GenAI/AI News Jun 16 25: Anthropic explicates the journey of building agentic systems, HBR warns of agentic issues and more...
There a whole lot of agents going on...
When people talk about agents, they are forgetting about market power. In this vein, I learned so much from my dear friend Benn Konsynski, now at Emory. He had a theory of inter-organizational systems the centered on where the “policy” for a transaction sat. He had a taxonomy of five types of markets in which the buyer controlled the policy, the seller, and then three “levels” of disclosure inside the market itself. I think we will have different agentic contexts that follow a similar pattern. When WalMart goes to Proctor & Gamble and wants a particular agentic framework, P&G will comply, etc. This market-power based policy enforcement will be key for the future of agentic systems, IMHO. On to today’s news!
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Today there were 3 Essential and 3 Optional articles.
Organizations Aren’t Ready for the Risks of Agentic AI
Rating: Essential
Rationale: This HBR essay by Reid Blackman maps out how as organizations scale from narrow to multi-agent AI, risk escalates exponentially, offering a maturity curve and four capability gaps—stress testing, observability, kill-switches, and skilling—needed for responsible AI governance. Our analysts stressed that its clear risk framework and pragmatic guidance make it an essential read to avoid being unprepared as agentic AI becomes mainstream.
Future of Work with AI Agents
Rating: Essential
Rationale: This Stanford SALT Lab study uncovers how 1,500 workers across 104 occupations view AI agents, revealing critical mismatches between their preferences and current capabilities in automation and augmentation. Our analysts emphasized that its data-driven human-agent framework (with zones and H1–H5 scale) offers valuable insights for AI leaders to align deployment strategies with real workforce preferences, making it essential for guiding long-term AI-driven workplace transformation.
How we built our multi-agent research system
Rating: Essential
Rationale: Anthropic details its multi-agent “orchestrator-worker” architecture that coordinates parallel subagents for dynamic, high-quality research—marking a major step forward in AI automation and content generation. Our analysts highlighted its engineering rigor, lessons on token usage and traceability, and broad architectural applicability, making it crucial for anyone building reliable and scalable AI agent systems.
Next‑Gen Pentesting: AI Empowers the Good Guys
Rating: Optional
Rationale: a16z spotlights AI-driven penetration testing tools like Unpatched AI that continuously scan and exploit vulnerabilities, reflecting a shift from manual to autonomous security approaches. Our analysts liked the concept but viewed it as a niche security update rather than must-have news for most AI leaders, making it optional for deeper exploration.
Accelerating Articul8’s domain-specific model development with Amazon SageMaker HyperPod
Rating: Optional
Rationale: This AWS case study shows how fine-tuning domain-specific models (“DSM”) via SageMaker HyperPod boosts performance over general LLMs. While acknowledging performance gains, our analysts noted it reads like product marketing with limited strategic insights—worth reviewing, but not urgent.
ComfyUI‑R1: Exploring Reasoning Models for Workflow Generation
Rating: Optional
Rationale: This research introduces an LLM‑driven UI layer for automating workflow design, showing promise in stitching together processes via generative AI. However, it remains early-stage with underwhelming benchmarks. Analysts viewed it as a valuable trend to monitor but not yet impactful enough for widespread adoption.
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Onward,
John Sviokla, Paul Baier and our AI Analysts, Luda Kopeikina, Amanda Fetch, Adam Rappaport. and our Guest Reviewer today, Ben Faircloth from Seekr. Thank you for joining, Ben!