Join Raj Shukla, CTO of SymphonyAI, as he discusses the critical distinction between AI demos and production-ready systems, revealing that enterprises consistently underestimate the "last mile" challenges of authentication, authorization, and data scalability that break POCs when moving to production. Shukla explains how SymphonyAI's vertical AI approach pre-trains models on industry-specific ontologies and knowledge graphs for retail, financial services, industrial manufacturing, and enterprise IT, enabling faster ROI by providing pre-built agents and domain-specific context rather than expecting generic LLMs to solve everything. He addresses the hidden costs that shock CFOs—not LLM inference which has dropped 1000x, but the expensive work of making data and APIs AI-ready through proper governance layers and MCP server implementations, while warning that enterprises overestimate the autonomy achievable in the short term and underestimate the infrastructure work required for real process automation at scale.

Join Balaji Raghavan, Head of Engineering at Postman, as he discusses the critical gap between AI adoption and API readiness, revealing that while 80% of developers use AI, only 24% design APIs with AI agents as the intended consumer. Drawing from Postman's 40 million developer user base, Raghavan explains how human-designed APIs create ambiguity problems for AI systems, requiring additional tooling layers that often introduce security vulnerabilities through proxy credentials and unauthorized access risks. He addresses the uncomfortable reality that the industry is still in early stages of making AI reliably call APIs at scale, with hallucinations and context limitations preventing effective orchestration across hundreds of endpoints, while warning that judicious leaders must distinguish between deterministic flows and cases where expensive AI-based approaches are truly necessary to manage infrastructure costs and prevent cascade failures.

Join Stelios Diamantidis, Chief Product Officer at Cognichip, as he explores how artificial intelligence is revolutionizing semiconductor development by enabling more holistic design processes that can reduce development time by half and costs by 75%. He discusses how AI tackles complex challenges across the entire chip design workflow—from early product definition through manufacturing—including verification, debugging, and hardware-software co-design optimization. Diamantidis envisions a near future where AI agents serve as true co-designers, helping engineers navigate the complex trade-offs between performance, power efficiency, and chip area while enabling rapid creation of bespoke accelerators tailored to specific AI workloads.