Trustworthy AI Agents

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Jun 25, 2025

Tanmai Gopal, CEO and Co-Founder at Hasura, sheds light on building reliable AI systems. He emphasizes the importance of trustworthiness in AI agents for enterprise applications, discussing how organizations can overcome challenges in this space. The conversation delves into the differences between agentic and generative AI, particularly in their approach to problem-solving. Gopal also critiques existing data pipelines, advocating for innovative methods to enhance reliability and facilitate better human-AI collaboration.

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INSIGHT

Evolving Trust in AI

Trust in AI evolves from simple task reliability to business-critical decision delegation.
Businesses need AI that can be trusted like rigorous professionals, not just friendly assistants.

INSIGHT

Measuring AI Reliability

Enterprise trust in AI depends on reliability defined by predictability and explainability.
AI must avoid surprising failures and produce consistent results to gain business trust.

INSIGHT

Agentic vs Generative AI Trust

Agentic AI differs from generative AI in goal orientation and predictability needs.
Reliable AI must balance creative problem-solving with predictable methodologies.

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Tanmai Gopal (@tanmaigo, CEO/Founder @HasuraQL) discusses the importance of reliability and trustworthiness for both generative and agentic AI. We discuss the pitfalls in existing data pipelines and how to enhance the results.

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SHOW NOTES:

Topic 1 - Welcome to the show, Tanmai. Give everyone a quick introduction.

Topic 2 - Our topic today is Reliable and Trustworthy AI Agents. First off, what’s the problem we’re solving for here (define reliability and trustworthiness)? Are we solving for hallucinations? Reliability? Connecting private and Enterprise data to models with fine-tuning or RAG?

Topic 3 - How is reliability or trustworthiness measured? I would imagine this isn’t black and white, but maybe a bit more subjective?

Topic 4 - How do Agentic and GenAI differ, if at all, with this model? I would think that since Gen AI lends itself more to the creative side and Agent AI is very deterministic, the approaches to solving the problem might be different. Thoughts?

Topic 5 - Let’s talk about data pipelines. Today, many organizations take an off-the-shelf frontier or foundational model and then apply a RAG pipeline to it for customization. Sometimes fine-tuning is involved, but in my experience, this is the exception rather than the rule. What is wrong with that architecture today? How is this less reliable?

Topic 6 - Let’s talk about Hasura and PromptQL. As I understand it, you are decoupling query planning from execution, thereby creating a more deterministic AI workflow. Now… that’s a mouthful. Can you break down what this means and explain how the architecture differs?

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