Gaurav Bhandari
Gaurav is the Content Lead at Kore.ai. He has over 10 years of experience creating content and designing marketing campaigns that drive organic growth for B2B companies. As both an engineer and a marketer, he excels at translating technical concepts into compelling marketing content. Outside of work, he enjoys long walks and playing badminton with his 10-year-old daughter.
Articles by this author
Democratizing the frontier: Why enterprises don’t need to build their own AI model
Building your own frontier AI model is no longer the competitive edge it once was. Research shows smaller, cheaper models keep closing the gap on giant ones, and real examples like Land O'Lakes prove a small model tuned on your own data can beat a frontier model at lower cost. Real advantage now comes from your context layer, private evaluations, and a flexible harness that lets you swap models without rebuilding your stack.
How to ensure AI agents survive production at enterprise scale?
Most AI agent pilots never reach production, and many that do collapse once they hit real traffic. Four things separate agents that survive from ones that break: robustness to catch quality drift before customers notice, scalability that plans for traffic spikes ahead of time, controllability so teams can intervene and audit decisions, and extensibility so the system can adapt without a full rebuild each time something changes.
How to choose the right model for your AI agents (and stop overpaying)
Running every AI agent on the same frontier model wastes money, since 40 to 60 percent of enterprise token budgets go to waste. It also adds latency and creates a single point of failure. The fix is matching each agent's model to its actual task, weighing data sensitivity, cost, latency, and risk, then choosing between frontier, commodity, or compact models. Configuring reasoning depth and verbosity per agent matters just as much as picking the model itself.
Are your AI agents production-ready? 400+ leaders say not yet
A Kore.ai survey of 408 leaders found 82% of enterprises say AI agents have taken consequential actions in production, yet 70% could not trace a failure back to the responsible agent. Nearly 80% of those actions required a costly manual reversal, even though most organizations already had guardrails in place. Most still treat agent failures as IT issues rather than operational risk. The takeaway is that governance has to be built into agents from the start, not added afterward.
AI agent guardrails: How to keep your agents safe and predictable from day one
AI agent guardrails are technical controls, and they need to cover four areas: behavior, data access, tool actions, and operational limits. Guardrails fail when teams rely on prompts alone, only enforce rules at runtime, or apply the same controls to every agent regardless of risk. The fix is layering guardrails into the architecture itself, configuring per agent constraints, and adding platform level controls before agents ever go live.
What is an AI-native organization? How to build an AI-native enterprise
AI-native means AI is built into the architecture itself, not added as a feature. A simple test to check AI-native: remove AI from the platform, and if it stops working, it is AI-native. It rests on three pillars: intelligence as the foundation, autonomy within guardrails, and compounding improvement over time. Most enterprises stumble by retrofitting AI into human designed workflows, underestimating the ongoing work AI systems need, or treating a successful pilot as proof production will go smoothly.