Agentic AI

Okta beat fiscal Q1 expectations, with revenue up 11% driven by strong demand for identity security amid the rise of agentic AI. The company reported adjusted EPS of 91 cents on $765 million revenue. CEO Todd McKinnon highlighted AI’s role as a growth catalyst, emphasizing Okta’s long-term strategy to provide foundational security infrastructure for future AI deployments. Okta is enhancing offerings like “Okta for AI agents” to address security needs arising from AI expansion.

Mistral AI is exploring custom chip design and manufacturing for cost reduction and infrastructure control, aiming to compete with U.S. AI giants. The company is investing heavily in data center expansion in France and Sweden to boost compute capacity and launching an enterprise agent platform called “Vibe” for complex task execution. Mistral AI aims for significant revenue growth by 2026.

Embodied AI, moving beyond digital to physical realms like warehouses and public spaces, introduces new safety risks. Singapore’s IMDA released a Model AI Governance Framework for Agentic AI, stressing stringent controls, monitoring, and human oversight for autonomous systems interacting with the physical world. This shift necessitates regulatory paradigms akin to aviation and critical infrastructure, focusing on reliability, operational safety, and iterative testing. Accountability remains with human operators, guiding complex AI ecosystems from development to deployment.

OpenAI establishes its first Applied AI Lab outside the US in Singapore, investing S$300 million and creating over 200 technical roles. Singapore also unveils an updated agentic AI governance framework, building on previous iterations and incorporating feedback from over 60 organizations. This framework offers clearer guidance on the responsible deployment of AI agents, addressing risks of multi-agent systems and human accountability, with detailed case studies illustrating practical implementation.

Google unveiled Gemini 3.5 Flash, a faster, more affordable AI model, at its I/O conference. They also introduced Gemini Spark, an AI agent for task automation, and Omni, a world model for simulating physical environments and advanced video editing. These moves emphasize Google’s commitment to AI innovation and integrating AI into user-facing services.

Joe Rose of JBS Dev debunks the myth that perfect data is required for generative AI. He highlights that modern tools can interpret imperfect data, enabling faster AI adoption. While human oversight is crucial for managing AI unpredictability, a progressive approach to layering use cases can gradually increase efficiency. Rose also anticipates a shift towards cost-efficiency and portability in AI models, advocating for in-house development over SaaS vendors when feasible.

Bain & Company projects a $100 billion US market opportunity for SaaS companies using agentic AI, primarily for automating complex enterprise coordination tasks. This AI transforms manual, labor-intensive processes across various systems, unlocking new market segments. Sales, COGS, and operations represent significant segments, with customer support and R&D showing the highest automation potential. SaaS firms should identify automatable workflows, assess data quality, and invest in AI talent and infrastructure. The window for capitalizing on this opportunity is rapidly closing.

AMD’s CEO Lisa Su forecasts significant CPU demand growth, exceeding 35% annually, driven by agentic AI. This surge, particularly in data centers, contrasts with previous projections and suggests a massive market expansion, potentially exceeding $120 billion by 2030. AMD’s CPU strength is poised to capitalize on this trend, impacting the AI computing landscape.

Google’s Gemini Enterprise Agent Platform, launched at Cloud Next ’26, integrates agentic AI governance as a core feature, addressing a significant industry gap. Unlike previous add-ons, this platform assigns cryptographic identities to agents and centralizes oversight through an Agent Gateway. This move tackles the widespread challenge of AI sprawl and lack of control, where many organizations struggle to manage AI deployments effectively. Google is shifting focus from model capabilities to control plane ownership, offering a robust solution for enterprises to build, scale, and govern AI agents securely.

OpenAI has launched GPT-5.5, its most capable agentic AI, designed for professional tasks and autonomous agents. It excels in planning, tool use, and self-correction, showing significant improvements on benchmarks like Terminal-Bench 2.0 and SWE-Bench Pro. While boasting enhanced long-context reasoning, it did not score on MCP Atlas. Pricing is higher but justified by increased token efficiency, with a premium tier for advanced users. Real-world use cases demonstrate tangible business value and operational efficiencies.