Most AI agents are built backwards, starting with models instead of system architecture.

Aishwarya Srinivasan, Head of AI Developer Relations at Fireworks AI, joins host Conor Bronsdon to explain the shift required to build reliable agents: stop treating them as model problems and start architecting them as complete software systems. Benchmarks alone won't save you.

Aish breaks down the evolution from prompt engineering to context engineering, revealing how production agents demand careful orchestration of multiple models, memory systems, and tool calls. She shares battle-tested insights on evaluation-driven development, the rise of open source models like DeepSeek v3, and practical strategies for managing autonomy with human-in-the-loop systems. The conversation addresses critical production challenges, ranging from LLM-as-judge techniques to navigating compliance in regulated environments.

Connect with Aishwarya Srinivasan:

LinkedIn: https://www.linkedin.com/in/aishwarya-srinivasan/

Instagram: https://www.instagram.com/the.datascience.gal/

Connect with Conor: https://www.linkedin.com/in/conorbronsdon/

00:00 Intro — Welcome to Chain of Thought

00:22 Guest Intro — Ash Srinivasan of Fireworks AI

02:37 The Challenge of Responsible AI

05:44 The Hidden Risks of Reward Hacking

07:22 From Prompt to Context Engineering

10:14 Data Quality and Human Feedback

14:43 Quantifying Trust and Observability

20:27 Evaluation-Driven Development

30:10 Open Source Models vs. Proprietary Systems

34:56 Gaps in the Open-Source AI Stack

38:45 When to Use Different Models

45:36 Governance and Compliance in AI Systems

50:11 The Future of AI Builders

56:00 Closing Thoughts & Follow Ash Online

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