#44: GPT-5’s Rough Launch, The Agentic Web Revolution & PwC’s 120+ AI Agents

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The Agentic Web Is Coming — and It's Built to Run Without You

A new paper Agentic Web: Weaving the Next Web with AI Agents outlines a future where autonomous, goal-driven AI agents don’t just assist; they interact, coordinate, and execute tasks for each other across the Internet.

Why Agentic Web Matters:

• Agent-to-agent workflows become the default: Instead of humans orchestrating tools, agents will delegate, plan, and solve multi-stage tasks among themselves, effectively turning the web into a computational substrate.

• Three foundational pillars:

  • Intelligence: Retrieval, planning, reasoning

  • Interaction: Protocols for agent discovery, messaging, delegation

  • Economics: Defining effort, value exchange, and cost structures for autonomous workflows

• Built-in realism, not just hype: The paper digs into infrastructure challenges, communication protocols, orchestration models, and even the beginnings of an “Agent Attention Economy” to manage resource allocation. All of this is grounded, not speculative.

Takeaways:

• Start designing for agent ecosystems now: Your UI, APIs, and architecture must anticipate agents talking to agents - not just a user talking to one agent.

• Think of your agent as both consumer and producer: It will need interfaces to call, listen to, and assess other agents in chained workflows.

• Trust needs building into the plumbing: With agents trading work, audit, fallback logic, and governance play a critical role - long before regulatory demands arrive.

The Agentic Web isn’t a distant sci-fi future - it’s blueprinting how agents will weave the next layer of intelligent automation across the Internet. If you’re building, planning, or funding today’s agent tools, start thinking multi-agent, composable, and network-native.

PwC & Google Cloud Launch 120+ AI Agents

PwC and Google Cloud have quietly unveiled a fleet of over 120 AI agents, built across 24 distinct workflows and powered by Vertex AI, Gemini models, Agentspace, and PwC’s own Agent OS. These aren’t prototypes, they’re production-ready agents built for real business outcomes.

• Modular orchestration, not monolithic: Agent OS acts as a “switchboard,” letting businesses combine 5–10 specialized agents into reusable workflows, like retail anomaly detection, clinical documentation, SOX audit support, or customer intent routing.

• High-impact results: Clients report up to 8× speed improvements and 30% cost reductions in core operations. These agents are designed to scale across industries - healthcare, finance, telecom, and more.

• Enterprise-grade deployment: Fully governed, auditable, and cloud-flexible - deployed via Agentspace or on-prem using Agent OS. This makes it manageable and secure at scale.

Why You Should Care:

• This is what agentic infrastructure looks like at scale: Pre-packaged, composable, and deployable with governance baked in.

• Federated agents are now business assets: Modular bots can be swapped, reused, and customized much like microservices in the backend.

• ROI is real, not theoretical: PwC's deployment moves agentic AI beyond experimentation and into value-driven execution.

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📡 TOP Agent Signals

• Vercel launched AI builder v0.app

• Anthropic Expands Government Reach with $1 Claude Deal

• Grok 4 is now free for all users worldwide!

• Google DeepMind Unveils Genie 3 for Interactive 3D Environments

• Wells Fargo Becomes First Major Bank with Enterprise-Wide AI Agents

• Walmart Launches Unified "Super Agent" Framework

• Brightwave added Research Agents to its agentic AI platform

• Rubrik introduced Agent Rewind, a tool that provides audit trails

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• An arXiv paper introduced “Agent Lightning,” a reinforcement-learning framework for training LLM-based agents. It decouples agent execution from training and applies hierarchical RL to agent workflows, enabling RL fine-tuning of agents built with diverse frameworks.

• An arXiv study presenting a runtime governance framework for AI agents. MI9 provides real-time monitoring and control (risk indices, telemetry capture, FSM-based checks, etc.) across agent architectures for safe, scalable deployment

• An OPPO research team paper demonstrated an “Efficient Agents” framework balancing effectiveness with cost. They report retaining ~96.7% of a baseline agent system’s performance while reducing inference cost by ~28%, pointing to more sustainable agent designs.

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