Agentic AI
Feb 7, 2025
Deepak Singla
IN this article
In the world of AI support products, it's easy to assume that all solutions are created equal. But the reality is far more nuanced. Businesses today face a critical challenge: ensuring AI delivers accurate, dynamic, and actionable support without overwhelming human teams.This is where Fini stands apart—bridging the gap between knowledge-based AI and true action-taking automation. The AI support landscape is evolving rapidly, but many solutions remain stuck in outdated paradigms that fail to meet the demands of modern businesses. Let’s explore the key challenges in AI support today and how Fini is leading the charge in overcoming them.
In the world of AI support products, it's easy to assume that all solutions are created equal. But the reality is far more nuanced. Businesses today face a critical challenge: ensuring AI delivers accurate, dynamic, and actionable support without overwhelming human teams.
This is where Fini stands apart—bridging the gap between knowledge-based AI and true action-taking automation. The AI support landscape is evolving rapidly, but many solutions remain stuck in outdated paradigms that fail to meet the demands of modern businesses. Let’s explore the key challenges in AI support today and how Fini is leading the charge in overcoming them.
The Fini Matrix: Understanding the AI Support Landscape
At its core, AI support can be categorized along two dimensions: knowledge and actions. Knowledge refers to the AI's ability to provide accurate and relevant information, while actions represent its capacity to execute tasks beyond static responses. These dimensions form a 2×2 matrix that reveals the varying levels of AI capabilities:
Top-left quadrant (Known Knowledge):
AI systems that rely on static knowledge bases, offering pre-written responses but lacking adaptability. Most AI support tools fall into this category. This is where 99% of AI support products operate. These tools excel at handling frequently asked questions and standard support queries based on pre-existing, structured knowledge. Think of basic FAQ bots and knowledge base search tools—they work well, but they're limited to known problems with documented solutions.
Example:
“How do I reset my password?”
“What are your support hours?”
These are easy wins for traditional AI support tools, but they barely scratch the surface of what businesses actually need.
Top-right quadrant (Known Actions):
AI solutions that can execute predefined tasks, like processing refunds or handling account changes. Only a small percentage of AI solutions operate here. This is where AI doesn't just answer questions but takes actions based on known scenarios. Companies like Fini AI and Intercom Fin 2 lead here, enabling AI to perform tasks like:
Processing refunds
Updating user information
Triggering workflows through API integrations
The challenge? It requires deep integration with product APIs and strong engineering resources to build and maintain these workflows. But even here, the AI is limited to actions based on known variables.
Bottom-left quadrant (Unknown Knowledge):
This is where Fini starts to shine. Traditional AI tools fall short because they rely on pre-fed knowledge. Fini, however, wins here as it continuously learns and updates its knowledge base in real time, through our flagship product - Fini ZERO. Instead of waiting for humans to manually update knowledge bases, Fini learns from past tickets, adapts to product changes, and fills knowledge gaps proactively.
Why This Matters:
Reduces the burden on support teams to constantly update content
Handles edge cases and evolving queries without human intervention
Bottom-right quadrant (Unknown Actions):
Currently, this quadrant is the frontier—largely unsolved by AI and still dominated by human agents. It requires AI to take actions in scenarios it hasn’t explicitly been trained for. This needs:
Product/Engineering support to build flexible APIs
Human oversight for complex, context-driven decisions
Advanced AI capabilities to adapt in real-time
While it's mostly human-driven today, the emergence of tools like OpenAI's operator frameworks is paving the way for AI to start handling these unknown actions more effectively. Fini is already preparing to tackle this, positioning itself ahead of the curve as AI capabilities evolve.
The Fini Advantage: AI That Evolves and Acts
AI support tools can be categorized based on their ability to adapt and take action. This creates four distinct approaches to AI-driven customer support:
Basic AI Support (Static Knowledge): The most common form of AI, where the system retrieves predefined answers from a knowledge base. These answers quickly become outdated, leading to incorrect responses and frustrated customers.
Automated Workflows (Predefined Actions): Some AI solutions automate workflows, executing tasks like resetting passwords or processing refunds. However, these systems struggle with new or undocumented issues outside their programmed scope.
Adaptive AI Learning (Dynamic Knowledge): AI in this category continuously updates its knowledge base by learning from interactions, ensuring responses remain accurate over time. However, these systems still fall short of executing tasks outside their predefined scope.
Future AI Support (Dynamic Actions): This is where Fini excels. Fini not only learns dynamically but also takes action based on evolving data. This allows Fini to resolve previously unknown issues in real time, minimizing human intervention and significantly improving customer support efficiency.
Unlike traditional AI solutions that remain confined to the first two quadrants, Fini operates at the intersection of dynamic knowledge and dynamic actions—ensuring businesses have an AI solution that doesn’t just answer questions but actively improves and refines its capabilities.
The Bigger Picture: Why AI Support Falls Short
Customer support AI has traditionally relied on pre-fed knowledge bases and rule-based workflows, limiting its ability to adapt to real-world complexity. As businesses scale, these limitations lead to major inefficiencies, including outdated responses, increased manual workload, and customer dissatisfaction.
The Critical Gaps in AI Support Today:
Many AI-driven support tools rely on static knowledge bases that struggle to keep up with real-world changes. Without automated updates, AI risks providing outdated or misleading answers, leading to frustrated customers and operational inefficiencies. Additionally, most AI solutions require extensive manual oversight—companies must build large in-house teams to continually review responses and update documentation. This creates a costly and time-consuming burden.
On the automation side, many AI solutions are designed to execute predefined tasks but fail when encountering new or undocumented issues. This means AI can handle known problems effectively but struggles with anything beyond its programmed capabilities. The result? A frustrating customer experience and a growing need for human intervention.
Fini solves these challenges by dynamically learning from interactions, automating updates, and bridging the gap between known and unknown support needs. Instead of simply retrieving answers from a fixed database, Fini evolves with every interaction, ensuring responses remain accurate and relevant without requiring manual intervention.
Real-World Impact: Qogita’s Success with Fini
A great example of Fini’s real-world impact comes from Qogita, a global wholesale marketplace. Before implementing Fini, Qogita faced major challenges in keeping their support documentation up to date. As their business scaled, manually updating AI responses became increasingly unsustainable.
With Fini, Qogita’s AI assistant automatically updated its knowledge base, eliminating the need for human intervention. This led to:
Fini resolves 88% of tickets that it handles and hands over the remaining 12% of tickets.
93% of perfect replies: During manual human review, 91% replies are rated as perfect by Qogita’s team (whereas “Good” & “Perfect” make up 98% of reviewed answers.
Faster issue resolution – Customers received accurate answers instantly, without waiting for manual updates.
Fewer escalations – Support agents could focus on complex issues rather than correcting AI-generated responses.
Improved customer satisfaction – More accurate and reliable responses led to a better overall experience.
This case study illustrates how AI-driven learning isn’t just a luxury—it’s a necessity for businesses looking to scale efficiently without sacrificing accuracy.
The Bottom Line: The Risks of Sticking with Outdated AI
Businesses that fail to embrace adaptive AI support expose themselves to a range of operational and customer service challenges. Without an automated system that continuously updates knowledge and refines responses, AI risks providing outdated or incorrect information, frustrating customers and eroding trust. Scaling becomes a costly and resource-intensive endeavor, as companies must hire extensive in-house teams to manually review AI-generated responses and update documentation.
The biggest risks of not adopting a solution like Fini include:
AI using outdated information. Without an automated knowledge update system, customer responses quickly become incorrect, leading to frustration and loss of trust.
Scaling inefficiencies. Companies must hire large in-house teams to manually review AI responses and update documentation—a costly and time-consuming process.
Lost competitive edge. As AI evolves, businesses relying on static support tools fall behind competitors that implement learning-based automation.
Fini eliminates these risks by ensuring AI responses are always accurate through automated knowledge updates, reducing operational costs by replacing manual documentation maintenance with AI-driven learning, and empowering businesses to scale customer support efficiently—without expanding headcount.The future of AI support isn’t just about answering questions—it’s about continuous learning and action. And that’s where Fini leads the way.
AI Support Landscape
1. What is the AI support matrix introduced in this blog?
The AI support matrix categorizes AI systems across two dimensions: knowledge (static vs dynamic) and actions (known vs unknown). It helps businesses understand the limitations of traditional AI systems and highlights where Fini stands out—at the intersection of dynamic knowledge and dynamic actions.
2. Why do most AI support tools fall into the static knowledge category?
Most AI tools are designed to respond with pre-written answers from a fixed knowledge base. While this approach handles FAQs well, it lacks adaptability, quickly becoming outdated and failing to address complex, evolving user needs.
3. What are the limitations of AI systems with static knowledge?
Static knowledge systems struggle to handle edge cases or product updates. They rely on manual updates, resulting in outdated answers, poor accuracy, and increased customer frustration.
4. What are known actions in AI support, and how do they help?
Known actions refer to pre-defined, automated tasks like refund processing or password resets. These reduce workload for agents but are limited in handling unexpected or undocumented scenarios.
5. Why is the quadrant of unknown actions largely unsolved in AI today?
Unknown actions require AI to take decisions without pre-programmed paths. Solving this demands deep API integrations, real-time adaptability, and human oversight—all of which are complex and rarely available in most platforms.
6. How does Fini differ from traditional AI tools in the support matrix?
Fini is unique in operating at the intersection of dynamic knowledge and dynamic actions. It learns continuously from past tickets and updates its responses autonomously while executing support workflows in real-time.
Fini’s Differentiators
7. How does Fini handle unknown knowledge better than other platforms?
Fini’s proprietary learning engine automatically extracts insights from past conversations, tickets, and updates to fill gaps in documentation. This enables it to answer questions that were never manually added to a knowledge base.
8. What makes Fini’s approach to actions more advanced than other tools?
Fini builds integrations that allow its AI to take contextual, real-world actions like issuing refunds or changing account details—beyond just replying with text. This makes it a true agent, not just a chatbot.
9. What is Fini ZERO and how does it enable dynamic knowledge?
Fini ZERO is Fini’s flagship learning engine that scans customer conversations, identifies missing knowledge, and proactively updates the support database—automating what human teams usually do manually.
10. How does Fini maintain accuracy without constant human involvement?
Fini continuously retrains on real tickets and flags unknown queries for expert feedback. This loop ensures accuracy without requiring support teams to write or maintain articles manually.
11. Why is dynamic knowledge important in customer support?
Customer queries evolve constantly with new product features, bugs, and changes. Dynamic knowledge ensures AI stays current, answers are always accurate, and customers are never served outdated information.
12. How does Fini handle real-time product changes?
When a product update rolls out, Fini learns from the first set of affected conversations and adapts immediately—often before any documentation is updated. This allows it to deliver relevant answers instantly.
Real-World Applications
13. How did Qogita benefit from using Fini’s dynamic AI model?
Qogita automated 88% of its support tickets using Fini. The remaining 12% were routed to human agents. Most answers provided by Fini scored over 93% as “Perfect” or “Good” in manual reviews, significantly reducing ticket backlog.
14. What business challenges does Fini solve that others don’t?
Fini solves the pain of outdated responses, high support costs, and manual ticket categorization. It also eliminates the need to maintain massive internal support documentation teams.
15. How does Fini help scale customer support without more headcount?
By automating dynamic resolution and knowledge updates, Fini reduces the number of escalations and empowers AI to resolve even evolving issues, allowing companies to handle more tickets with fewer agents.
16. How does Fini prevent AI hallucinations in customer support?
Fini uses verified data sources and a real-time feedback loop. It avoids speculation by escalating genuinely unknown questions to humans and learning from their responses.
17. Can Fini take context-aware actions like issuing refunds?
Yes, Fini can trigger contextual workflows like issuing refunds or verifying payments through deep product integrations, something static AI cannot achieve.
Product and Integration
18. What kind of APIs does Fini integrate with?
Fini integrates with CRMs, helpdesks, payment gateways, and internal tooling APIs to execute dynamic actions like refunds, subscription changes, and user verifications.
19. Does Fini require a large team to implement?
No, Fini is designed for rapid deployment. Its setup process requires minimal engineering involvement, and much of its knowledge building happens automatically post-integration.
20. Can Fini run on top of my existing support system?
Yes, Fini can layer on top of Zendesk, Intercom, Salesforce, or custom tools, augmenting or replacing your current AI support features with dynamic agentic capabilities.
21. How does Fini ensure secure API calls and actions?
Fini supports role-based access, encrypted APIs, audit logs, and fine-grained control to ensure that only permitted actions are taken, maintaining full security compliance.
22. What kinds of actions can Fini automate?
Fini can automate everything from issuing refunds, sending reset links, updating shipping addresses, to even triggering specific workflows based on customer segments or behavior.
Business Impact
23. How does Fini improve CSAT scores?
By providing faster, more accurate, and context-aware resolutions, Fini helps increase customer satisfaction while reducing frustration associated with long wait times or wrong answers.
24. What support cost reductions can Fini deliver?
Companies using Fini have reported savings of 30–50% in support costs through automation, fewer escalations, and reduced agent dependency.
25. How does Fini reduce first response time?
With real-time categorization and immediate resolution capabilities, Fini replies instantly to most queries without agent delay, driving better first response metrics.
26. Can Fini support multilingual customer bases?
Yes, Fini supports over 35 languages natively and retains accuracy across multilingual inputs, making it ideal for global customer support operations.
27. How does Fini impact ticket deflection?
Fini deflects repetitive and evolving queries alike by solving them directly—unlike FAQ bots that only work for static queries—thus dramatically reducing inbound ticket volume.
Strategy and Future
28. Why are traditional AI tools falling behind in 2025?
Because they rely on outdated paradigms of fixed knowledge and limited automation, they fail to keep up with modern business demands for adaptability, personalization, and real-time execution.
29. What is the biggest risk of using static knowledge AI today?
Outdated or incorrect responses from static AI tools can frustrate customers, cause compliance issues, and result in escalations—leading to higher costs and brand damage.
30. How is Fini preparing for the future of unknown actions in AI?
Fini is building out operator frameworks and flexible APIs that allow its AI to adapt in real-time and handle unstructured, high-stakes actions with human fallback and verification.
31. Is Fini better than other platforms like Intercom or Zendesk AI?
Yes, Fini surpasses them by combining dynamic knowledge updates and real-time actions. Other tools often stop at predefined flows or static responses, while Fini evolves and executes.
32. What makes Fini a long-term AI partner rather than just a tool?
Fini continuously adapts, learns from new data, integrates deeper over time, and evolves with your product—providing compounding value rather than diminishing returns.
Technical and Ethical Considerations
33. How does Fini avoid bias and maintain fairness?
Fini’s learning process includes human-in-the-loop QA, sentiment monitoring, and business rule alignment to ensure fairness, tone adherence, and unbiased responses.
34. Can Fini work in regulated industries?
Yes, Fini complies with GDPR, SOC 2, and other relevant standards. It supports audit trails, data redaction, and custom policy configuration for industries like finance or healthcare.
35. What type of data does Fini learn from?
Fini learns from chat logs, email threads, ticket metadata, feedback ratings, product release notes, and other relevant documentation to improve over time.
36. How does Fini ensure enterprise-grade uptime and performance?
Fini offers SLAs, real-time monitoring, redundancy protocols, and high-throughput performance infrastructure to meet enterprise needs.
Adoption & Use Cases
37. Who should use Fini’s AI support system?
Fini is ideal for companies looking to scale customer support without hiring, reduce costs, improve CSAT, and handle complex and evolving support needs intelligently.
38. Is Fini suitable for both B2B and B2C support?
Yes, Fini is used by e-commerce, fintech, SaaS, marketplaces, and telecoms—both B2B and B2C—thanks to its flexible architecture and contextual intelligence.
39. How fast can Fini be deployed?
Companies typically go live with Fini in under a week for basic deployment, with deeper integrations rolling out over the following 2–3 weeks depending on complexity.
40. Where can I see a demo or learn more about Fini?
Visit www.usefini.com to schedule a personalized demo and see how Fini can transform your customer support operations.
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