From GenAI to Agentic AI: Episode 3 of Tesseract Talks

Enterprise AI is entering a new phase.

Over the last few years, organizations have experimented heavily with generative AI—testing chatbots, copilots, and model-driven applications. But as early excitement gives way to real-world deployments, a different challenge is emerging: how do you move from isolated AI features to intelligent systems that can operate safely, reliably, and at scale?

That question was at the heart of a recent episode of Tesseract Talks, with Anuraag Gutgutia, Co-founder and CEO of TrueFoundry and Raghu Sethuraman, Vice President of Engineering at Automation Anywhere.

Together they explored the shift from generative AI to agentic AI—and what it really takes to make that transition work inside large organizations

Watch Episode 1: Turning AI Chaos into Control with Nikunj Bajaj

Watch Episode 2: The Hidden Infrastructure Powering Enterprise AI with Abhishek Choudhary

Here are some of the key insights from that conversation.

The Big Shift: From Models to Systems

For the past few years, most AI conversations have revolved around models. Enterprises compared benchmarks, debated providers, and experimented with prompts. But as Raghu pointed out, agentic AI requires a fundamentally different mindset.

Traditional generative AI applications were essentially “models wrapped as APIs.” In contrast, agentic AI systems involve many moving parts—tools, memory, workflows, integrations, and guardrails. Models are only one component of a much larger system

This introduces entirely new challenges:

• How do you manage prompts and version them?
• How do you give agents access to internal tools securely?
• How do you ensure scalability and reliability?
• How do you put guardrails around powerful external models?

According to Raghu, the problem has evolved from picking the right model to designing the right infrastructure.

“AI is transitioning from a model-as-an-API approach to a system design challenge,” he explained. “Enterprises must now build platforms that can orchestrate multiple components together in a secure and governed way”.

Why Governance Becomes Mission-Critical

Once AI systems begin taking actions—rather than just generating text—governance stops being a nice-to-have. It becomes essential.

Anurag illustrated this with a concrete example: imagine a travel company launching an AI assistant that can book flights on behalf of users.

What could go wrong?

Plenty.

• A user might trick the system into generating inappropriate content
• Bots could flood the system with millions of expensive queries
• Someone might manipulate the agent into accessing another user’s data
• Costs could spiral out of control with no usage limits

Each of these represents a different governance challenge: safety, cost control, access management, and identity enforcement

This is why agentic AI requires governance across multiple layers:

• Data governance – ensuring sensitive information is protected
• Access controls – making sure agents only access what they should
• Rate limiting and cost controls
• Auditability and lineage tracking
• Operational monitoring and guardrails

Beyond Pilots: The Real Enterprise AI Challenge

If there was one clear takeaway from the discussion, it’s this:

Enterprise AI success is no longer about models. It’s about lifecycle management.

Building a clever demo is easy. Running intelligent agents safely, securely, and reliably at enterprise scale is the hard part.

As organizations move beyond proofs of concept and into full production deployments, the focus must shift to:

• Governance
• Scalability
• Security
• Observability
• Control

At TrueFoundry, that’s exactly what we’re building for. Turning agentic AI into enterprise reality requires more than models. It requires a platform built for control, governance, and scale.