Quant's Agentic AI Fixes Chatbot Dead Ends, Resolving 77% in Real Time

Agentic AI That Ends Broken Loops in Customer Support

Customers get stuck. They bounce between bots and agents, repeat their story, and leave frustrated. Quant, a startup focused on AI-driven support, is aiming straight at that pain with an agentic AI system built to resolve issues end-to-end-without handing off or stalling out.

According to CMSWire, early results show a 77% real-time resolution rate. For support leaders under pressure to lift CSAT while cutting costs, that kind of autonomy is the point: fewer dead ends, fewer escalations, faster outcomes.

What Makes Quant Different

• Thinks and acts: It reasons through requests, plans steps, and executes actions-not just replies with suggestions.
• Works across systems: Pulls data via APIs, updates records, and coordinates across departments.
• Handles real tasks: Initiates refunds, schedules repairs, and closes loops without human intervention when safe to do so.
• Targets the "broken loop": Reduces the ping-pong between bot and agent that drives churn.

Early adopters in e-commerce and telecom report shorter resolution times and less queue pressure. Natural language processing interprets complex queries, while multi-step reasoning selects the right data and actions to finalize a case.

Why This Matters Now

Industry signals point to broad adoption. A Cisco study projects agentic AI will manage 68% of customer interactions by 2028. Gartner forecasts 80% of common issues handled autonomously by 2029 and a potential 30% drop in operating costs.

Public chatter mirrors the data. On X, practitioners highlight reduced manual oversight and smarter decisions from real-time data fusion. Leaders are asking how to capture lost revenue from missed calls and unqualified leads with proactive automation.

What Changes for Support Teams

• From firefighting to oversight: Agents focus on exceptions, empathy, and quality-AI closes routine loops.
• Higher containment with better outcomes: More resolved in-channel without sacrificing CSAT.
• Faster cycle times: Lower average resolution time and fewer back-and-forths.
• 24/7 reliability: Off-hours and peak surges handled without spinning up overtime.

Risk, Trust, and Guardrails

Autonomy raises fair questions: Can enterprises trust AI with nuanced interactions? What about errors, bias, or privacy exposure? These concerns are fixable with the right controls.

• Permissioning by action: Limit refunds, credits, and data edits by amount, context, and customer tier.
• Human-in-the-loop on edge cases: Route risky decisions or sentiment-sensitive moments to experts.
• Full audit trails: Log prompts, data access, and actions for review and compliance.
• Safe fallbacks: Instant escalation when confidence is low or policies conflict.
• Bias and regression testing: Continuously test on sensitive segments and monitor drift.
• Data minimization: Only fetch what's needed for the task; rotate credentials; enforce least privilege.

Implementation Playbook for CS Leaders

Don't try to automate everything. Start with the cases that annoy customers and clog your queues. Set guardrails, measure results, expand with confidence.

• Map high-volume intents to concrete actions: refunds, order status, plan changes, appointment scheduling.
• Define policies per action: thresholds, exceptions, required checks, and acceptable data sources.
• Integrate cleanly: API access to CRM, billing, logistics, knowledge, and authentication.
• Pilot in a narrow lane: One region or product line; compare against a control group.
• Baseline metrics before launch: FCR, AHT/ART, containment, escalation rate, CSAT/NPS, cost per contact.
• Train agents for new roles: Exception handling, empathy spikes, QA, and process improvement.
• Stand up a feedback loop: Agent notes, customer comments, and error reviews drive quick iteration.
• Align with legal and security: Data retention, consent, redaction, and vendor risk checks.

KPIs to Watch

• First Contact Resolution and Average Resolution Time
• Containment vs. CSAT/NPS (no trade-offs hidden behind bot walls)
• Escalation rate and reasons
• Cost per contact and agent utilization
• Refund accuracy, policy adherence, and error time-to-correction

Beyond Support

Agentic AI won't stop at customer care. Expect extensions into compliance checks, data quality tasks, and back-office workflows. For high-stakes decisions, McKinsey and others stress pairing AI with human judgment-AI accelerates, humans decide.

If your team is stuck in dead-end loops, the path forward is clear: automate actions where rules are stable, keep humans close where nuance matters, and scale what works.

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