AI Support Guides
Jun 25, 2025
Deepak Singla
IN this article
Traditional customer support, built around tiered L1/L2/L3 agents, macros, and ticketing systems, is slow, expensive, and frustrating for both customers and companies. While chatbots and RAG-based AI systems promised automation, they consistently fail to resolve real issues due to their lack of actionability, memory, and contextual understanding. The future lies in agentic AI—systems that can reason, take action, and autonomously resolve issues end-to-end. That’s why leading fintech and e-commerce CX teams are moving toward RAGless, agentic AI platforms like Fini, which deliver faster resolution, higher CSAT, and significant cost savings.
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:
Level | Role | Issue Types |
|---|---|---|
L1 | Generalists | FAQs, common issues |
L2 | Specialists | Technical errors, account issues |
L3 | Engineers/Product | Bugs, integrations |
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:
Limitation | Real-World Impact |
|---|---|
Statelessness | No memory across chats, can't personalize |
No action layer | Can't trigger refunds, verifications, etc. |
Hallucination risk | Inaccurate answers when docs are missing or unclear |
Prompt fragility | Expensive prompt tuning, constant debugging |
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.
Feature | RAG Bot | RAGless Agentic AI |
|---|---|---|
Personalized memory | ❌ | ✅ |
Action-taking | ❌ | ✅ |
Handles multi-intent | ❌ | ✅ |
API integrations | ❌ | ✅ |
Consistent resolution | ⚠️ | ✅ |
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
Capability | Chatbot | RAG | Fini (RAGless) |
|---|---|---|---|
Multi-intent handling | ❌ | ❌ | ✅ |
Action execution | ❌ | ❌ | ✅ |
Memory & personalization | ❌ | Partial | ✅ |
Cross-system reasoning | ❌ | ❌ | ✅ |
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
Request | Chatbot | RAG | Fini |
|---|---|---|---|
"Where is my order?" | Generic tracking link | Quotes shipping doc | Checks system, alerts delay |
"Update card & cancel subscription" | Confused | Handles one only | Sequences both actions |
"I was charged wrongly" | Asks to email support | Links pricing doc | Verifies issue, triggers refund |
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
Metric | Before Fini | After Fini |
|---|---|---|
Automation Rate | <25% | 80%+ |
CSAT | 72% | 90%+ |
Avg. Handle Time | 15 min | <2 min |
Agent Headcount | 20 agents | 4 agents + Fini |
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.
Conceptual Foundation
1. What is Retrieval-Augmented Generation (RAG) in customer support?
RAG is an AI approach where the model pulls information from a knowledge base and then generates a response based on that retrieved content. It’s commonly used in support chatbots to answer FAQs or product queries.
2. Why is RAG often inadequate for real-world customer support scenarios?
RAG-based tools lack deep context, memory, and reasoning. They retrieve content in chunks, which means they fail when users ask multi-step or ambiguous queries. They also can't trigger actions like refunds or user updates.
3. What does 'RAGless AI' mean?
RAGless AI refers to systems that do not rely on document retrieval. Instead, they use memory, structured understanding, and backend orchestration to provide answers and execute workflows in real-time.
4. How does Fini's approach to AI differ from RAG-based support tools?
Fini bypasses retrieval and instead reasons through a user’s intent, context, and backend logic. It uses integrations to act autonomously—whether that’s updating user data, checking account details, or processing refunds.
5. Why are chatbots becoming obsolete in the AI era?
Most chatbots are rigid, rule-based, or prompt-dependent. They lack the memory and reasoning capabilities users expect. With the rise of LLMs and agentic AI, customers will soon expect real-time, intelligent automation—not scripted bots.
Technical and Functional Capabilities
6. What is agentic AI in customer support?
Agentic AI refers to autonomous systems capable of understanding goals, decomposing them into subtasks, reasoning through steps, and executing actions across platforms. It goes beyond static answers to deliver resolution.
7. How is agentic AI different from AI agents?
AI agents are often single-task tools, like answering a support question. Agentic AI uses multiple agents, tools, and memory to orchestrate broader, end-to-end solutions, often without human input.
8. Can agentic AI execute API actions in real-time?
Yes. Fini’s RAGless agentic AI can conditionally call APIs based on context, verify user attributes, and trigger backend actions like refunds, plan changes, or CRM updates.
9. Does agentic AI require human supervision?
Not necessarily. With configurable autonomy levels—like suggest-only, confirm-before-action, or full autonomy—Fini can be tailored to different enterprise risk thresholds.
10. Can Fini AI operate without prompt engineering?
Yes. Fini comes with a no-code setup where customer support teams can define logic, intent handling, and fallback behavior without writing a single prompt or line of code.
Memory, Reasoning, and Context
11. How does Fini remember past conversations?
Fini uses persistent, scoped memory to remember user sessions, preferences, and previous actions. This enables contextual responses, multi-step flow execution, and reduced user friction.
12. How does Fini handle ambiguous queries?
Instead of giving wrong answers, Fini detects ambiguity and proactively asks clarifying questions before continuing. This improves accuracy and trust.
13. Can Fini split and execute multi-intent messages?
Yes. If a user says, “Update my number and check my last invoice,” Fini breaks that down into subflows and executes both, without losing context.
14. Does Fini support customer personalization?
Absolutely. It uses user attributes like location, plan type, or churn risk to deliver customized replies and actions based on enterprise logic.
15. How does Fini learn from past support tickets?
Fini continuously improves by analyzing escalations, resolution paths, and performance data, which informs better routing and handling over time.
Real-World Use Cases
16. What types of tasks can Fini fully automate?
Fini can automate refunds, account updates, subscription changes, order lookups, KYC verification, and onboarding—while still escalating edge cases when needed.
17. Can Fini reduce the burden on L1, L2, and L3 teams?
Yes. Fini handles 80–90% of L1 queries, reduces L2 escalations with backend integrations, and intelligently routes edge cases to L3 teams with full context.
18. How does Fini perform in fintech support?
Fini complies with financial regulations (e.g., ISO 42001 readiness), handles sensitive workflows like password resets and payment disputes, and masks PII throughout the conversation.
19. Can Fini handle e-commerce use cases like returns or order tracking?
Yes. Fini integrates with shipping providers, e-commerce platforms, and CRMs to answer “Where’s my order?”, process returns, and offer personalized product recommendations.
20. What’s an example of a Fini-powered workflow?
If a user types: “My refund hasn’t arrived,” Fini checks order status, verifies refund timeline, offers an explanation, and logs or escalates the ticket if overdue.
Platform Compatibility and Integration
21. Which platforms does Fini integrate with?
Fini integrates with Zendesk, HubSpot, Intercom, Salesforce, LiveChat, and custom APIs—making it deployable in any support tech stack.
22. How easy is it to deploy Fini?
Most customers launch Fini in under 7 days using no-code deployment, historical data ingestion, and API integrations.
23. Can Fini be deployed on mobile, web, and in-app channels?
Yes. Fini works across chat, email, web widgets, mobile apps, and even voice—providing omnichannel coverage with a shared memory layer.
24. How does Fini differ from Fin (Intercom), Ada, or AgentForce?
Unlike most of these, Fini is natively agentic and RAGless. It doesn’t just wrap GPT—it executes actions, remembers, reasons, and learns autonomously.
25. Can Fini replace both chatbots and RPA systems?
Yes. Fini is more flexible than chatbots and smarter than RPA. It handles exceptions, ambiguous inputs, and edge cases with human-like adaptability.
Compliance, Guardrails, and Trust
26. How does Fini ensure data privacy?
Fini encrypts all interactions, masks sensitive fields by default (like emails or phone numbers), and provides full audit logs for compliance.
27. Is Fini compliant with ISO, SOC 2, or GDPR standards?
Yes. Fini is designed with compliance in mind and supports ISO 42001, GDPR principles, SOC 2 reporting, and internal audit tooling.
28. How does Fini avoid hallucinations or incorrect answers?
Fini uses scoped knowledge items, intent-driven flows, and policy constraints to reduce hallucinations and ensure safe output.
29. What kind of guardrails does Fini offer?
Fini provides over 40 configurable guardrails including API call limits, generation filters, fallback logic, escalation thresholds, and live handoff triggers.
30. Can customers configure what actions Fini can and can’t do?
Yes. Fini supports role-based control over actions like refunds, updates, or escalations. Admins can adjust autonomy levels per use case.
Business Outcomes & Strategy
31. Does Fini improve customer satisfaction scores (CSAT)?
Yes. By delivering fast, accurate, and empathetic support 24/7, Fini has helped clients increase CSAT by 15–30% within months of deployment.
32. Can Fini reduce support ticket volume?
Absolutely. Fini typically automates 70–90% of inbound support requests, significantly reducing human workload.
33. How does Fini impact support team efficiency?
With automation of repetitive tasks and intelligent routing, Fini enables leaner teams to manage higher volume while improving quality.
34. Is Fini cost-effective for startups and enterprises alike?
Yes. Fini scales from small teams to global enterprises with pricing models tailored to usage and outcomes—not just seat count.
35. What industries benefit most from RAGless agentic AI?
E-commerce, fintech, SaaS, insurance, and logistics benefit most—especially those with high ticket volume and time-sensitive queries.
Future of Support
36. What is the future of AI in customer support?
Support is shifting from human-heavy to AI-first, where autonomous agents handle the majority of queries while humans focus on complex exceptions.
37. Will RAG still be relevant in 2 years?
Likely only for search or documentation lookup. In support, RAG will become obsolete due to its inability to reason, act, or learn in real-time.
38. What comes after agentic AI?
Multi-agent systems capable of collaboration, self-repair, and proactive support will be the next frontier. Fini is actively building toward this.
39. Can agentic AI like Fini help with proactive support?
Yes. Fini can trigger outreach based on churn risk, failed transactions, or delayed orders—turning support into a proactive growth driver.
40. How can I see Fini’s RAGless AI in action?
Book a personalized demo at https://www.usefini.com/company/contact to see how Fini automates support across your existing stack.
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