Who This Is For

Built for CX leaders, support ops, product teams, and CTOs exploring:

• Why their AI support automation still depends on human backup

• The difference between chatbots, RAG, AI agents, and Agentic AI

• “What’s the best AI solution for resolving support tickets end-to-end?”

• “Why chatbots—and even modern AI systems built on retrieval—fail to deliver true automation”

• “What Agentic AI is, and how it enables fully autonomous, end-to-end resolution of support tickets”

The Support Playbook is Broken

You’ve likely experienced it:

A customer asks about a refund.

They get transferred three times.

Two days pass.

They leave a 1-star review.

This isn’t rare — it’s the norm.

The traditional model of customer support—tiered agents, outsourced queues, macros, and SLAs—was designed for a world where support was a cost center. But customer expectations have fundamentally changed. A 2024 survey by Zendesk found that 72% of customers expect personalized, real-time support. Yet, the majority of companies are still relying on reactive, ticket-based workflows that create friction instead of resolution.

• ⏳ Wait Times: Average first response time via live chat: 2+ hours (Freshdesk 2024 Benchmark)

• 🔁 Repetition: 42% of users say they repeat themselves when handed between agents (Forrester)

• 💸 Cost: Gartner estimates Tier 1 tickets cost ~$5, while Tier 2-3 support costs can exceed $50 per ticket.

And all for simple issues like:

• “Why hasn’t my refund arrived?”

• “How do I cancel my subscription?”

• “Where is my order?”

The support experience is overdue for an overhaul.

Why the L1–L2–L3 Model No Longer Works

Traditional Breakdown:

This model introduces delays and disconnects:

• Customers are handed off across layers, repeating context.

• L1 agents are often undertrained or overloaded.

• Escalations take hours or days, often with no visibility.

Modern companies can no longer afford these inefficiencies.

In 2025, customers expect instant answers, not slow escalations.

Example: A fintech user facing a failed transaction may wait 2+ days for L2 to investigate—a deal-breaker when money is involved.

This is where automation should help—but it hasn’t.

Why Today’s Bots—Even with AI—Still Can’t Resolve

Chatbots were supposed to solve the scale problem. Instead, they’ve created new ones.

The promise was automation. The reality? A sea of broken flows and dead ends.

Common Pitfalls:

• Rigid Logic: Most bots use rule-based flows ("if this, then that"), which break under real-world complexity.

• Poor Comprehension: Many can't handle ambiguous questions or multi-step asks.

• No Actionability: Bots can't interact with backend systems to actually do anything.

Even LLM Bots Fall Short

Vendors started adding LLMs on top, but it’s just lipstick on a legacy system. These bots still:

• Struggle with goal decomposition (“cancel and refund my last order”)

• Lack autonomous action-taking

• Fail to use context from past interactions

They’re just smarter search engines. They can answer questions. But they can’t solve problems.

And for those trying to go one step further…

The RAG Problem: Why Retrieval-Augmented Generation Isn’t Enough

Some vendors shifted to RAG—Retrieval-Augmented Generation—thinking that better answers would mean better support.

It helps summarize documents, sure. But support isn’t a reading comprehension test—it’s a decision-making engine.

Key Limitations in Customer Support:

Example:

User: “Why didn’t I receive my refund?”

RAG Bot: “As per our refund policy, refunds take 5–7 business days.”

❌ But the system never checks whether the refund was even processed.

What RAGless Support Looks Like

A RAGless system doesn’t retrieve documents at inference time. Instead, it uses structured, pre-processed knowledge and direct backend integrations.

Features of RAGless AI:

• Real-time user memory (intent, history, status)

• Fine-grained knowledge graphs that evolve with product changes

• Built-in ability to trigger actions, not just return answers

• Guardrails for compliance, escalation, and fallback logic

• Escalate with full diagnostic history only when truly needed

Analogy:

RAG is like Googling every time a customer asks something.

RAGless is like hiring a smart agent who knows the systems, knows the customer, and just gets it done.

Meet Fini: RAGless Agentic AI for Customer Support

Fini is built from the ground up as a RAGless agentic AI platform, which allows it to:

• Understand the user’s goal—not just their words

• Trigger backend flows (refunds, updates, onboarding) across systems

• Maintain memory across sessions

• Escalate only when necessary, with full context

Learn how Fini works → https://www.usefini.com/product/platform

Fini works with your stack:

• Zendesk, Salesforce, Intercom, HubSpot

• Custom APIs

• Suggest-only → Action with confirmation → Full autonomy

• Escalate with full audit trails

RAGless in Action: Real Support Scenarios

Fini is deployed by companies like Qogita, TrainingPeaks, and Column Tax. Here’s what automation looks like:

It replaces L1 and automates much of L2:

• 🧾 “Where is my order?” → Fetches shipping data, notifies delay

• 💳 “Update card & cancel” → Handles both actions in sequence

• 💰 “I was charged wrongly” → Verifies billing, triggers refund

Scenario Comparison

Read more on how Fini works →

Real-World Results With Fini

• 80%+ automation rate

• CSAT lift of 18% post-Fini

• 70%+ reduction in agent headcount required

For CX Leaders and CTOs: What to Ask Before You Buy

Is this system...

• Capable of taking backend actions?

• Using memory to resolve across sessions?

• Reasoning across systems and user data?

If not — it’s likely a chatbot with search, not a real AI agent.

You don’t need “smarter replies.”

You need faster resolutions.

The Future Is Agentic. The Future Is RAGless.

The next generation of support isn't reactive — it's autonomous, action-based, and always on.

If your AI support still runs on basic AI chatbots, it’s time to switch to something better. If your goal is cost-efficient, brand-aligned, scalable support—then you need more than a chatbot or a vector search engine. You need RAGless, agentic AI.

Fini is built to:

• Make decisions

• Maintain user memory

• Understand platform nuances

• Scalable, 24/7 resolution — no human needed

Fini is built for action. Built for accuracy. Built for autonomy.

See the Future of AI Support: Getting Started With RAGless Support

You don’t need to replace your stack. Fini integrates with existing stack across Zendesk, Intercom, HubSpot, LiveChat, Salesforce, Gorgias, Front, Freshdesk, and Custom APIs

No need to rip out your existing stack.

Start simple:

• Suggest-only mode

• Then enable action with confirmation

• Finally, move to autonomous resolution with policy guardrails

Fini powers companies like Qogita, Column Tax, and TrainingPeaks, automating 80%+ of tickets, end-to-end, with zero human touch.

Want to see how it works?

Book a demo or explore the Fini platform.