Agentic AI
Jan 17, 2025
Deepak Singla
IN this article
RAG systems excel at retrieving and presenting answers but fall short when action is required. Agentic AI bridges this gap by combining knowledge retrieval with real-time actions, transforming support workflows with dynamic, action-oriented AI solutions.
If you’ve ever built or worked with AI systems, you know the allure of Retrieval-Augmented Generation (RAG). It’s been the go-to framework for most companies in the last 24 months, and for good reason. It’s simple, powerful, and does one thing exceptionally well: it retrieves answers from knowledge bases, phrases them nicely, and gets the job done for simple, static Q&A.
But here’s the catch: RAG systems falls short when you need AI to take actions or adapt beyond the scope of static knowledge. They’re great at telling users what to do but fail to handle the actual doing. Imagine a customer asking, “How do I reset my password?” A RAG system will walk them through the steps, but what if the AI could just reset the password for them?
That’s where Agentic AI shines. Agentic AI works because it combines the ability to retrieve knowledge with the capacity to take action. Let’s break it down:
1. Stateful Interactions: Unlike RAG, which treats each question in isolation, Agentic AI tracks user context and state over time. This makes it ideal for multi-step processes like troubleshooting or account updates.
2. Action-Oriented Workflows: Integrated with APIs, Agentic AI doesn’t just tell you how to do something—it directly executes tasks. For example:
• Updating customer profiles.
• Submitting refund requests.
• Scheduling deliveries.
3. Dynamic Learning Loops: By leveraging Reinforcement Learning from Human Feedback (RLHF) and real-time event tracking, Agentic AI can continuously improve workflows based on outcomes.
4. Knowledge Evolution: Instead of relying solely on static databases, Agentic AI connects to live systems (like ticket logs or CRM updates), dynamically adjusting its knowledge to reflect changes in policies, product features, or edge cases.
Agentic AI helps you take actions. It’s the difference between saying, “You can find the details here” and “Done! I’ve processed that refund for you.”
To move from RAG to Agentic AI, here’s what we built at Fini:
1. Knowledge Store: An AI system that structures, updates, and de-duplicates knowledge from past tickets, ensuring agents act on the latest, cleanest data.
2. Custom Actions: Agents integrate with customer APIs to directly perform tasks (using systems like Zapier for simplicity or custom endpoints for scalability).
3. Context Management: A robust state management layer that retains conversation history and customer-specific attributes to tailor solutions dynamically.
4. Safety Guardrails: Guardrails ensure agents act within defined boundaries and notify users of any uncertainty before taking critical actions.
The result? Companies like Column Tax Qogita have seen their support workflows automated by over 70%. Response times have gone from hours to seconds, and human agents now focus on solving complex problems and edge cases, instead of handling repetitive actions.
RAG laid the foundation, but Agentic AI is the future. It’s not just answering questions—it’s delivering outcomes.
Are you still stuck on RAG, or are you building for the next wave?
Introduction to Agentic AI
1. What is Agentic AI and how is it different from traditional RAG systems?
Agentic AI refers to artificial intelligence systems that can not only retrieve information (like RAG—Retrieval-Augmented Generation) but also take intelligent actions based on user input. While RAG focuses on answering questions using static knowledge, Agentic AI adds layers of memory, context, API execution, and dynamic learning—allowing it to perform tasks like issuing refunds, updating records, or completing workflows autonomously.
2. Why are RAG systems not enough for modern customer support?
RAG systems are limited to providing information—they can't complete tasks. For example, if a user asks how to reset a password, a RAG system explains the steps, but Agentic AI can actually reset it. In high-volume support environments, this difference translates to major time and cost savings.
3. How does Agentic AI improve response times in support?
Agentic AI reduces support resolution time from hours to seconds by taking direct actions (via APIs or internal tools), skipping manual steps like agent triage or form submission. It also avoids repeated clarification by remembering user context across steps.
4. Is Agentic AI replacing RAG?
Agentic AI builds on the foundation of RAG but adds execution capabilities, memory, and dynamic learning. It doesn't replace RAG completely but augments it with action and adaptability—making it ideal for end-to-end resolution in customer support.
5. What types of tasks can Agentic AI perform that RAG cannot?
Agentic AI can execute tasks such as issuing refunds, updating customer profiles, fetching real-time order statuses, scheduling deliveries, or escalating to a human agent when confidence is low—tasks RAG models are not built for.
Technical Capabilities
6. How does Agentic AI manage multi-step processes?
Through stateful memory and context tracking, Agentic AI can follow a user across multiple steps, remembering what’s been said and done earlier. This enables it to handle scenarios like troubleshooting, subscription changes, or returns processing seamlessly.
7. What is the role of context management in Agentic AI?
Context management allows the AI to remember user attributes (e.g., account type, location, issue history), which ensures more accurate responses and better action decisions over time. It’s key to personalization and multi-turn interaction success.
8. Can Agentic AI adapt to policy or product changes automatically?
Yes, because Agentic AI connects to live systems like CRMs, ticket logs, or internal tools, it can continuously refresh its understanding of workflows, product changes, or updated policies—ensuring its answers stay accurate and actionable.
9. How does Fini handle custom actions using Agentic AI?
Fini allows businesses to plug in their own APIs (or connect via Zapier or other middlewares) to enable AI agents to take real actions, such as canceling subscriptions, updating shipping addresses, or verifying customer identity.
10. How does Fini implement guardrails in Agentic AI systems?
Fini’s AI includes over 40 configurable safety guardrails to prevent undesired behavior. These include input validation, output filtering, fallback to human agents when confidence is low, and contextual restrictions based on use case or user tier.
Use Cases & Benefits
11. What business problems does Agentic AI solve better than RAG?
Agentic AI is more effective for problems requiring execution—such as refunding payments, checking loyalty points, or initiating account changes. It's especially valuable for e-commerce, fintech, and SaaS companies dealing with high support volumes.
12. How does Agentic AI impact customer satisfaction (CSAT)?
Agentic AI delivers faster, more accurate, and complete resolutions without the need for handoffs, which leads to lower wait times, fewer errors, and a more seamless experience—resulting in higher CSAT scores.
13. How does Agentic AI reduce operational costs?
By automating actions, not just answers, Agentic AI reduces ticket handling time, minimizes human workload, and prevents repetitive tickets. Fewer escalations and shorter resolution cycles lead to meaningful cost savings.
14. Can Agentic AI personalize support experiences?
Yes, by using stored user attributes and conversation history, Agentic AI can tailor responses to each user’s situation. For example, it can greet a user by name, reference past issues, or apply personalized discounts.
15. What kind of ROI can companies expect by switching to Agentic AI?
Customers using Fini’s Agentic AI have seen up to 70% automation of support workflows, 80%+ resolution rates, and over 50% cost reduction—demonstrating a strong ROI even within the first few weeks of deployment.
Fini-Specific Implementation
16. What is Fini’s Knowledge Store and why is it critical?
Fini’s Knowledge Store is a structured, continuously updated memory of your support content, ticket resolutions, and internal documentation. It ensures the AI always acts on the latest, cleanest, and most reliable data—reducing hallucinations and outdated answers.
17. How do Fini agents take action in real-world systems?
Fini integrates with tools like Zendesk, Salesforce, HubSpot, Stripe, and your internal APIs. This allows agents to perform actions like processing refunds, updating user data, or triggering internal workflows in a secure, auditable manner.
18. How does Fini maintain accuracy in action-taking AI?
Fini uses human-in-the-loop feedback, regular audits, and context validation to ensure accuracy. If the AI is uncertain or fails, it flags the case to a human and learns from their resolution for future improvement.
19. What’s the onboarding time for deploying Agentic AI via Fini?
Most companies are up and running within 5–7 days, including knowledge ingestion, API integration, and flow configuration. Fini offers a no-code dashboard for customization and easy tuning post-launch.
20. Can Fini’s Agentic AI scale with my support volume?
Absolutely. Fini’s architecture is designed to scale across millions of conversations per month, dynamically routing users and executing flows without slowing down or compromising on accuracy.
Integrations & Tooling
21. What platforms does Fini integrate with for support automation?
Fini natively integrates with Zendesk, Intercom, Salesforce, HubSpot, Freshdesk, Front, LiveChat, Stripe, Notion, Confluence, and more—allowing your AI agents to read, write, and act across systems.
22. Is technical expertise required to set up Fini?
No. Fini’s no-code interface allows teams to deploy flows, create prompts, set up API calls, and monitor performance without needing engineering resources. However, developer options are available for advanced use cases.
23. Can I configure different flows for different user segments?
Yes, Fini supports conditional logic and user segmentation, allowing you to create flows for new users, power users, premium accounts, or even specific geographies or product plans.
24. How are Fini’s AI agents updated as the product evolves?
Updates can be made automatically through knowledge syncing or manually through the dashboard. Fini also learns continuously from new support tickets, updating knowledge and flows accordingly.
25. Does Fini support multilingual interactions?
Yes. Fini’s Agentic AI supports multiple languages and can deliver localized responses, perform actions in localized systems, and handle region-specific logic when needed.
Learning & Feedback
26. How does Fini use reinforcement learning?
Fini collects human feedback on failed or flagged tickets and uses that data to retrain models, refine knowledge, and improve flow logic. This makes the AI smarter and more effective over time.
27. Can my agents review and improve the AI’s actions?
Yes. Every action and message is logged, and your team can approve, edit, or reject AI decisions. These inputs help the system self-correct and get better in future conversations.
28. How does Fini identify knowledge gaps?
Fini’s analytics dashboard shows where users drop off, when agents intervene, and what queries lead to “I don’t know” responses—surfacing areas where knowledge is missing or unclear.
29. What role do feedback loops play in Agentic AI?
Feedback loops ensure that the system learns from every outcome—successful or failed—so that it improves automatically. With each cycle, resolution rates increase and edge case coverage grows.
30. How often does the AI retrain itself?
Retraining is ongoing—Fini learns from every new resolved ticket, corrected output, or updated article. You can also schedule manual retraining or trigger it after major product changes.
Security, Trust, and Compliance
31. Can Agentic AI make unauthorized changes?
No. All actions performed by Fini’s AI are bounded by permissions, APIs, and approval rules you define. Sensitive workflows like refunds or data deletion can require manual approval or MFA.
32. What audit logs does Fini provide?
Fini maintains detailed logs of every AI action, user interaction, and flow path—allowing you to track, audit, and troubleshoot every AI decision for transparency and compliance.
33. Is Agentic AI safe for regulated industries like fintech or healthcare?
Yes, Fini complies with major security standards (SOC 2, GDPR, HIPAA where needed), and its AI systems operate within strict guardrails, offering fine-grained control over data access and usage.
34. How does Fini ensure user privacy?
Fini redacts PII, supports data masking, and operates under strict encryption standards. You control what data is ingested, stored, and used for training.
35. What happens when the AI is unsure about an action?
Fini’s confidence thresholds prevent the AI from executing tasks when uncertain. It will either ask for confirmation, offer to escalate to a human, or refrain from taking action until verified.
Adoption & Future
36. Do I need to overhaul my support workflows to use Fini?
Not at all. Fini plugs into your existing tools and enhances your current workflows. You can start small—automating only FAQs or one flow—and expand over time.
37. Can Agentic AI handle edge cases or rare issues?
With reinforcement learning and human-in-the-loop feedback, Fini improves even on rare queries. Over time, it develops deeper coverage, minimizing agent escalations.
38. What kind of reporting and analytics does Fini offer?
Fini provides dashboards showing resolution rates, agent transfer rates, confidence scores, ticket volumes by flow, and reasons for failure—giving you visibility and control.
39. Can Agentic AI work alongside human agents?
Yes. Fini enables hybrid workflows where the AI handles what it can and tags a human when needed. Transitions are seamless, and context is preserved across handoffs.
40. How do I get started with Fini’s Agentic AI?
You can book a 15-minute demo with the Fini team to walk through capabilities, see real examples, and customize a deployment plan tailored to your support stack.
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