Watch our insightful one-hour webinar to get practical insights, best practices, and a roadmap for getting started with Agentic AI.
Q&A from Live Webinar
1. What skills or mindset should teams develop if they want to move from basic automation to fully agentic systems?
Think of it like moving from “just doing tasks” to “thinking and acting on their own.” Teams need to shift from a task automation mindset to an autonomy mindset.
Here’s what helps:
• Learn prompt engineering and how reasoning frameworks work—this is
the brain of your agents.
• Get comfortable with tool orchestration—connecting APIs, multi-agent workflows, and making them work together.
• Build strong data governance and observability skills—because
safe autonomy needs monitoring and drift detection.
• And don’t forget change management—people need to trust these
systems.
Bottom line: Treat agents as collaborators, not just tools. Design workflows where humans and agents complement each other.
2. How can organizations ensure responsible and ethical use of Agentic AI, especially when agents act autonomously?
• Put guardrails in place for access control, memory integrity, and safe autonomy.
• Scale governance as you grow—don’t try to do everything on day one.
• Use AgentOps for monitoring, bias checks, and accountability.
• Stay aligned with standards like GDPR, SOC2, HIPAA, and keep audit trails.
Remember: Governance isn’t a blocker—it actually unlocks innovation by creating a safe space for experimentation.
3. How to start with agentic AI as a designer? Are there any prerequisites one must be aware of?
If you’re a designer, start by understanding the patterns of agents—deterministic, hybrid, and autonomous—and where they fit.
Then, dive into UX for AI agents:
• How do you design prompts?
• How do you build feedback loops and error recovery flows?
Also, get familiar with multi-agent orchestration.
Prerequisites? Basic knowledge of LLM capabilities, API integration, and ethical design principles.
4. Will Agentic AI be available as an application for organizers based on their requirement?
Yes, modular agentic platforms can be tailored to organizational needs.
Solutions will likely include configurable workflows, governance layers, and integration APIs.
5. Do you think AgentOps — observability, monitoring, safety, drift detection — will become a formal discipline like DevOps or MLOps?
Absolutely. Managing autonomous agents isn’t a side task—it’s going to be a discipline of its own. Expect standards for monitoring, safety, and lifecycle management, just like DevOps transformed software delivery.
6. “I’m a college final year student pursuing my bachelors in computer science. I have started my Agentic AI journey and I have built a few agents using LangChain, LangGraphs, RAG and FAST API.
You’re off to a great start with LangChain, LangGraphs, RAG, and FastAPI! Next steps:
• Explore multi-agent orchestration and governance frameworks.
• Learn about MCPs, observability tools, and ethical AI design.
• Build portfolio-ready projects and contribute to open source—it really helps you stand out.
7. What are the other things I need to learn and how do I study Agentic AI effectively to get a job at Orion Innovation.
Focus on:
• LLM fine-tuning and agent frameworks.
• Enterprise integration and security.
• Compliance and scalability.
And yes—show applied skills through real projects. That’s what makes you job-ready.
8. Is vibe coding effective and useful to build large scale AI applications and does vibe coding affect the learning ability??
It’s great for quick prototyping and creative exploration. But for enterprise-scale systems, you still need structured engineering and rigorous testing. Balance creativity with discipline.
9. What is your advice on bringing people along on this journey?
Communicate the “why” clearly. Show the value.
Offer training and hands-on workshops.
Start with hybrid workflows—people trust what they can see working.
10. Are there instances where scaling Agentic AI successfully does not result in positive ROI / business value? If so, why?
Yes—if it’s done without aligning to business goals or without proper governance. Poor change management and lack of integration can turn scaling into a cost sink instead of a value driver.
11. How do you see cost structure changing once autonomous agents handle most tier-1/tier-2 issues?
Expect big savings on operational costs and faster resolution times. But remember—initial investment in governance and infrastructure is key.
12. What do you think enterprises are not even thinking about yet that will become critical for agentic AI success in the next 12 months?
• Agentic Mesh architectures for distributed autonomy.
• Standardization of AgentOps and compliance frameworks.
• Human-agent collaboration models for decision-making.
13. Does Orion use any Vibe coding tools to build AI agents or to your projects? Or does it encourage?
Yes, we do encourage using automation and vibe coding to build agents, but with checks and balances. While many of our agents are still hand-coded, we see a clear trend toward AI-assisted tools for agent creation. A great example is Anthropic’s Claude Code, which started as an internal project and eventually began building more Claude Code using itself—a reinforcing loop. This approach is gaining traction, and we’re well on track to adopt more of these automated methods going forward.
14. How do you usually determine in enterprise which processes should be human led, agent led and which should be hybrid
Start with risk modeling.
• Begin with low-risk, high-impact areas—these are great for early wins.
• Gradually move to high-risk, high-impact processes once you gain confidence.
• Some tasks will always need human oversight, especially where judgment or compliance is critical.
Tip: Map every process back to its business value and risk profile before deciding.
15. How should we evaluate whether to go with build or buy when it comes to agentic AI systems.
It depends on your use case complexity and tech ecosystem:
• If you’re already on platforms like Microsoft, AWS, or Google, buying pre-built solutions often makes sense.
• For highly specialized or complex workflows, custom builds may deliver better results.
Rule of thumb: Start with what aligns best with your existing stack, then scale with bespoke solutions if needed.
16. Based on your experience, which skills will become most valuable as Agentic AI becomes embeded in the enterprise
Two big ones:
• Continuous learning—AI evolves fast, so adaptability is key.
• Cross-functional thinking—understanding tech, business, and governance together.
If you thought learning stopped after college, think again—this is a lifelong journey.
17. What are the use cases where Agentic AI is better suited than GenAI? Will GenAI kind of disappear as background technology in the future
GenAI isn’t going anywhere—it powers Agentic AI. Think of Agentic AI as GenAI plus autonomy.
Best-fit use cases:
• Customer service—agents can not only recommend but also take action (e.g., rebook flights).
• IT operations—agents can troubleshoot and fix issues, not just suggest solutions.
18. Is there a danger of “runaway agents”, if so, what measures do enterprises take to mitigate the risks
Yes, risks exist—like prompt injection or rogue agents.
Mitigation strategies:
• Strong AI governance platforms with monitoring and logging.
• Human-in-the-loop oversight for critical decisions.
• Security controls to prevent hijacking and privilege abuse.
19. What’s the biggest misconception enterprises have when implementing Agentic AI?
That agents are just chatbots.
Reality: Agents are action-oriented software entities that automate work, not just conversations. They plan, reason, and execute tasks autonomously.
20. For companies just getting started, what are the first two steps they should take to adopt Agentic AI confidently?
• Step 1: Audit what’s already happening with GenAI or automation in your org—start from there.
• Step 2: Understand your business processes deeply and identify where autonomy adds real value.
Bonus: Partner with trusted AI vendors early.
21. How do you see Agentic AI impacting workforce models — will it replace roles or redefine them?
Mostly redefine, not replace.
Agents will become work companions, helping employees with decision-making, writing, and repetitive tasks. Think augmentation, not elimination.
22. What do you see as the biggest technical limitation preventing agents from being reliable in high-stakes, deterministic workflows?
Non-determinism in LLMs.
Even with temperature settings, outputs can vary.
Solution: Build tolerance thresholds and control mechanisms for error handling and alerts.
23. Is Agentic Mesh currently a concept or some organizations have already got to that level in their automation journey? If so, what impact has it made?
It’s beyond concept, but still for mature organizations.
Agentic Mesh makes sense when you’ve automated large parts of your business and now want distributed autonomy across ecosystems.
Early adopters are using protocols like A2A and MCP to enable this.
