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Agentic AI refers to artificial intelligence systems that can autonomously plan, make decisions, and take actions to achieve goals without step-by-step human instruction. In contact centers, this means AI agents that do not just assist human agents but actually handle customer interactions independently: answering questions, resolving issues, processing transactions, and escalating only when necessary.
Gartner reported a 1,445% surge in inquiries about multi-agent AI systems in 2025. McKinsey identifies customer care as the number one deployment area for agentic AI. The technology is not theoretical. It is being deployed now, and it is reshaping how contact centers operate.
But here is the question no one is answering well: when an AI agent handles a customer call autonomously, who audits that interaction for quality, compliance, and accuracy?
The term "agentic" distinguishes a new class of AI from the chatbots and IVR systems contact centers have used for years. The difference is autonomy.
Traditional AI in contact centers follows predefined scripts and decision trees. A chatbot answers FAQ questions from a knowledge base. An IVR routes calls based on DTMF inputs. A sentiment analysis tool flags negative calls for human review. These systems react to inputs but do not independently plan or act.
Agentic AI systems operate differently. They can:
In practice, this means an agentic AI system can receive a customer call about a billing dispute, pull up the account history, identify the discrepancy, determine the appropriate resolution based on company policy, apply a credit, confirm with the customer, and close the interaction without a human agent touching it.
The adoption drivers are straightforward.
Cost pressure. Contact center labor is the largest operational expense. In India's BPO industry, which employs over four million people, agent salaries, training, and attrition costs account for 60 to 70% of operating budgets. AI agents that can handle routine interactions at a fraction of the cost per contact are economically compelling.
Scale demands. Customer expectations for instant, 24/7 support continue to rise. Staffing for peak volumes means overstaffing during troughs. AI agents scale instantly without scheduling constraints, overtime, or shift differentials.
Consistency. Human agents have good days and bad days. Their performance varies with fatigue, mood, training quality, and experience. An agentic AI system delivers the same quality of interaction at 3 AM that it delivers at 3 PM.
Speed to resolution. AI agents do not need to put customers on hold while they search a knowledge base or consult a supervisor. Response times drop from minutes to seconds.
McKinsey projects that agentic AI could automate 30 to 50% of routine contact center interactions within the next three to five years. For BPOs, this is not a distant future scenario. It is a near-term operational reality.
Here is where the industry has a gap. Contact centers have spent decades building quality assurance programs for human agents: scorecards, calibration sessions, coaching frameworks, compliance monitoring. These programs assume a human is on the call.
When an AI agent handles an interaction, the existing QA infrastructure does not apply. No one is scoring the AI's calls against a QA scorecard. No one is monitoring whether the AI delivered required compliance disclosures. No one is checking whether the AI's responses were accurate, appropriate, and aligned with brand voice.
This creates several risks:
Compliance exposure. In regulated industries, every customer interaction must meet specific disclosure and consent requirements. An AI agent that skips a required disclaimer exposes the organization to regulatory liability. Under India's Digital Personal Data Protection (DPDP) Act, mishandling personal data in a customer interaction carries penalties up to 250 crore rupees, regardless of whether a human or AI handled the call.
Accuracy risk. AI systems hallucinate. They generate confident-sounding responses that are factually wrong. In a contact center context, this means an AI agent might quote incorrect pricing, misstate policy terms, or provide inaccurate technical guidance. Without monitoring, these errors persist undetected across thousands of interactions.
Brand and experience risk. An AI agent that responds with the wrong tone, uses inappropriate language, or fails to show empathy in a sensitive situation damages the brand. Unlike a human agent who gets coached after a bad call, an unmonitored AI agent repeats the same mistake on every similar interaction.
Bias and fairness risk. AI systems can exhibit biases in how they handle different customer segments, languages, or issue types. Without auditing, discriminatory patterns can persist unnoticed.
The solution is not to avoid agentic AI. It is to audit it with the same rigor applied to human agents, and ideally more.
100% interaction coverage becomes non-negotiable. For human agents, sampling 2 to 5% of calls and extrapolating quality is an accepted compromise. For AI agents handling thousands of interactions per hour, sampling is insufficient. You need to evaluate every interaction because AI errors are systematic: if the AI makes a mistake in one interaction, it is likely making the same mistake in every similar interaction.
QA scorecards need an AI version. The criteria for evaluating an AI agent overlap with but differ from human agent scorecards:
Continuous monitoring, not periodic reviews. Human QA is typically reviewed in batches. AI QA needs to be continuous because a model update, a knowledge base change, or a prompt modification can alter AI behavior across all interactions instantly. Something that was working yesterday can break today.
Feedback loops must close faster. When you identify a quality issue with a human agent, you schedule a coaching session. When you identify a quality issue with an AI agent, you need to fix the system immediately because the error is being replicated at machine speed.
Here is a practical framework for contact center leaders deploying agentic AI alongside human agents.
Layer 1: Automated QA for all interactions. Deploy a conversation intelligence platform that evaluates every interaction, whether handled by a human or AI agent, against your QA criteria. Use automated call scoring to apply consistent evaluation standards across both human and AI agents at scale. The same scorecard logic that evaluates human agents should evaluate AI agents, with additional criteria for AI-specific risks like hallucination.
Layer 2: Anomaly detection. Set up alerts for statistical outliers in AI performance. If the AI's compliance score drops by more than 5% in a 24-hour period, if resolution rates for a specific issue type decline, or if customer satisfaction scores for AI-handled interactions diverge from human-handled ones, the system should flag it immediately.
Layer 3: Human-in-the-loop review. Not every AI interaction needs human review, but a structured sample should receive it. Focus human review on interactions where the AI's confidence score was low, where the customer expressed dissatisfaction, or where the AI took an unusual resolution path.
Layer 4: Compliance-specific monitoring. For regulated industries, build dedicated compliance checks that verify every AI interaction meets disclosure and consent requirements. This is not optional. It is the cost of deploying agentic AI in a regulated environment.
Layer 5: Periodic adversarial testing. Regularly test your AI agents with edge cases, ambiguous scenarios, and adversarial inputs. This is the equivalent of calibration sessions for human agents, ensuring the AI handles unusual situations appropriately.
The most practical approach is extending your existing QA infrastructure to cover AI agents rather than building a parallel system. If you already have a quality assurance program with defined scorecards, a conversation intelligence platform that evaluates calls, and a team that reviews flagged interactions, the extension to AI agents is incremental.
The key additions are:
Contact centers that audit both human and AI agents through the same platform gain a unified view of quality across their entire operation. They can compare human and AI performance on identical interaction types, identify where AI outperforms humans and vice versa, and make data-driven decisions about which interactions to automate.
Gistly provides the quality assurance infrastructure that makes agentic AI deployment responsible and auditable.
100% coverage of all interactions. Gistly evaluates every conversation, whether handled by a human agent, an AI agent, or a combination of both. The same QA criteria apply uniformly, creating a consistent quality standard across your entire operation.
Custom scorecards for AI-specific risks. Build evaluation criteria that include hallucination detection, compliance verification, escalation accuracy, and knowledge base alignment alongside standard quality measures.
Real-time compliance monitoring. For BPOs operating under the DPDP Act or other regulatory frameworks, Gistly monitors every interaction for required disclosures, consent verification, and data handling compliance, regardless of whether a human or AI handled the call.
Multilingual support. As agentic AI systems deploy in Indian contact centers handling Hindi, Tamil, Telugu, and code-switched conversations, Gistly's 10+ language support ensures quality monitoring covers every language.
48-hour deployment. Connect Gistly to your existing telephony and AI platforms and begin monitoring within 48 hours.
What is agentic AI in a contact center? Agentic AI refers to AI systems that can autonomously handle customer interactions by understanding context, making decisions, taking actions in connected systems, and resolving issues without human intervention. Unlike traditional chatbots that follow scripts, agentic AI can plan multi-step resolutions, adapt to unexpected situations, and escalate only when necessary.
How do you ensure quality when AI agents handle customer calls? Apply the same quality assurance rigor to AI agents that you apply to human agents, with additional criteria for AI-specific risks. Deploy a conversation intelligence platform that evaluates 100% of AI-handled interactions against your QA scorecard, including checks for accuracy, compliance, hallucination, and appropriate escalation. Continuous monitoring is essential because AI errors are systematic and scale instantly.
What are the risks of deploying agentic AI without oversight? The primary risks are compliance violations (missing required disclosures or mishandling personal data), accuracy errors (AI hallucinating incorrect information), brand damage (inappropriate tone or responses), and bias (discriminatory treatment of different customer segments). Without monitoring, these errors persist across thousands of interactions because the AI repeats the same mistake on every similar case.
Can existing QA tools monitor AI agents? Most traditional QA tools were designed for human agent evaluation and require modification to handle AI agent monitoring. The most effective approach uses a conversation intelligence platform that can evaluate both human and AI interactions through the same framework, with additional AI-specific criteria like hallucination detection and knowledge base alignment.
How does the DPDP Act apply to AI agents in Indian BPOs? The DPDP Act applies to all processing of personal data, regardless of whether a human or AI system handles it. AI agents that collect, process, or share customer data must comply with the same consent, disclosure, and data protection requirements as human agents. Organizations are liable for violations regardless of the agent type, with penalties up to 250 crore rupees.
Deploying AI agents? Make sure someone is watching. See how Gistly audits 100% of interactions, whether handled by humans or AI, with the same quality standards. Request a free demo →
Gistly audits every conversation automatically — compliance flags, QA scores, and coaching insights in 48 hours.