What happens when AI agents move beyond simple chatbots and start interacting with enterprise data, systems, and processes? And how do organizations ensure these agents remain grounded, reliable, and governed as they scale?
In this episode of eiqWaves, Ulhas Gangaji, Lead – Customer Enablement at EvoluteIQ, and Akshay Bharadwaj, Lead Engineer at EvoluteIQ, explore the rapid evolution of Agentic AI, from early chatbot and RAG approaches to multi-agent systems, AI evaluation, enterprise connectivity, governance, and AI-powered application generation.
Takeaways
Agentic AI is evolving from answering questions to taking meaningful action. The real challenge is building agents that are not only intelligent, but grounded, measurable, governed, and connected to the enterprise systems where work happens.
What does it take to move from individual AI agents to intelligent, scalable, and governed agentic systems?
In Part 02 of It’s a Mad, Mad, Mad, Mad Agentic World!, Ulhas Gangaji, Lead, Customer Enablement at EvoluteIQ, and Akshay Bharadwaj, Lead Engineer at EvoluteIQ, continue the conversation on what it takes to build enterprise-ready AI agents.
The discussion goes beyond prompts and models, exploring the importance of memory, context management, skills, tools, MCP integrations, agent loops, orchestration, traceability, and guardrails. They also look at how Agentic AI can work with local language models, specialized agents, enterprise systems, and industry-specific requirements.
Key Takeaway
Agentic AI isn’t just about making agents smarter. It’s about giving them the context, skills, tools, orchestration, and guardrails to act responsibly at scale.
As the conversation highlights, the next evolution of enterprise AI will be increasingly specialized, connected, observable, and governed-with agents working across business processes and systems while operating within clearly defined boundaries.
