not solving their problems. BlueTweak recommends treating these three KPIs as an inseparable set in any mature AI operation.
5. Implement contextual memory in your interactions
Agents with memory don't just resolve better, they learn from every interaction.
With the customer's history accessible in real time, the agent personalizes the approach, anticipates objections, and increases first-contact resolution rates. AI Voice for Debt Recovery illustrates how voice AI agents using sentiment analysis and account history can operate with near-100% reliability on defined tasks, precisely because they carry contextual memory into every call.
Is your operation ready to measure resolution, not just containment? Assess your AI maturity with our Readiness Tool →
Containment rate is a mirror, not a trophy
Containment rate is one of the most revealing metrics in an AI operation, but only when interpreted correctly. Used in isolation, it can create an illusion of efficiency that masks serious problems in quality, satisfaction, and recovery.
Operations that truly lead don't chase 100% containment. They chase 100% resolution. Getting there requires AI with memory, cross-referenced quality metrics, and continuous redesign based on what real customers actually need, not just what the dashboards report.
The difference between deflecting and resolving looks subtle in the report. In the operation's bottom line, it's the difference between a pretty metric and an operation that actually grows.
Ready to see how agents with memory transform your operation's performance? Talk to our team.